Sung-Soo Kim's Blog

- 26 January 2023 » Activate Your Modern MetaData Stack
- 24 January 2023 » Databricks Lakehouse makes payments ingestion and analytics simple
- 18 January 2023 » The Essential Guide to Data Lineage
- 17 January 2023 » Data Governance Explained in 5 Minutes
- 16 January 2023 » Automated Data Lineage with Unity Catalog
- 9 January 2023 » Active Metadata - Understanding the magic behind the Data Fabric
- 8 January 2023 » Introduction to Data Mesh
- 7 January 2023 » PostgreSQL vs MySQL
- 6 January 2023 » Activate Your Metadata to Empower Innovation
- 6 January 2023 » Accelerating Hybrid Data Mesh Implementation
- 5 January 2023 » Deep-Dive into Delta Lake
- 4 January 2023 » Delta Lake 2.0 Overview
- 3 January 2023 » Advancing Spark - The Photon Whitepaper
- 2 January 2023 » Column-Level Lineage and Active Metadata

## Sung-Soo Kim, Ph.D.
Artificial Intelligence Research Laboratory |

September 2000 ~ Present

*Principal Researcher*

Research on Machine learning, Big Data, Approximate query processing, SQL on Hadoop, HTAP, In-memory data management, multiscreen services, photo-realistic rendering (global illumination), real-time rendering, geometry compression algorithms, spatio-temporal data modeling/indexing and route determination algorithms.

~ August 2000

*Researcher*

Research on reuse based software engineering for large information systems.

Machine Learning, Big Data, data mining, In-memory data management, distributed query processing on evolving hardware, cost-based query optimization, pervasive parallelism.

Photo-realistic rendering, mesh simplification, multiresolution modeling, terrain simplification and rendering, geometric compression, non-photorealistic rendering and HCI.

3D GIS, video GIS, geographic hypermedia, scientific visualization for noise and vibration data, 3D graphics library and rendering engine for 3D GIS, spatio-temporal data modeling / spatial indexing

Route determination algorithm for OpenLS specification

Agile software developments, Test-driven developments, Object-oriented software design and implementation, component-based software development

[1] 강영민, 박동규, **김성수**, 으뜸 머신러닝, 생능출판사, 2021년.

[8] **Sung-Soo Kim**, Young-Kuk Kim and Young-Min Kang, "AORM: Fast Incremental Arbitrary-Order Reachability Matrix Computation for Massive Graphs," in *IEEE Access*, vol. 9, pp. 69539-69558, 2021, doi: 10.1109/ACCESS.2021.3077888. [SCIE]

[7] Semin Kang, **Sung-Soo Kim**, Jongho Won, Young-Min Kang, GPU-based parallel genetic approach to large-scale travelling salesman problem, * The Journal of Supercomputing*,
November 2016, Volume 72, Issue 11, pp 4399–4414, 2016. [SCI]

[6] Y.M. Kang, H.G. Cho, **Sung-Soo Kim**, Plausible and Realtime Rendering of Scratched Metal by Deforming MDF of Normal Mapped Anisotropic Surface, *Journal of WSCG*, Vol 19, Number 1-3, pp.101-109, 2011.

[5] **Sung-Soo Kim**, S. W. Nam, I. H. Lee, Fast Ray-Triangle Intersection Computation Using Reconfigurable Hardware, *Lecture Notes in Computer Science*(MIRAGE 2007). [SCIE]

[4] **Sung-Soo Kim**, S. W. Nam, D. H. Kim and I. H. Lee, Hardware-Accelerated Ray-Triangle Intersection Testing for High-Performance Collision Detection, *Journal of WSCG*, Vol 15, Number 3, ISSN 1213-6972, ISBN 978-80-86943-00-8.

[3] M. G. Cho, Y. J. Yu, **Sung-Soo Kim**, Traffic Prediction System using Real-time Traffic Information, *Lecture Notes in Computer Science* (LNCS: ICCSA 2006) [SCIE]

[2] Y. B. Kang, **Sung-Soo Kim**, A New Route Determination Approach using Future Traffic Prediction, *Journal of WSEAS Transactions on Systems*,Vol. 4(6), pp. 804-811, 2005. ISSN 1109-2777.

[1] **Sung-Soo Kim**, S. K. Choe, Stylized Silhouette Rendering using Progressive Meshes, *Journal of WSCG*, Vol 10, Number 3, pp. 51-58, Feb. 2002.

[9] **김성수**, 박춘서, 남택용, 이태휘, 샘플링 데이터를 이용한 혼합 밀도 네트워크 모델기반 근사 질의 처리, *정보과학회 컴퓨팅의 실제 논문지*, pp. 450-457, Vol. 28, No. 9, 2022. 9.

[8] **김성수**, 원종호, 해양플랜트의 예지보전을 위한 실시간 데이터 스트림 처리 구현, *정보과학회논문지: 시스템 및 이론*, 제42권 제7호, 2015.

[7] **김성수**, 손지연, 박준희, 디지털 오일필드에서 빅데이터 분석기반 IT융합 기술 동향, *전자통신동향분석*, 28권 4호 (통권 142), 2013.

[6] **김성수**, 조충래, 클라우드기반 멀티스크린 서비스 및 기술 동향, *한국스마트미디어학회 학회지*, 2012.

[5] 임충규, **김성수**, 김경일, 원종호, 박창준, 클라우드 컴퓨팅 기반의 게임 스트리밍 기술 동향, *전자통신동향분석*, 26권 4호, 2011.

[4] J. H. Lee, **Sung-Soo Kim**, H. J. Park, Comparing BRDF Models: Representation of Measured BRDF, *Transactions on SCCE*, Vol. 14, No. 5, 2009.

[3] 남승우, 김해동, **김성수**, 최진성, 렌더링 가속화 기술 동향, *전자통신동향분석*, 22권 4호, 2007.

[2] **Sung-Soo Kim**, K. H. Kim, K. O. Kim, Web-Based Media GIS Architecture using the Virtual World Mapping Technique, *Korean Journal of Remote Sensing*, Vol. 19, No. 1, pp. 71-80, 2003.

[1] **Sung-Soo Kim**, S. H. Lee, K. O. Kim, J. H. Lee, *Video GIS using Virtual World Mapping Technique, Journal of the Korean Geo-spatial Information Society*, Sep. 2002. (in Korean)

[46] Taewhi Lee, Kihyuk Nam, Choon Seo Park, **Sung-Soo Kim**, Exploiting Machine Learning Models for Approximate Query Processing, *International Conference on Big Data (Big Data) 2022*, pp.6734-6736, 2022. 12.

[45] Kihyuk Nam, Taewhi Lee, **Sung-Soo Kim**, Choon Seo Park, Taek Yong Nam, Insik Shin, An Efficient Data Analysis For Edge-Enabled Distributed Environments using Tractable Probabilistic Models, *International Conference on Big Data (Big Data) 2022*, pp.6769-6771, 2022. 12.

[44] Taewhi Lee, Kihyuk Nam, Choon Seo Park, **Sung-Soo Kim**, Query Transformation for Approximate Query Processing Using Synthetic Data from Deep Generative Models, *International Conference on Consumer Electronics-Asia (ICCE-Asia) 2022 *, pp.314-317, 2022. 10.

[43] Kihyuk Nam, **Sung-Soo Kim**, Choon Seo Park, Taek Yong Nam, Taewhi Lee, Designing ML-based Approximate Query Processing Services on Time-Varying Large Dataset for Distributed Systems, *The 13th International Conference on ICT Convergence (ICTC) 2022*, 2022. 10.

[42] **Sung-Soo Kim**, Young-Min Kang, Young-Kuk Kim, Sparsity-Aware Reachability Computation for Massive Graphs, *IEEE The International Conference on Big Data and Smart Computing (BIGCOMP) 2022*, 2022. 01.

[41] **Sung-Soo Kim**, Moonyoung Chung, Young-Kuk Kim, Urban Traffic Prediction using Congestion Diffusion Model, *IEEE The International Conference on Consumer Electronics (ICCE)-Asia 2020*, 2020. 11.

[40] **Sung-Soo Kim**, Okgee Min, Young-Kuk Kim, Improved Spatial Modeling using Path Distance Metric for Urban Traffic Prediction, *The International Conference on Big Data Applications and Services (BIGDAS) 2019*, 2019. 08.

[39] **Sung-Soo Kim**, Okgee Min, Young-Kuk Kim, SALT-Viz: Real-Time Visualization for Large-Scale Traffic Simulation, *2019 IEEE Transportation Electrification Conference and EXPO Asia-Pacific*, 2019. 05. [**Best Presentation Award**]

[38] Young-Min Kang, **Sung-Soo Kim**, Gyung-Tae Nam, A Parallel Approach to Object Identification in Large-scale Images, *International Conference on Electronics and Software Science 2016*, 2016. 11.

[37] Taewhi Lee, Moonyoung Chung, **Sung-Soo Kim**, Hyewon Song, Jongho Won, Partial Materialization for Data Integration in SQL-on-Hadoop Engines, *International Conference on IT Convergence and Security (ICITCS 2016)*, 2016. 9.

[36] **Sung-Soo Kim**, Taewhi Lee, Moonyoung Chung, Jongho Won, Sweet KIWI: Statistics-Driven OLAP Acceleration using Query Column Sets, *EDBT 2016, 19th International Conference on Extending Database Technology, Bordeaux, France, March 15-16*, 2016.

[35] **Sung-Soo Kim**, Taewhi Lee, Moonyoung Chung, Jongho Won, Flying KIWI: Design of Approximate Query Processing Engine for Interactive Data Analytics at Scale, *BigDAS 2015, International Conference on Big Data Applications and Services*, 2015.

[34] Semin Kang, **Sung-Soo Kim**, Jongho Won, Young-Min Kang, Bidirectional Constructive Crossover for Evolutionary Approach to Travelling Salesman Problem, *5th International Conference on IT Convergence and Security (ICITCS) 2015*.

[33] **Sung-Soo Kim**, C.L. Cho, J.H. Won, A Collaboration Middleware for Service Scalability in Peer-to-Peer Systems, *HPCS 2015, IEEE International Conference on High Performance Computing and Simulation*, 2015.

[32] **Sung-Soo Kim**, C.L. Cho, Multiscreen-based Gaming Services using Multi-view Rendering with Different Resolutions, *MOBILITY 2012, The Second International Conference on Mobile Services, Resources, and Users*, 2012.

[31] C.L. Cho, **Sung-Soo Kim**, K. D. Moon, Enhancing home entertainment experience with virtual UPnP media renderers, *2012 International Conference on ICT Convergence (ICTC)*, 2012.

[30] **Sung-Soo Kim**, K. I. Kim, J.H. Won, Multi-view Rendering Approach for Cloud-based Gaming Services, *The Third International Conference on Advances in Future Internet 2011*, Aug. 2011. [**Best Paper Award**]

[29] **Sung-Soo Kim**, Y. M. Kang, J. H. Won, Realistic Woven Fabric Rendering using Deformed Microfacet Distribution Function, *ICCIT 2010*.

[28] **Sung-Soo Kim**, Y.M. Kang, S.W. Nam, Procedural Approach for Realistic Woven Fabric Rendering, *EUROGRAPHICS 2010*. (Poster Paper)

[27] **Sung-Soo Kim**, K. H. Kim, S. K. Jang, J. M. Lim, K. Y. Wohn, Geo-spatial Hypermedia based on Augmented Reality, *WSCG 2010 (18th International Conference on Computer Graphics, Visualization and Computer Vision 2010)*, Feb. 2010.

[26] **Sung-Soo Kim**, J. H. Lee, S. W. Nam, Realistic Rendering System using the Measured BRDFs, *ICIS 2009 (International Conference on Interaction Sciences: Information Technology, Culture and Human)*, *ACM International Conference Proceeding Series*; Vol. 403, 2009.

[25] **Sung-Soo Kim**, Geo-Spatial Hypermedia based on Augmented Reality, *The Korea-Japan joint workshop on Frontiers of Computer Vision (FCV) 2007*.

[24] **Sung-Soo Kim**, Y. B. Kang, Congestion Avoidance Algorithm using Extended Kalman Filter, *International Conference on Convergence Information Technology*, IEEE Computer Society, Nov., 2007.

[23] Y. B. Kang, **Sung-Soo Kim**, A Fastest Route Planning for LBS based on Traffic Prediction, *9th WSEAS International Conference on Systems, Greece*, 2005. ISBN 960-8457-29-7. [**Best Paper Award**]

[22] **Sung-Soo Kim**, J. H. Park, Geographic Hypermedia using Search Space Transformation, *IEEE International Conference on Pattern Recognition (ICPR)* 2004, Aug. 2004.

[21] **Sung-Soo Kim**, J. H. Park, Linking Geographic Hypermedia using the Remotely Sensed Data, *IEEE International Conference on Multimedia and Expo (ICME)* 2004, June. 2004.

[20] **Sung-Soo Kim**, J. H. Park, Efficient Routing Service for the Open LBS Services, *GeoInformatics 2004*, June. 2004.

[19] **Sung-Soo Kim**, K. S. Kim, J. C. Kim, J. H. Lee, Efficient Route Determination Technique in LBS System, *ISRS(International Symposium on Remote Sensing) 2003*, Nov. 2003.

[18] **Sung-Soo Kim**, S. H. Lee, K. H. Kim, J. H. Lee, A Unified Visualization Framework for Spatial and Temporal Analysis in 4D GIS, *IEEE International Geoscience and Remote Sensing Symposium 2003*, Toulouse, France, July, 2003.

[17] K. H. Kim, **Sung-Soo Kim**, S. H. Lee, J. H. Park, J. H. Lee, The Interactive Geographic Video, *IEEE International Geoscience and Remote Sensing Symposium 2003*, Toulouse, France, July, 2003.

[16] J. C. Kim, **Sung-Soo Kim**, T. W. Heo, J. H. Park, OpenLS Directory Service Architectures and Implementation based on Web-Service, *International Symposium on Remote Sensing (ISRS) 2003*, Nov. 2003.

[15] K. H. Kim, **Sung-Soo Kim**, Sung-Ho Lee, K. O. Kim, J. H. Lee, GeoVideo: The Video Geographic Information System as a First Step Toward Media GIS, *ASPRS'2003 Conference*, 2003.

[14] **Sung-Soo Kim**, S. H. Lee, K. H. Kim, J. H. Lee, Media GIS Web Service Architecture using Three-Dimensional GIS Database, *ISRS 2002*, Oct. 2002.

[13] K. H. Kim, **Sung-Soo Kim**, Sung-Ho Lee, K. O. Kim, J. H. Lee, GeoVideo: A First Step to MediaGIS, *ISRS 2002*, Oct. 2002.

[12] S. H. Lee, K. H. Kim, **Sung-Soo Kim**, K. O. Kim, Representing Topological Relationships for 3-Dimensional Spatial Features, *ISRS 2002*, Oct. 2002.

[11] **Sung-Soo Kim**, S. H. Lee, J. H. Park, Y. K. Yang, Rule-based Modeling for 3D GIS, In *Proceedings of WSCG'2002*, pp. 21-24, Feb. 2002.

[10] **Sung-Soo Kim**, J. H. Park, Space-Efficient Terrain Rendering using Constrained Delaunay Triangulation, *IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2002*, Toronto, Canada, June 2002.

[9] **Sung-Soo Kim**, S. H. Lee, S. K. Choe, J. H. Lee, Component-Based 3D GIS Software Design for the Urban Planning, *MIS'2002 incorporating GIS and Remote Sensing*, pp. 205-214, April 2002.

[8] **Sung-Soo Kim**, S. K. Choe, J. H. Lee, Y. K. Yang, Rule-Based Modeler Component Design for 3D GIS Software, In *Proceedings of ISRS 2001*, pp. 89-94, Oct. 2001.

[7] **Sung-Soo Kim**, S. K. Choe, J. H. Lee, Y. K. Yang, Level-of-Detail-based Rendering and Compression for 3D GIS, *IEEE International Geoscience and Remote Sensing Symposium 2001*, July 2001.

[6] **Sung-Soo Kim**, K. H. Kim, J. H. Lee, Comparison of Slot Reuse Algorithms for CRMA High Speed Networks, In *Proceedings of World Multiconference on Systemics, Cybernetics and Informatics (ISAS/SCI 2001)*, Volume V, pp. 229-233, 2001.

[5] **Sung-Soo Kim**, J. H. Cho, S. K. Choe, Y. K. Yang, Feature-Based Graph Matching Algorithm for Image Mosaicing, In *Proceedings of World Multiconference on Systemics, Cybernetics and Informatics (ISAS/SCI 2001)*, Volume VI, pp. 315-319, 2001.

[4] S. K. Choe, K. H. Kim, S. H. Lee, **Sung-Soo Kim**, Y. K. Yang, Parametric Design and Visualization in 3-dimensional GIS Software Development, In *Proceedings of ISRS 2001*, pp. 128-131, Oct. 2001.

[3] S. H. Lee, Kyung-Ho Kim, S. K. Choe, **Sung-Soo Kim**, Y. K. Yang, Extension of OpenGIS OLE/COM SFS for 3-Dimensional GIS, In *Proceedings of ISRS2001*, pp. 248-251, Oct. 2001.

[2] **Sung-Soo Kim**, Y. S. Kim, M. G. Cho, H. G. Cho, A Geometric Compression Algorithm for Massive Terrain Data Using Delaunay Triangulation, In *Proceedings of WSCG'99*, pp. 124-131, Feb. 1999.

[1] J. K. Park, Y. M. Kang, **Sung-Soo Kim**, H. G. Cho, Expressive Character Animation with Energy Constraints, In *Proceedings of COMPUGRAPHICS'97*, Dec. 1997.

[50] 최민규, 한승훈, 김정선, 김성수, 임성수, 도시 네트워크의 교통 데이터 개선을 통한 그래프 딥러닝 모델 성능 분석, 2022년 한국소프트웨어종합학술대회 논문집, 2022.

[49] 김성수, 박춘서, 남택용, 이태휘, 시놉시스 데이터를 이용한 ML 기반 근사 질의 처리 모델, pp.52-54, 2021년 한국소프트웨어종합학술대회 논문집, 2021. [우수발표논문상 수상]

[48] 박춘서, 김성수, 남택용, 이태휘, 머신 러닝 모델 기반 근사 질의 처리 방법에 관한 연구, pp.532-534, 한국정보처리학회 학술 발표 대회 (추계) 2021.

[47] 김성수, 민옥기, 김영국, 메소스코픽 트래픽 시뮬레이션을 위한 실시간 가시화 시스템 구현, pp.779-782, 대한전자공학회 학술대회(추계) 2018,

[46] 김성수, 민옥기, 김영국, 멀티모달 데이터 기반 교통 흐름 예측 시스템 설계, 2018년 한국지능시스템학회 춘계학술대회, 2018. 4.

[45] 김성수, 민옥기, GPU 기반 메소스코픽 트래픽 시뮬레이터 설계, 2017년 한국소프트웨어종합학술대회 논문집, 2017. 12.

[44] 김성수, 정문영, 이태휘, 송혜원, 원종호, SQL-on-Hadoop을 위한 벡터처리기반 질의실행 엔진 설계, 제43회 한국정보과학회 동계학술발표회, 2016 학술대회 학술발표집, 2016. 12.

[43] 이종욱, 임현승, 김성수, 2차원 공간에서 효율적인 선형 스카이라인 알고리즘, 제43회 한국정보과학회 동계학술발표회, 2016 학술대회 학술발표집, 2016. 12.

[42] 정문영, 이태휘, 김성수, 송혜원, 원종호, 대용량 데이터 처리를 위한 질의 컬럼셋과 수평 파티션의 통합 방법, 한국정보처리학회 추계학술발표대회, 2016. 11.

[41] 김성수, 정문영, 이태휘, 원종호, 의료 데이터기반 빅데이터 분석 서비스 구현, 제42회 한국정보과학회 동계학술발표회, 2015 학술대회 학술발표집, 2015.

[40] 정문영, 이태휘, 김성수, 원종호. 맵리듀스 함수 지원을 위한 SQL 질의의 확장 방법, 한국정보처리학회 추계학술발표대회, 2015.

[39] 김성수, 원종호, 예지 분석을 위한 실시간 빅데이터 스트림 처리, 제 41회 한국정보과학회 동계학술발표회, 2014 학술대회 학술발표집, 2014. **[우수논문상 수상]**

[38] 김성수, 한효녕, 박준희, 해양플랜트 설비 성능평가를 위한 스트림 데이터 처리, 대한기계학회 IT융합 학회, 2014 학술대회 학술발표집, 2014.

[37] 김성수, 조충래, 손지연, P2P 환경에서 스마트 앱간 협업을 위한 논리적 통신기반 미들웨어, 한국 CAD/CAM 학회 2014 학술대회 논문집, 2014.

[36] 김성수, 조충래, 손지연, 멀티스크린 서비스를 위한 서로 다른 해상도를 제공하는 다시점 렌더링, 한국컴퓨터그래픽스학회 2013 학술대회 학술발표집, 2013.

[35] 김성수, 조충래, 손지연, 김진태, 박광로, 스마트 디바이스 협업 미들웨어 설계 및 구현, 한국 CAD/CAM 학회 논문집, 2013.

[34] 김성수, 조충래, 박윤경, 김진태, 문경덕, 가상 디바이스 앙상블 서비스를 위한 미들웨어 설계, 한국 CAD/CAM 학회 논문집, 2012.

[33] 김성수, 임충규, 김경일, 원종호, 박창준, 스트리밍 게임 서비스를 위한 렌더링 시스템 설계, 한국 CAD/CAM 학회 논문집, 2011.

[32] 김성수, 이주행, 서대종, 남승우, 전역조명 기반 렌더링 소프트웨어 개발, 한국 CAD/CAM 학회 논문집, 2010.

[31] 이주행, 서대종, 김성수, 남승우, 다층 재질의 모델링 및 렌더링 기법에 대한 연구, 한국 CAD/CAM 학회 논문집, 2010.

[30] 서대종, 이주행, 김성수, 남승우, 지연 셰이딩을 이용한 동적 시점 환경에서의 실시간 재조명, 한국 CAD/CAM 학회 논문집, 2010.

[29] 김성수, 이주행, 서대종, 남승우, 측정기반 BRDF를 활용한 사실적 렌더링 시스템, 한국 컴퓨터그래픽스 추계학술대회 논문집, 2009.

[28] 서대종, 이주행, 김성수, 남승우, Go!Rilla 실시간 재조명 시스템의 기능 확장, 한국 컴퓨터그래픽스 추계학술대회 논문집, 2009.

[27] 강영민, 강정훈, 김성수, 의류 객체의 사실적 렌더링 기법, 한국 컴퓨터그래픽스 추계학술대회 논문집, 2009.

[26] 강영민, 김성수, 남승우, 직조 기반 비등방 반사를 이용한 의류 렌더링, 한국게임학회 추계학술대회 논문집, 2009.

[25] 이주행, 김성수, 박형준, 측정 BRDF 표현 기법 비교, 한국 CAD/CAM 학회 학술대회, 2009.

[24] 남승우, 김성수, 진성일, 최진성, 대용량 데이터에 대한 광선-삼각형 충돌 처리를 위한 하드웨어 구조, 영상처리 및 이해에 관한 워크샵, 2008.

[23] 남승우, 김해동, 김성수, 렌더링 가속화 기술 동향, 전자통신동향분석, 2007.

[22] Sung-Soo Kim, J. C. Kim, K. S. Kim, J. H. Park, J. H. Lee, Route Determination Service for Open LBS, Korean Information Processing Society Fall Conference, Nov. 2003.

[21] J. C. Kim, T.W. Heo, Sung-Soo Kim, K. S. Kim, J. H. Park, J. H. Lee, Directory Web Service based on EJB, Korean Information Processing Society Fall Conference, Nov. 2003.

[20] T. W. Heo, J. C. Kim, Sung-Soo Kim, K. S. Kim, J. H. Park, J. H. Lee, The Implementation of OpenLS Presentation Web Service, Korean Information Processing Society Fall Conference, Nov. 2003.

[19] Sung-Soo Kim, K.H. Kim, S. H. Lee, J. H. Park, Efficient Implementation of 4D GIS for Spatio-Temporal Analysis, Korean Information Science Society Spring Conference, April, 2003.

[18] S. H. Lee, Sung-Soo Kim, K.H. Kim, J. H. Park, Data Provider Service in 4D GIS, Korean Information Science Society Spring Conference, April, 2003.

[17] Sung-Soo Kim, S. H. Lee, K.H. Kim, K. O. Kim, Media GIS using 3D Graphic Mapping Technique, Korean Information Science Society Fall Conference, Oct. 2002.

[16] Sung-Soo Kim, K.H. Kim, J. H. Lee, Video Geographic Information Service using 4S-Van Camera Data, Korean Information Processing Society Fall Conference, Nov. 2002.

[15] S. H. Lee, K. H. Kim, Sung-Soo Kim, K. O. Kim, Topological Relationships of Three-Dimensional Spatial Features, Korean Information Processing Society Fall Conference, Nov. 2002.

[14] Sung-Soo Kim, S. H. Lee, J. C. Jeon, J. H. Park, Progressive Silhouette Rendering with Mesh Simplification for Silhouette Preservation, HCI Korea 2002 Conference, Feb. 2002.

[13] K. H. Kim, Sung-Soo Kim, S. H. Lee, J. H. Lee, GeoVideo: Video Geographic Information Systems, Korean Information Science Society Spring Conference, April. 2002.

[12] Sung-Soo Kim, K. H. Kim, J. H. Lee, 3D Terrain Rendering using Contour Line Data, Korean Information Science Society Spring Conference, pp. 625-627, April 2001.

[11] Sung-Soo Kim, Progressive Silhouette Rendering using Level-of-Detail Meshes, Korean Information Science Society Fall Conference, pp. 505-507, Oct. 2001.

[10] Sung-Soo Kim, S. H. Lee, Y. K. Yang, Slot Reuse Algorithm for CRMA High Speed Networks, Korean Information Science Society Fall Conference, pp. 160-162, Oct. 2001. (in English)

[9] Sung-Soo Kim, S. H. Lee, J. H. Lee, Y. K. Yang, Terrain Reconstruction from Contour Lines, Korean Information Processing Society Fall Conference, pp. 641-644, Oct. 2001.

[8] Sung-Soo Kim, S. K. Choe, J. H. Lee, Y. K. Yang, The Design of Scene Modeler Component for 3D GIS Software, Korean Information Processing Society Fall Conference, pp. 81-84, Oct. 2001.

[7] Sung-Soo Kim, K. S. Kim, S. H. Lee, S. K. Choe, K.H. Kim, J. H. Lee, Y. K. Yang, The Three-Dimensional Extension for Mapbase Components using the Rule-Based Modeling, Korean Multimedia Society Fall Conference, pp. 171-176, Nov. 2001.

[6] S. H. Lee, Sung-Soo Kim, Y. K. Yang, OpenGIS OLE/COM SFS Extension for 3D GIS , Korean Information Science Society Fall Conference, Oct. 2001.

[5] J. C. Jeon, Sung-Soo Kim, Y. J. Yim, Y. K. Yang, Real-time motion computation using CCD camera images, Inductive Weapon Conference, Agency for Defense Development, 2001.

[4] J. H. Cho, Sung-Soo Kim, T. C. Yang, Effective Silhouette Edge Rendering using Parameterized Brush Functions, Korean Information Science Society Fall Conference, Oct. 2000.

[3] J. H. Cho, Sung-Soo Kim, Y. T. Kim, Mesh Simplification for Volume and Boundary Preservation, Korean Information Science Society Fall Conference, pp. 583-585, 1999.

[2] Sung-Soo Kim, Y. J. Yu, J. M. Park, H. G. Cho, Terrain Simplification using New Cost Function, Korean Information Science Society Fall Conference, pp. 644-646, 1998.

[1] J. K. Park, Y. M. Kang, Sung-Soo Kim, H. G. Cho, Character Animation Technique using Energy Constraints, Korean Information Science Society Fall Conference, pp.587-590, 1997.

- agile
^{14} - algorithms
^{31} - analytics
^{36} - big data
^{287} - computer graphics
^{70} - computer networks
^{16} - data curation
^{13} - data management
^{286} - data science
^{39} - design patterns
^{14} - developments
^{78} - gpgpu
^{23} - graph mining
^{430} - hadoop & mapreduce
^{168} - htap
^{39} - machine learning
^{837} - nosql
^{24} - papers
^{36} - parallel computing
^{92} - presentation
^{82} - publications
^{12} - software engineering
^{19} - spark
^{59} - sql on hadoop
^{17} - stream computing
^{48} - tez
^{30} - yarn
^{52}

- » Unit Test
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- » Wiki Tutorial
- » Adapting to Change
- » Agile Big Data
- » TDD Best practices
- » Test-Driven Development in a nutshell
- » Scrum Events
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- » JUnit, A Tool for Test-Driven Development
- » Explaining Agile
- » Extreme Programming
- » Scrum Agile Development Process
- » Agile Processes and Scrum

- » Notes on Graph Theory
- » The Robot Revolution - The New Age of Manufacturing
- » Evolutionary Generative Adversarial Networks
- » Time Series Generation with Recurrent Conditional GANs
- » Predicting Car Prices using Neural Networks
- » Predicting Car Prices - Linear Regression
- » Deep Reinforcement Learning
- » Brains, Minds and Machines Summer Course
- » OpenAI Meta Learning and Self Play
- » Sequential Implementation of Mesoscopic Simulation
- » Predicting congestion on London's roads with TensorFlow
- » Improving Traffic Prediction Using Weather Data
- » Limitations of Macroscopic and Mesoscopic Simulation
- » Simulation Loop
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- » Distributed Systems
- » High-Performance Mesoscopic Traffic Simulation
- » Microscopic Simulation
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- » T tree
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- » Computability, Complexity, and Theory - Georgia Tech
- » PageRank Algorithm
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- » Bloom Filter
- » Confluent and Functional Persistence
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- » Graphs in Machine Learning
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- » Structural Analysis and Visualization of Networks
- » Perform SQL Selects on R Data Frames
- » R Graphics
- » Google's R Style Guide
- » IBM Watson at Work
- » Hype Cycle
- » Predictive Methodologies - Analysis of Time and Space
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- » Theoretical Foundations of Data Mining and Predictive Analytics
- » Data Used in Predictive Policing
- » Operational Challenges of Predictive Policing
- » Forecast Vs. Prediction
- » Recommendations
- » Predictive Policing Myths and Pitfalls
- » Prediction-Led Policing Process and Prevention Methods
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » “Big Data” Promises a Revolution
- » T-Shaped Data Scientists
- » Adapting to Change
- » Agile Big Data
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- » The Variety of Data Scientists
- » The Real-Time Big Data Architecture (RTDBA) Stack
- » The Five Phases of Real Time
- » Case Studies of Analyzing Data
- » Data Science, Engineering, and Data-Driven Decision Making
- » Clustering Data Scientists
- » Solving the Wanamaker Problem for Healthcare
- » Parallel R
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- » Analytics vs. Predictive Analytics
- » The evolution of analytics

- » Activate Your Modern MetaData Stack
- » Databricks Lakehouse makes payments ingestion and analytics simple
- » The Essential Guide to Data Lineage
- » Data Governance Explained in 5 Minutes
- » Automated Data Lineage with Unity Catalog
- » Active Metadata - Understanding the magic behind the Data Fabric
- » Introduction to Data Mesh
- » PostgreSQL vs MySQL
- » Activate Your Metadata to Empower Innovation
- » Accelerating Hybrid Data Mesh Implementation
- » Deep-Dive into Delta Lake
- » Delta Lake 2.0 Overview
- » Advancing Spark - The Photon Whitepaper
- » Column-Level Lineage and Active Metadata
- » Five Things to Consider About Data Mesh and Data Governance
- » Data Mesh Implementation Patterns
- » Intro to Databricks Lakehouse Platform Architecture and Security
- » Meshing About with Databricks
- » Enterprise Data Fabric
- » Data Fabric for Self-Driving Cars
- » Speed at Scale - Using GPUs to Accelerate Analytics for Extreme Use Cases
- » SpatialHadoop - A MapReduce Framework for Big Spatial Data
- » Mining Massive Data Sets
- » Latent Space Model for Road Networks to Predict Time-Varying Traffic
- » Three Applications of Deep Learning in Big Data Analytics
- » Fog Computing vs. Edge Computing
- » From Big to Now - The Changing Face of Data
- » Fast Data at Internet Scale with Amazon ElastiCache for Redis
- » Fast Data, Fast Monitoring
- » Wide & Deep Learning; Memorization + Generalization with TensorFlow
- » Keynote - TensorFlow Dev Summit 2017
- » Cloud of Things Overview
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- » 5 Ways to Make Your Hive Queries Run Faster
- » TensorFlow Introduction
- » Apache Ignite In-Memory Data Fabric
- » Introduction to HDFS Erasure Coding in Apache Hadoop
- » SDN and Big Data
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- » Analytics in Motion (AIM)
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- » Real-time Big Data Analytics
- » HiBench Suite
- » Map-D
- » MultiQx-GPU
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- » Where are the GPU based SQL databases?
- » Comparing Relational Databases, Memory Cache, and NoSQL
- » Oracle In-Memory Database Cache Overview
- » MapGraph
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- » Big Data, Fast Data The Need for In Memory Database Technology
- » Arrary DBMS
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- » Michael Stonebraker's Talk
- » Sample Data
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- » Creating Applications using Spark Streaming
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- » Teaching creative computer science
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- » Kudu - New Apache Hadoop Storage for Fast Analytics on Fast Data
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- » 2014 ACM A.M. Turing Lecture
- » What is the Future of Hadoop?
- » Sparrow Paper Review
- » Spark Architecture Shuffle
- » Explore In-Memory Data Store Tachyon
- » Tachyon Overview
- » Perform SQL Selects on R Data Frames
- » R Graphics
- » Google's R Style Guide
- » Approximate Aggregation Queries in Presto
- » Off-heap Memory in Apache Flink and the curious JIT compiler
- » AMP Camp 6
- » Spark 1.5 DataFrame API Highlights
- » Fundamentals of Parallel Computing with the GPU
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- » High-throughput, low-latency, and exactly-once stream processing with Apache Flink
- » Hyper
- » From Big Data to Fast Data
- » SAP Hana In-Memory Explained In Nine Minutes
- » Business unIntelligence
- » The Challenges of Data Science
- » How Data Science Driven Software is Eating the Connected World
- » Data Curation as Publishing for the Digital Humanities
- » How WellCare Accelerated Big Data Delivery to Improve Analytics
- » Sharing Experiences in Cloud Adoption
- » IBM Hybrid Transaction Analytic Processing HTAP
- » What is Hybrid Transaction/Analytical Processing (HTAP)?
- » CIOs and the Ongoing Data Persistence Struggle
- » Eight Big Trends in Big Data Analytics
- » Hybrid Trasansaction/Analytical Processing (HTAP)
- » Hybrid Trasansactional/Analytical Processing (HTAP)
- » HTAP and The Future of Data
- » Does ethical content curation exist? A data-driven answer
- » Stonebraker points to data curation as next integration step
- » Three Approaches to Scalable Data Curation
- » The Landscape of Big Data, Curation
- » Data Curation
- » Tackling Data Curation
- » Hype Cycle
- » The Virginian Database
- » The Analytical Bootstrap
- » DAQ Introduction
- » A New Paradigm for Approximate Query Processing
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- » Five most popular similarity measures implementation in python
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- » Apache Spark - Caching and Checkpointing Under the Hood
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- » Spark Streaming Basic Concepts
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- » Useful Research Related to Apache Drill
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- » Layers of Query Processing
- » Database Workloads
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- » Characteristics of an Analytic Workload
- » Workload Management for Big Data Analytics
- » SIX sparkling features of Apache Spark!
- » Use Parquet with Impala, Hive, Pig, and MapReduce
- » Actian Unleashes SQL Users on Hadoop with Easy Access, Faster Performance
- » Presto as a Service
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- » Serialization
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- » Why Oracle Big Data SQL Potentially Solves a Big Issue with Hadoop Security
- » Apache Tez Presentation
- » Announcing Apache Pig 0.13.0
- » Benchmarking Apache Hive 13 for Enterprise Hadoop
- » In-Stream Big Data Processing
- » Apache Hadoop YARN ResourceManager
- » Introduction To Pivotal's Next Generation Analytic Data Lake
- » War of the Hadoop SQL engines. And the winner is …?
- » Exploring Pivotal HD Single Node (VM)
- » How Pivotal Works
- » Parquet - Columnar Storage for the People
- » Pivotal Big Data Suite
- » Pivotal HD Introduction & Demo
- » Special "Use Cases" Big Data & Brews - Hortonworks, Pivotal, MapR
- » What is Apache Drill?
- » Drilling into Big Data with Apache Drill
- » Fun with TPC-H (Part 1)
- » Implementing Drill Math Functions
- » Presto By Facebook – A SQL Engine
- » Big Data - The Hadoop Data Warehouse
- » Building a Data Warehouse with Hadoop
- » Datawarehouse
- » Big Data Architecture
- » Comparing Information Architecture Operational Paradigms
- » Big Data Architecture Capabilities
- » What is SQL-on-Hadoop?
- » Apache Storm Basics
- » What does “100 times faster than Hive” actually mean?
- » GraphX Programming Guide
- » TPC Benchmark
- » M7 - Native Storage for MapR Tables
- » Hadoop 2.4.0 Installation on Mac OS X Mavericks
- » A Leap Forward With SQL on Hadoop
- » Special "How it Works" Big Data & Brews
- » 10 undeniable reasons people hate Big Data
- » 5 technologies that will help big data cross the chasm
- » Big Data & Brews - Ari Zilka, CTO of Hortonworks' Talk
- » NewSQL Vs. SQL on Hadoop
- » Presto as a Service
- » Adding ACID to Apache Hive
- » Eight SQL on Hadoop Challenges
- » 11 SQL-on-Hadoop Tools
- » Processing streaming data in Hadoop with Apache Storm
- » Interactive Query for Hadoop with Apache Hive on Apache Tez
- » Introducing Apache Tez 0.4
- » Big Data Genomics Sequencing
- » Smart Things vs. Many Experienced Things
- » Hardening Hadoop for Healthcare with Project Rhino
- » Functional Programming
- » Why Functional Programming?
- » Implementing Graph-Based Applications
- » Designing Graph-Based Applications
- » Data-intensive Computing
- » Ensuring the Validity, Veracity, and Volatility of Big Data
- » Operationalizing Big Data
- » Modern Oil & Gas Architectures Built with Hadoop
- » Visualization for Data-Intensive Science
- » Stanford Network Analysis Platform(SNAP)
- » Gray’s Laws Database-centric Computing in Science
- » Connecting to the Scientists
- » Big Data Dilemma
- » Polyglot Persistence
- » Predictive Methodologies - Analysis of Time and Space
- » Predictive Methodologies - Analysis of Space
- » Predictive Methodologies - Analysis of Social Networks
- » Theoretical Foundations of Data Mining and Predictive Analytics
- » Data Used in Predictive Policing
- » Operational Challenges of Predictive Policing
- » Forecast Vs. Prediction
- » Recommendations
- » Predictive Policing Myths and Pitfalls
- » Prediction-Led Policing Process and Prevention Methods
- » Facebook Presto
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » “Big Data” Promises a Revolution
- » T-Shaped Data Scientists
- » Adapting to Change
- » Agile Big Data
- » Scales of Big Data
- » The Variety of Data Scientists
- » The Real-Time Big Data Architecture (RTDBA) Stack
- » The Five Phases of Real Time
- » Case Studies of Analyzing Data
- » Data Science, Engineering, and Data-Driven Decision Making
- » Clustering Data Scientists
- » PredictionIO
- » Multi-tenant Database Systems
- » Big Data in Motion
- » Building the Health Information Infrastructure for the Modern ePatient
- » Solving the Wanamaker Problem for Healthcare
- » Understanding the Types of Big Data Problems
- » The Invention of Stream Computing
- » The Emergence of NoSQL
- » Modelling in Big Data
- » Analyzing Data in Motion A Real-World View
- » Understanding trade-offs with Brewer’s CAP theorem
- » Analytics vs. Predictive Analytics
- » Streams design patterns
- » The evolution of analytics
- » Impedance Mismatch
- » The Value of Relational Databases
- » What is NoSQL?
- » NoSQL History

- » Hybrid Grains
- » Beautiful Layered Materials, Instantly
- » Gaussian Material Synthesis
- » Self-Illuminating Explosions
- » An Efficient Scattering Material Representation
- » Leveraging Activation Sparsity for Training Deep Neural Networks
- » Deep Photo Style Transfer
- » AI Creates 3D Models From Faces
- » A modular GPU raytracer using OpenCL for non-interactive graphics
- » Semantic Scene Completion from a Single Depth Image
- » Real-Time Oil Painting on Mobile Hardware
- » Geometric Detail Transfer
- » Space-Time Video Completion
- » AI Makes Stunning Photos From Your Drawings
- » Real-time Fiber-level Cloth Rendering
- » Shape and Material from Video
- » Learning to Fill Holes in Images
- » Calendar of Computer Graphics Events
- » AI Builds 3D Models From Images With a Twist
- » How Do Hollywood Movies Render Smoke?
- » Fast Photorealistic Fur and Hair With Cone Tracing
- » Enhance Super Resolution From Google
- » Large-Scale Fluid Simulations On Your Graphics Card
- » AI Makes 3D Models From Photos
- » Text Style Transfer
- » Amazing Slow Motion Videos With Optical Flow
- » Neural Network Learns The Physics of Fluids and Smoke
- » Stunning Video Game Graphics With Voxel Cone Tracing
- » Papers that have used pbrt
- » Mathematical Methods for Robotics, Vision, and Graphics
- » Image Synthesis From Text With Deep Learning
- » PBRT Installation on macOS
- » Water Wave Simulation with Dispersion Kernels
- » Crumpling Sound Synthesis
- » Introduction to "Physically Based Shading in Theory and Practice"
- » Multiphase Fluid Simulations
- » Sound Propagation With Bidirectional Path Tracing
- » 3D Printing Acoustic Filters
- » SIGGRAPH 2016 Papers on the Web
- » Modeling Colliding and Merging Fluids
- » Surface-Only Liquids
- » Artistic Style Transfer For Videos
- » SIGGRAPH 2016 – Technical Papers Trailer
- » Separable Subsurface Scattering
- » Real-Time Shading With Area Light Sources
- » Face2Face; Real-Time Facial Reenactment
- » Embree; High Performance Ray Tracing Kernels
- » Narrow Band Liquid Simulations
- » Photos @ EDBT 2016
- » 3D Depth From a Single Photograph
- » Research Papers in Computer Graphics
- » Readings in Physically Based Rendering
- » Metropolis Light Transport
- » Data Visualization
- » Building LuxRender on OS X
- » Calendar of Computer Graphics Events
- » SIGGRAPH 2015 papers on the web
- » SIGGRAPH 2015 - Technical Papers Trailer
- » 39 Data Visualization Tools for Big Data
- » Keep Your Crises Small
- » Creativity, Inc. Interview
- » SIGGRAPH 2014 Technical Papers
- » SIGGRAPH 2014 Papers
- » Physically Based Shading in Theory and Practice
- » The Forgotten History Of CGI
- » Steve Jobs at SIGGRAPH 1995
- » Visualization for Data-Intensive Science
- » A Material Point Method For Snow Simulation
- » Reflections on Image-Based Modeling and Rendering
- » Korean Computer Graphics Conference 2013

- » SDN and Big Data
- » Central Office Re-architected as a Datacenter (CORD)
- » Basic ONOS Tutorial
- » Potential Novel Applications of Open SDN
- » Hybrid Unified Communications and Collaboration
- » SDN Note
- » RAMCloud Overview
- » RAMCloud
- » Open Network Operating System
- » Understanding the Differences between SDN and Server Virtualization
- » Open Daylight Technical Overview
- » An Introduction To Software Defined Networking
- » SDN과 NFV와 네트워크 가상화의 차이점 이해하기
- » How to prepare for SDN
- » Software-Defined Networks
- » Future of the Network Documentary

- » Knowledge Curation and Knowledge Fusion
- » Self-Curating Databases
- » Machine Driven, Human Guided Data Curation for Decision Advantage and Action
- » Machine Learning as the Key to Personalized Curation
- » Text Mining and Knowledge Graphs in the Cloud
- » Knowledge Graph and Cognitive Computing
- » Knowledge Graph
- » Data Curation as Publishing for the Digital Humanities
- » Does ethical content curation exist? A data-driven answer
- » Stonebraker points to data curation as next integration step
- » Three Approaches to Scalable Data Curation
- » The Landscape of Big Data, Curation
- » Data Curation

- » How to Write a Great Research Paper
- » Vision Transformer - Keras Code Examples
- » Vision Transformer
- » MLP-Mixer; An all-MLP Architecture for Vision
- » From AlphaGo to MuZero
- » Moral Machines; How culture changes values?
- » Generative Adversarial Networks
- » Hardness of Approximation Between P and NP
- » GPT-3 = Generation Pre-trained Transformer 3
- » Graph Attention Networks Implementation
- » Introduction to Pytorch Geometric
- » Deep Learning for Graphs
- » Geometric Deep Learning
- » Graph Neural Networks - Models and Applications
- » Matching of Matching Graphs
- » Graph Classification using Structural Attention
- » Approximate Personalized PageRank on Dynamic Graphs
- » GCNs for Web-Scale Recommender Systems
- » Inductive Representation Learning on Large Graphs
- » Write Research Paper Outline
- » How to Respond to Reviewer Comments
- » Limitations of Graph Neural Networks
- » Choice of Graph Representation
- » Inductive Representation Learning on Temporal Graphs
- » Applications of Graph ML
- » Machine Learning with Graphs
- » How to Review a Research Paper
- » Approximate standing queries on Stream Processing
- » Learning Mesh-Based Simulation with Graph Networks
- » Temporal Graph Networks for Deep Learning on Dynamic Graphs
- » Graph Neural Networks and Knowledge Graph Completion
- » Parallel and Distributed Computing in Python with Dask
- » Reducing Communication in Graph Neural Network Training
- » Mathematical Foundations of the GraphBLAS and Big Data
- » Graph Signal Processing - Accounting for Geometry in Data
- » Graph Signal Processing for Machine Learning Applications
- » Papers on Recommendation Systems
- » Representational Power of Graph Neural Networks
- » Tunable Stream Graph Embeddings at Scale
- » Graph Neural Networks
- » Graph Convolutional Networks using only NumPy
- » Temporal Graph Networks for Dynamic Graphs
- » Graph Algorithms as Matrix Vector Products
- » Distributed Machine Learning with Python
- » Deep Networks Are Kernel Machines
- » How the Quest for the Ultimate Learning Machine Will Remake Our World
- » Fast Distributed Algorithms for Girth, Cycles and Small Subgraphs
- » Graph Sketching, Streaming, and Sampling
- » Theoretical Foundations of Graph Neural Networks
- » Efficiently Answering Span Reachability Queries
- » Probabilistic Reachability for MDPs
- » How computer science can help fight COVID-19
- » Enable Digital Transformation with Big Spatial Data & Analytics
- » Online Approximation Techniques for Spatial Data
- » Sparse Matrices Beyond Solvers
- » New Machine Learning Tool Tracks Urban Traffic Congestion
- » Graph Convolutional Neural Networks for Molecule Generation
- » Deep Learning on Graphs
- » Principles and Applications of Relational Inductive Biases
- » Traffic Prediction with Advanced Graph Neural Networks
- » Papers on Graph Neural Networks
- » Self-Driving Databases
- » Non-Volatile Memory for Database Management Systems
- » Using Apache Arrow, Calcite and Parquet to build a Relational Cache
- » Data Vocalization
- » Cloud of Things Overview
- » Blockchain Explained
- » Proceedings of the VLDB Endowment
- » Data Preparation for Machine Learning
- » How to Prepare Data For Machine Learning
- » IoT, Azure, Machine Learning and more
- » IBM Watson IoT Platform
- » Revisiting Reuse in Main Memory Database Systems
- » What's New with NewSQL?
- » Spark-GPU; An Accelerated In-Memory Data Processing Engine on Clusters
- » GPU SQL Query Accelerator
- » Multi-core Parallelism in a Column-store
- » Self-Driving Database Management Systems
- » Graph Mining for Log Data
- » Understanding Multitenancy and the Architecture of the Salesforce Platform
- » Probabilistic Ranking Problem
- » Data Blocks; Hybrid OLTP and OLAP on Compressed Storage using both Vectorization and Compilation
- » PVLDB Papers
- » Progressive skyline computation in database systems
- » Proceedings of the VLDB Endowment, Volume 10
- » Vectorized Executor
- » Skyline Operator
- » Top 10 Strategic Technology Trends 2016
- » Faster In Memory Computing with No Deadlocks
- » The Emergence of Converged Data Platforms and the Role of In Memory Computing
- » Parallel Data Processing
- » Apache Ignite In-Memory Data Fabric
- » Smart Data Discovery
- » Introduction to LLVM
- » Min-cost Flow Network Algorithms
- » Elasticity with YARN
- » Breakthrough Processor and System Design with SPARC M7
- » Life Beyond Column-Stores - Exploiting intra-cycle parallelism
- » Knowledge Curation and Knowledge Fusion
- » OLTPBench
- » CrowdSky; Skyline Computation with Crowdsourcing
- » Automatic Source Transformation from C++ to CUDA using Clang/LLVM
- » Efficient Computation of Containment and Complementarity in RDF Data Cubess
- » Data Processing in Modern Hardware
- » Self-Curating Databases
- » Verification of Evolving Graph-structured Data under Expressive Path Constraints
- » Top-k Indexes Made Small and Sweet
- » Dynamic Graph Queries
- » Worst-Case Optimal Algorithms for Parallel Query Processing
- » Declarative Probabilistic Programming with Datalog
- » Lightning Fast Social Media Analytics
- » Statistics-Driven OLAP Acceleration using Query Column Sets
- » Concurrency Control and Recovery for Search Trees
- » How DeepMind Conquered Go With Deep Learning
- » 자유가 없는 나라의 공부
- » Skip List
- » Transactional Memory in Practice
- » DB2 for z/OS Best Practice; Locks and Latches
- » Database Recovery - Basic Concepts
- » Latches
- » Apache Calcite Overview
- » A Better B-Tree in Hekaton
- » The Bw-Tree; A B-tree for New Hardware
- » Hekaton Research
- » Distributed Transactions
- » ACID Isolation Level
- » Notes About Memory Allocation Redesign
- » Postgres Modifications
- » The Databaseology Lectures
- » C++ Arena Allocation Guide
- » Region-based Memory Management
- » PostgreSQL
- » What is VoltDB?
- » Release Notes - Tajo - Version 0.11.1
- » Peloton Architecture
- » Vagrant Tutorial
- » Introduction to Vagrant
- » Insert-Only Implementation Strategies
- » A Hands-free Adaptive Store
- » The Bw-Tree Key-Value Store
- » Bitmap Indices
- » IBM Cognos Analytics
- » Understanding the SQL Server Columnstore Index
- » Main Memory Databases
- » Dictionary-based Order-preserving String Compression
- » Hekaton Breaks Through
- » The Merits of Data Compression
- » DBMS Components
- » Compensating Transaction
- » In-Memory Column Store Architecture Overview
- » Advantages & Disadvantages of an In-Memory Database
- » Latches, Spinlocks, and Lock Free Data Structures
- » Research Topics in Database Management
- » Main Memory DBMS
- » HYBRID WORKLOADS AND HTAP
- » Peloton
- » Database Systems Course
- » Gartner Top 10 Tech Trends for 2016
- » CES 2016 Keynote
- » MVCC Overview
- » SAP HANA
- » HYRISE
- » Main Compensation and Delta Compensation
- » Aggregate Cache
- » MonetDB Source compilation on OS X
- » In-Memory Accelerator for Hadoop
- » Main Memory and Streaming Databases
- » Building Postgresql on OS X
- » TPC-H
- » Setting Up An Organized Folder Structure
- » TPC-DS
- » Proceedings of the VLDB Endowment, Volume 9
- » In-Memory Data Management Course
- » Machine Driven, Human Guided Data Curation for Decision Advantage and Action
- » A GPU Parallelized Database Accelerator
- » Databaseology Lectures
- » Zeppelin Deployment
- » Zeppelin Structure
- » The Pushdown of Everything
- » Zeppelin Installation
- » MySQL Installation on Ubuntu
- » PVLDB 2015 Vol. 9
- » Data Engineering Course
- » Apache Spark vs. MapReduce
- » Korean Constitution
- » Database Reading List
- » Big Data, Fast Data The Need for In Memory Database Technology
- » Arrary DBMS
- » Arrary Databases
- » Introduction to LLVM
- » MySQL Installation on Mac OS X
- » Sample Data
- » RAMCloud Presentations
- » Teaching creative computer science
- » Discardable Memory and Materialized Queries
- » 2014 ACM A.M. Turing Lecture
- » IEEE Transactions on Knowledge and Data Engineering
- » Optimizing Big Data Through Curation
- » Cognitive Computing
- » SAP S4 HANA Technical White Board
- » Using Electronic Health Records for Better Care
- » 6 Big Data Analytics Use Cases for Healthcare IT
- » Four Use Cases for Healthcare Predictive Analytics, Big Data
- » Five open source Big Data projects to watch
- » Reservoir Sampling in MapReduce
- » Clinical Data Repository Versus a Data Warehouse
- » Top 50 Data Science Resources
- » High-throughput, low-latency, and exactly-once stream processing with Apache Flink
- » Hyper
- » From Big Data to Fast Data
- » SAP Hana In-Memory Explained In Nine Minutes
- » Business unIntelligence
- » The Challenges of Data Science
- » How Data Science Driven Software is Eating the Connected World
- » Data Curation as Publishing for the Digital Humanities
- » How WellCare Accelerated Big Data Delivery to Improve Analytics
- » Sharing Experiences in Cloud Adoption
- » IBM Hybrid Transaction Analytic Processing HTAP
- » What is Hybrid Transaction/Analytical Processing (HTAP)?
- » CIOs and the Ongoing Data Persistence Struggle
- » Eight Big Trends in Big Data Analytics
- » Hybrid Trasansaction/Analytical Processing (HTAP)
- » Hybrid Trasansactional/Analytical Processing (HTAP)
- » HTAP and The Future of Data
- » Mega-KV, A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores
- » SDN Note
- » My Top 10 Assertions about Data Warehouses
- » RAMCloud Overview
- » RAMCloud
- » The Virginian Database
- » Massive Throughput Database Queries with LLVM on GPUs
- » Apache Tajo Internal
- » Readings in Data Management
- » Database Management Systems
- » Planning the Approach
- » Database Management Systems
- » Readings in Database Systems
- » Query Optimization Bibliography
- » PostgreSQL
- » Advantages and Drawbacks of Using Stored Procedures for Processing Data
- » What is SQL-on-Hadoop?
- » What does “100 times faster than Hive” actually mean?
- » GraphX Programming Guide
- » TPC Benchmark
- » Hadoop 2.4.0 Installation on Mac OS X Mavericks
- » Choosing distribution models
- » Hadoop Sector will Have Annual Growth of 58% for 2013-2020
- » Why Impala Continues to Lead?
- » Apache Spark Resource Management and YARN App Models
- » Measures of Query Cost
- » Query Processing
- » Query Transformation
- » Database Conferences
- » Query Optimizer
- » Research Topics in Database Systems
- » ACID Properties of Transactions
- » Introduction to Distributed Database Management Systems
- » Introduction to Data Warehousing
- » Date's Twelve Rules for a DDBMS
- » Depth of Knowledge
- » Mapping and Folding and the Map-Reduce Paradigm
- » Codd's 12 Rules
- » OLAP vs. OLTP
- » Comparison - Ad-hoc vs Stored Procedure vs Dynamic SQL
- » FINDING INFORMATION ON THE WEB
- » Advanced Metasearch Engine Technology
- » Multi-tenant Database Systems
- » Building a Hybrid Modern Data Architecture for Apache Hadoop
- » Continuous Quries Languages
- » Semantics of Relations in Continuous Queries
- » Continuous Quries as Views
- » Building the Health Information Infrastructure for the Modern ePatient
- » Continuous Query Semantics and Operators (Part II)
- » Stream Windows
- » Continuous Query Semantics and Operators (Part I)
- » Writing Input And Output Adapters
- » Stream Models
- » In-Memory Caches
- » Pattern Matching Techniques
- » Real-time Operational Intelligence
- » Real-Time Data Stream Manager (RTDSM) Interface Specification
- » Modelling in Big Data
- » Understanding trade-offs with Brewer’s CAP theorem
- » VLDB 2013
- » Impedance Mismatch
- » NoSQL History

- » Latent Dirichlet Allocation Concept
- » Reinforcement Learning Course - Georgia Tech
- » Digital Transformation in a Connected World
- » What is Graph Analytics?
- » Graphs in Machine Learning
- » Social and Information Network Analysis
- » Structural Analysis and Visualization of Networks
- » Deep Learning for Database Performance Optimization
- » No Such Thing As Artificial Intelligence
- » Machine Learning as the Key to Personalized Curation
- » 22nd ACM SIGKDD Conference
- » What is Zeppelin Interpreter
- » Using Electronic Health Records for Better Care
- » 6 Big Data Analytics Use Cases for Healthcare IT
- » Four Use Cases for Healthcare Predictive Analytics, Big Data
- » Five open source Big Data projects to watch
- » Reservoir Sampling in MapReduce
- » Clinical Data Repository Versus a Data Warehouse
- » Top 50 Data Science Resources
- » Future of big data analytics
- » Five most popular similarity measures implementation in python
- » Requirements for Cluster Analysis
- » What is Cluster Analysis?
- » Cluster Analysis
- » GPU Programming for the Data Sciences
- » Statistics, Data Mining, Machine Learning and Artificial Intelligence
- » Regression Analysis
- » The Data Science Process
- » Statistical Thinking in the Age of Big Data
- » Populations and Samples
- » Thought Experiment - Meta-Definition
- » A Data Science Profile
- » The Role of the Social Scientist in Data Science
- » What is Data Science? - The Current Landscape
- » What is Data Science?
- » T-Shaped Data Scientists
- » The Variety of Data Scientists
- » Data Science, Engineering, and Data-Driven Decision Making
- » Clustering Data Scientists

- » Dependency Injection
- » 11 Core Big Data Workload Design Patterns
- » Bigtable
- » Chubby
- » The Google File System (GFS)
- » Overview of Data Storage and Coordination Services
- » Underlying Communication Paradigms
- » Overall Architecture and Design Philosophy
- » JUnit, A Tool for Test-Driven Development
- » Concurrent Bug Patterns and How to Test Them
- » Introduction to Bug Patterns in Java
- » Foundational data architecture patterns
- » Streams Application Design
- » Filter pattern

- » Visual Studio Code, CMake and LLDB
- » Extreme Team
- » Visual Studio Code - C++ Development
- » UML Tools for Python
- » Introduction to gRPC
- » Design Patterns in Python
- » Real-Time Data Processing with Apache Flink
- » Mesos Installation
- » Building Mesos
- » Traffic Prediction
- » An Introduction to Protobufs
- » Taking the I out of PaaS with Apache Mesos
- » Software Projects Built on Mesos
- » Distributed Deep Learning on Mesos
- » Apache Mesos and GPUs
- » Apache Mesos
- » Pair Programming Tips
- » In-core computation with HyperBall
- » 소프트웨어 개발의 이해를 돕기 위한 비유
- » Redmine Plugin Installation
- » Wearable Technology Trends in 2016
- » Building LuxRender on OS X
- » KIWI Installation
- » IntelliJ IDEA 15
- » Maven Support in IntelliJ IDEA
- » CUDA LLVM Compiler
- » Stanford Compilers Course
- » Unit Test using specs
- » Scala to Java
- » Privacy as a Practice in Mobile App Development
- » Function Composition
- » Scala Collection
- » Common Lisp using LLVM and C++ for Molecular Metaprogramming
- » Big Data Challenges and Opportunities
- » Rethinking SIMD Vectorization for In-Memory Databases
- » Scala idiom - Prefer immutable data structures
- » Scala - Tools and Frameworks
- » Scala - Pattern Matching
- » Scala - Functional/Collection
- » Why Scala?
- » Introduction to Scala
- » Scala - Object Oriented Features
- » Scala Installation
- » Scala Basics
- » SBT (Simple Build Tool)
- » GNUPlot Basics
- » Google Protocol Buffer Basics - Java
- » How to fix “System program problem detected” error on Ubuntu
- » Samsung Developer Day 2014 at MWC - Samsung MultiScreen SDK
- » Using Gitlab on Mac OS X
- » Gitlab Installation on Mac OS X
- » Why Functional Programming?
- » Macport Commands Summary
- » TDD Best practices
- » Test-Driven Development in a nutshell
- » Introduction to the Maven
- » Native C/C++ Like Performance For Java Object Serialization
- » Using ByteBuffer in Java
- » Esper Relational Database Adapter
- » Event Rendering to XML and JSON
- » Esper Socket Adapter
- » Esper Adapter Concept
- » Scatter plots in Python
- » Generating random numbers and plots in Python
- » JUnit, A Tool for Test-Driven Development
- » Syntax highlighting tools for github page
- » Boxplots comparing two different populations in Python
- » JAXB Class Generation From XML Schema File using JAXB Tool
- » Introduction to Esper for Java
- » My Scoop It! Sites
- » Concurrent Bug Patterns and How to Test Them
- » Introduction to Bug Patterns in Java
- » N-Screen Application Development Manual
- » Android WebView Application using Naver Open API
- » Extreme Programming
- » Git Commands
- » Git Concepts and Architecture
- » Adding a post to github page

- » Gunrock
- » Compiling CUDA C/C++ with LLVM
- » gpucc; an open-source GPGPU compiler
- » Automatic Source Transformation from C++ to CUDA using Clang/LLVM
- » Map-D
- » Rethinking the Role of CPUs in Modern Computers
- » MultiQx-GPU
- » Concurrent Analytical Query Processing with GPUs
- » A GPU Parallelized Database Accelerator
- » Fundamentals of Parallel Computing with the GPU
- » Mega-KV, A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores
- » The Virginian Database
- » Massive Throughput Database Queries with LLVM on GPUs
- » Hadoop + GPU Architecture
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » IBM Juices Hadoop With Java On Tesla GPUs
- » Setup a JavaCL Maven Project
- » Some Common Parallel Patterns
- » Why does MapReduce + GPU Computing?
- » GPU Programming for the Data Sciences
- » GTC On-Demand
- » Computational Thinking

- » Activate Your Modern MetaData Stack
- » Databricks Lakehouse makes payments ingestion and analytics simple
- » The Essential Guide to Data Lineage
- » Data Governance Explained in 5 Minutes
- » Automated Data Lineage with Unity Catalog
- » Active Metadata - Understanding the magic behind the Data Fabric
- » Introduction to Data Mesh
- » PostgreSQL vs MySQL
- » Activate Your Metadata to Empower Innovation
- » Accelerating Hybrid Data Mesh Implementation
- » Deep-Dive into Delta Lake
- » Delta Lake 2.0 Overview
- » Advancing Spark - The Photon Whitepaper
- » Column-Level Lineage and Active Metadata
- » Five Things to Consider About Data Mesh and Data Governance
- » Data Mesh Implementation Patterns
- » Intro to Databricks Lakehouse Platform Architecture and Security
- » Meshing About with Databricks
- » Enterprise Data Fabric
- » Converging Advances to Accelerate Molecular Simulation
- » Learning Delaunay Surface Elements for Mesh Reconstruction
- » Data Fabric for Self-Driving Cars
- » Knowledge Graphs Seminar
- » Data Cataloging with Knowledge Graphs
- » The Foundation of a Data Fabric
- » Data Mesh, Data Fabric, Data Lakehouse
- » Tractable Probabilistic Circuits
- » Distributed Analytical Database Systems
- » GraphZeppelin - Streaming Graph Connectivity at Scale
- » Understanding Graph Data Representations in Triplestores
- » Graph-Powered Data Exploration
- » Google BigQuery
- » Conformal Prediction Tutorial
- » Traffic Prediction Paper Collection
- » End-to-end Optimization of Machine Learning Prediction Queries
- » Compute Complex Temporal Join Queries Efficiently
- » Making Learned Query Optimization Practical
- » Universal Database Optimizer
- » Machine Learning for Query Optimization
- » ML Explainability
- » Towards Generalizable Autonomy
- » Building an AI and ML ready Modern Data Platform
- » Diffusion Probabilistic Modelling of Protein Backbones in 3D
- » ICCV Authors Guidelines
- » Diffusion Probabilistic Models
- » Stable Diffusion - What, Why, How?
- » Deep Energy-Based Learning
- » Incident Prediction in Spatio-Temporal Road Graph Networks
- » Diffusion probabilistic modelling of protein backbones in 3D
- » Contrastive Learning in PyTorch
- » Neural Rendering
- » Anatomy of a Research Paper
- » Geometric Deep Learning for Drug Discovery
- » Deploy Custom Python Model Server
- » Contrastive Learning in PyTorch
- » InferenceService using a Custom Torchserve Image
- » From Machine Learning to Autonomous Intelligence
- » Towards a Learned Index Structure for ANN Search
- » Graph Processing; from Academic Research to Industrial Application
- » Katana Graph; A Graph Intelligence Platform
- » Machine Learning for Scientific Discovery
- » Graph Neural Networks as Gradient Flows
- » Ab-Initio Potential Energy Surfaces
- » Reinforcement Learning via Sequence Modeling
- » Recipe for a General, Powerful, Scalable Graph Transformer
- » Spatial Hash Grids & Tales from Game Development
- » Cardinality Estimation Benchmark
- » Spatial Indexing in PostGIS
- » Data-driven Learned Metric Index; an Unsupervised Approach
- » Synopses - Samples, Histogram, Wavelets and Sketches
- » Query Execution in MonetDB
- » Low overhead self-optimizing storage for compression
- » A Database Tuning Tool that "Reads the Manual"
- » Rethinking Graph Transformers with Spectral Attention
- » Introduction to TorchServe
- » MicroK8s Installation & Simple Commands
- » Diffusion Models from Scratch in PyTorch
- » Deep Learning Theory Lectures
- » Kubeflow Fairing
- » Kubeflow Fairing, Pipelines, and Training
- » On Recoverability of Graph Neural Network Representation
- » Deploy Model to KServe
- » TorchScript and PyTorch JIT
- » Spectra of Graphs and Hypergraphs
- » How to Serve PyTorch Models with TorchServe
- » A Novel Data Set for Information Retrieval on the Basis of Subgraph Matching
- » Graph Embedding in Vector Spaces Using Matching-Graphs
- » Google Cloud BigQuery ML Using SQL
- » Open World Lifelong Learning
- » On Machines that can Learn Continually
- » Introduction to Continual Learning
- » Introducing OpenAPI Generator
- » Kubeflow Development Environment
- » KFServing Deep Dive
- » Protein structure prediction with AlphaFold
- » Deep Learning for Scientific Computation
- » Started with AI in Drug Discovery
- » Istio Simplified
- » Knative and Microk8s with Multipass
- » KServe in Kubenetes
- » Graph Ordering Attention Networks
- » Introduction to KServe
- » Position-Aware Graph Neural Network using Reachability Estimations
- » Isomorphism Using Adjacency Matrix
- » Efficient Probabilistic Truss Indexing on Uncertain Graphs
- » Toward Verified Artificial Intelligence
- » Modern Data Stack
- » PyTorchJobClient
- » KServe (Kubeflow KFServing) Live Coding Session
- » Workflow for ML Projects — MLOps
- » Kubeflow Setup
- » Introduction to Data Mesh
- » Theory of Graph Neural Networks
- » Representational Power of Graph Nerual Networks
- » Task structure and generalization in graph neural networks
- » Geometry Processing with Neural Fields
- » Trends in AI
- » RelationalAI Knowledge Graph Management System
- » Topological Graph Neural Networks
- » Causality and (Graph) Neural Networks
- » Using Graph Neural Networks for Multi-Node Representation Learning
- » Epistemic Uncertainty Estimation for Efficient Search of Drug Candidates
- » Toward Neuro Causality
- » Connect to MySQL Database from Visual Studio Code
- » Data-Efficient Graph Grammar Learning for Molecular Generation
- » Rethinking Graph Transformers with Spectral Attention
- » How GNNs and Symmetries can help to solve PDEs
- » Neural diffusion PDEs, differential geometry, and graph neural networks
- » BigQuery ML; Machine Learning with Standard SQL
- » GNN with Learnable Structural and Positional Presentations
- » Istio Service Mesh 101
- » Graph Neural Networks and Diffusion PDEs
- » Recommender Systems using Graph Neural Networks
- » Manage Multi-tenant ML Workloads Using Istio
- » Graph Neural Networks as Neural Diffusion PDEs
- » Exploring ML Model Serving with KServe
- » Kubeflow Tutorial | Model Serving
- » Using AI to accelerate scientific discovery
- » Java to MySQL using Visual Studio Code
- » Developing the Kubernetes Python Client
- » Kubernetes Patterns
- » Using Visual Studio Code for Java Maven Project
- » Mapping External Services
- » MiniKF; Kubeflow on your laptop
- » Gateway Routing Pattern
- » Kubeflow v1.5 Release Overview
- » TensorFlow Distributed Training on Kubeflow
- » Sidecar Pattern
- » YAML
- » Kubeflow on macOS
- » Why is Kubernetes called K8s?
- » Kubernetes Crash Course for Absolute Beginners
- » Marrying Top k with Skyline Queries
- » Spatial Skyline Queries on Triangulated Irregular Networks
- » Challenges and Solutions of Using Kubernetes for Blockchain Applications
- » PyTorch Training (PyTorchJob)
- » Kubeflow Architecture
- » Istio and Service Mesh
- » Kubernetes Tutorial for Beginners
- » Serving Machine Learning Models at Scale Using KServe
- » Advanced Model Inferencing Leveraging Kubeflow Serving
- » PyTorch via SQL Commands
- » Scaling Shortest Path Graph Queries on Very Large Networks
- » Hierarchical Core Maintenance on Large Dynamic Graphs
- » Shortest Paths and Centrality in Uncertain Networks
- » Bag of Tricks for Node Classification with Graph Neural Networks
- » The Case for Learned Index Structures
- » Deep Learning for Graph Similarity Search
- » HyperSPNs; Compact and Expressive Probabilistic Circuits
- » Learned Index Structures for Dynamic and Multi-Dimensional Data
- » Blame the Data, Not the System
- » Hyperbolic Embeddings in Machine Learning and Deep Learning
- » What are Temporal Databases?
- » Git Commit Message Style Guide
- » What is PrestoDB?
- » Spatial, High Dimensional, Temporal Data Indexing and Querying
- » A Learned Spatial Index for Range and kNN Queries
- » An Introduction to Spatial Data and its Applications
- » Robustness/Interpretability in Vision & Language Models
- » Visual Data Analysis; How? When? Why?
- » SageDB; A Self-Assembling Database System
- » Towards Zero-Shot Learning for Databases
- » From Workload-Driven to Zero-Shot Learning
- » From Notebook to Kubeflow Pipelines to KFServing
- » Improving Transfer and Robustness of Supervised Contrastive Learning
- » Approximate Query Processing
- » What are Probabilistic Data Structures?
- » Approximation Algorithms for Large Scale Data Analysis
- » Modeling Relational Data with Graph Convolution Network
- » Open-source Change Data Capture With Debezium
- » Finding Approximately Repeated Patterns in Time Series
- » Looper; An End-to-End ML Platform for Product Decisions
- » Learned Indexing and Sampling for Improving Query Performance
- » Principles of Good Machine Learning Systems Design
- » Deep Learning Design Patterns
- » Tips for Thinking Like a Machine Learning Architect
- » Machine Learning Design Patterns
- » Introduction to Kubeflow
- » Martingale Theory
- » Making Architecture Matter
- » Bayesian Inference for Big Data
- » Architecture and Cardinality Estimations for Graph Queries
- » What causes congestion?
- » Optimization problems in graphs with locational uncertainty
- » Deep Implicit Layers
- » Convex Functions
- » Quantum Deep Learning
- » Uncertainty and Out-of-Distribution Robustness in Deep Learning
- » Probabilistic Methods for Increased Robustness in Machine Learning
- » Probabilistic Circuits - Representations, Inference, Learning and Theory
- » FLAT-Fast, Lightweight and Accurate Method for Cardinality Estimation
- » Learned Query Scheduling
- » A 10 Minute Introduction to Kubeflow
- » Ethics of Artificial Intelligence
- » What is AI Ethics?
- » What is the Cardinality Estimator?
- » Qd-tree - Learning Data Layouts for Big Data Analytics
- » Sum-Product Networks
- » Towards Zero-Shot Learning for Databases
- » Machine Learning Design Patterns
- » Learned DBMS Components 2.0
- » Deep Multi-task and Meta Learning
- » Machine Learning Design Patterns - Rebalancing
- » Calcite Tutorial
- » NeuroCard - One Cardinality Estimator for All Tables
- » GNN based Recommender Systems
- » On the Nature of Data Science
- » Breaking the Limit of Graph Neural Networks using Local Mixing Patterns
- » Performance Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks
- » Data Fabrics for Enterprise Data Management?
- » Query Optimization and Acceleration at Dremio
- » AI4DB & DB4AI-Papers
- » Permissioned Blockchains
- » Apache Spark, Data Science + Machine Learning
- » Introduction to Temporal Graph Neural Networks
- » Traffic Forecasting with Pytorch Geometric Temporal
- » Geometric Deep Learning
- » What Is NFT? - Non Fungible Token
- » Deep Recommender Systems
- » Weisfeiler and Lehman Go Cellular; CW Networks
- » Matrix Multiplication, and the Asymptotic Spectrum Of Tensors
- » Traffic Prediction for Intelligent Transport System
- » Revisiting Knowledge Graph Completion From a Practical Perspective
- » Applying Machine Learning-based Database Tuning in Production
- » Cloud-Native Ledger Graph Database
- » A Probabilistic Model for Joint Inference from Differential Equations
- » Optimization Frameworks for Graph Clustering
- » Neuralising a Computer Scientist
- » Preparing For Your Dissertation Defense
- » Oral Dissertation Defense Preparation
- » How To Defend Your Thesis? Top 10 Tips For Success
- » How to Get a PhD in Computer Science
- » 100 Thesis Defense Questions in 3 Categories
- » 10 Mistakes to Avoid When Defending Your Thesis
- » How to prepare for your PhD thesis defence
- » Geometric Deep Learning-Past, Present, And Future Optimization
- » Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
- » How to write a literature review - my simple 5 step process!
- » Geometric Deep Learning - from Euclid to drug design
- » Graph Isomorphism in Quasipolynomial Time
- » Geometric Deep Learning Blueprint
- » Geometric Deep Learning-The Erlangen Programme of ML
- » KDD Cup OGB Large-Scale Challenge
- » Theoretical Foundations of Data-Driven Algorithm Design
- » Relational Inductive Biases, Deep Learning, and Graph Networks
- » The Future Is Big Graphs
- » The Universal Approximation Theorem
- » Equivariant Networks and Natural Graph Networks
- » An Introduction to Hamiltonian Monte Carlo Method for Sampling
- » Recurrent Neural Networks for Cognitive Neuroscience
- » Permutation Equivariance of Graph Filters
- » Graph Convolutional and Isomorphism Networks
- » Combinatorial Properties of the Weisfeiler-Leman Algorithm
- » Advances in Self-Supervised Learning
- » Contrastive Loss, Deep Metric Learning
- » Self-Supervised Learning
- » Contrastive Learning; A General Self-supervised Learning Approach
- » Supervised Contrastive Learning
- » What is Noise-Contrastive Estimation?
- » GNNs with Learnable Structural and Positional Representation
- » Knowledge Graph Representation
- » Temporal Graph Networks (TGN)
- » Building a Recommender System using Graph Neural Networks
- » Junction Tree Variational Autoencoder for Molecular Graph Generation
- » Understanding Graph Attention Networks
- » GVAE Training and Adjacency Reconstruction
- » Graph Representation Learning for Drug Discovery
- » Graph Neural Networks for Traffic Prediction
- » Visual Exploration of Trajectory Data
- » GPS Data Analysis with Python
- » 22 Python libraries for Geospatial Data Analysis
- » Clustering Trajectories
- » Towards Causal Representation Learning
- » Random Sum Product Networks
- » Understanding & Generating Source Code with Graph Neural Networks
- » Sum-Product Networks; The Next Generation of Deep Models
- » Deep Learning to Discover Coordinates for Dynamics
- » The Power of Graph Signal Processing
- » Some Mathematical Problems in Graph Signal Processing
- » On Laplacian Eigenmaps for Dimensionality Reduction
- » Overview of Graph Embeddings
- » Sum-Product Networks; Powerful Models with Tractable Inference
- » Sum-Product Networks
- » Geometric Deep Learning and Reinforcement Learning
- » Discovering Genetic Medicines with our AI Drug Discovery Platform
- » Proof of Euler's Formula
- » GNNs and RL in Traffic Optimization in Cities
- » Graph Neural Networks and Applications to Deep Reinforcement Learning
- » Writing Scientific Papers
- » Drug Discovery with GANs
- » PyTorch nn.Embedding()
- » Reproducing Kernel Hilbert Spaces
- » Dynamical Systems and Machine Learning
- » An Introduction to Hilbert Spaces
- » Graph Neural Networks in Computational Biology
- » What is Euler's Number 'e'?
- » Euler's Formula
- » The Characteristic Equation and Eigenvalues
- » Spanning Trees and Kirchhoff's Theorem
- » Acceptance Rate of AI Conferences
- » Deep Graph Learning Foundations, Advances and Applications
- » Learning Graphs from Data; A Signal Processing Perspective
- » Making Graphs Compact by Lossless Contraction
- » A Short Course in Spectral Graph Theory
- » An Introduction to Spectral Graph Theory
- » Spectral Graph Theory; Enter Linear Algebra
- » Spectral Graph Theory; The Standard Random Walk
- » Spectral Graph Theory; The Quadratic Form
- » The Laplacian Matrices of Graphs; Algorithms and Applications
- » Pragmatic Ridge Spectral Sparsification for Large-Scale Graph Learning
- » Knowledge Graph Embeddings Tutorial; From Theory to Practice
- » How to use edge features in Graph Neural Networks
- » Graph Representation Learning
- » Papers on Graph Neural Networks
- » A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
- » Super-Constant-Pass Streaming Lower Bounds for Reachability
- » Awesone Neural ODE
- » Neural Differential Equations
- » Generative Adversarial Networks and TF-GAN
- » Fairness and Bias in Algorithmic Decision-Making
- » A Learned Sketch for Subgraph Counting
- » Learning over sets, subgraphs, and streams
- » Categorical Reparameterization with Gumbel-Softmax
- » A Deterministic Parallel APSP Algorithm and its Applications
- » How Do You Generate Synthetic Data?
- » Synthetic Tabular Data Generation
- » Decremental All-Pairs Shortest Paths in Deterministic Near-Linear Time
- » Streaming Lower Bounds for Reachability
- » Learning Deep Matrix Factorizations Via Gradient Descent
- » NVIDIA Omniverse and a Future of Shared Worlds
- » Introduction to Graph Neural Networks
- » CVPR 2021 Workshop on Autonomous Vehicles
- » Pytorch-BigGraph; A Large Scale Graph Embedding System
- » Unsupervised Intelligent Agents
- » Uncertainty, causality and generalization
- » Illustrated Guide to RNN, LSTM, and Transformers
- » Banach Space Representer Theorems for Neural Networks
- » Next-Generation Recurrent Network Models
- » Graph Representation Learning for Drug Discovery
- » PostgreSQL Optimizer Methodology
- » PPRGo-Scaling Graph Neural Networks with Approximate PageRank
- » The Energy-Based Learning Model
- » But how does bitcoin actually work?
- » Explainable AI Cheat Sheet - Five Key Categories
- » Digital Twins and Geospatial Data
- » Trends in Traffic Prediction
- » Understanding Competencies
- » Scaling Up Graph Neural Networks to Large Graphs
- » Inductive Logic Programming
- » Useful Resources for Traffic Prediction
- » A Video Game That Looks Like Reality!
- » Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
- » New lower and upper bounds for quantile summary algorithms
- » Data Summarization for Machine Learning
- » Position aware Graph Neural Networks
- » Instacart Data Description
- » Fast Counts on Big Data Systems
- » MonetDB; Scale Up Before You Scale Out
- » Kronecker Graphs, Data Generation, and Performance
- » AI+VR; The Kayfabe Life
- » Efficient Query Processing Using Machine Learning
- » How to use mathcha math online editor
- » Uncertainty estimation in supervised learning
- » Approximately Bayesian Ensembling
- » Efficient Data Processing on Modern Hardware
- » AI Bias and Fairness
- » Generative Models for Graphs
- » Evidential Deep Learning and Uncertainty
- » Learned Approximate Query Processing
- » Meta Learning Tutorial
- » Deep Learning Outline
- » Transfer Learning
- » Approximate nearest neighbor search in high dimensions
- » Learning-Based Sketching Algorithms
- » Deep Learning Under Distribution Shift
- » Principles for Tackling Distribution Shift
- » Scalable Graph Neural Networks with Deep Graph Library
- » Deep InfoMax
- » Introduction to Bandits in Recommender Systems
- » Our paper has been published in IEEE Access!
- » Constant Girth Approximation for Directed Graphs in Subquadratic Time
- » Conditionally optimal approximation algorithms
- » Sparsification of Graphs and Matrices
- » Introduction to Graph Thinking
- » Reinforcement Learning Course - Georgia Tech
- » Digital Transformation in a Connected World
- » What is Graph Analytics?
- » Graphs in Machine Learning
- » Social and Information Network Analysis
- » Structural Analysis and Visualization of Networks
- » MapGraph
- » Organizational Charts
- » Large Graph-Mining - Power Tools and a Practitioner's Guide
- » Stanford Network Analysis Platform(SNAP)
- » Community Detection - Clustering Coefficient
- » Predictive Methodologies - Analysis of Time and Space
- » Predictive Methodologies - Analysis of Space
- » Predictive Methodologies - Analysis of Social Networks
- » Theoretical Foundations of Data Mining and Predictive Analytics
- » Data Used in Predictive Policing
- » Operational Challenges of Predictive Policing
- » Forecast Vs. Prediction
- » Recommendations
- » Predictive Policing Myths and Pitfalls
- » Prediction-Led Policing Process and Prevention Methods
- » Predictive Policing
- » A Taxonomy of Predictive Methods
- » “Big Data” Promises a Revolution
- » Principal Component Analysis
- » Applications using graph representation

- » Connected Components in MapReduce and Beyond
- » Michael Stonebraker's Talk
- » Running Hadoop MapReduce on Tachyon
- » What is the Future of Hadoop?
- » Hadoop + GPU Architecture
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » IBM Juices Hadoop With Java On Tesla GPUs
- » Apache Hadoop vs. IBM Platform Symphony and Infosphere BigInsights
- » How HPC is Hacking Hadoop
- » High-Performance Computing Cluster
- » Hadoop-RDMA
- » MapReduce for C - Run Native Code in Hadoop
- » 5 Ways to Make Your Hive Queries Run Faster
- » HIVE 0.14 Cost Based Optimizer (CBO) Technical Overview
- » DryadLINQ
- » Dryad
- » Join Optimization
- » Map-Side Join Vs. Join
- » What is map side join and reduce side join?
- » Hive Architecture
- » Translate from MapReduce to Apache Spark
- » An Insight into Hadoop YARN NodeManager
- » Hadoop-GIS Spatial Queries
- » Running a cluster of virtual machines with Hadoop
- » Use Parquet with Impala, Hive, Pig, and MapReduce
- » Actian Unleashes SQL Users on Hadoop with Easy Access, Faster Performance
- » Presto as a Service
- » HIVE as a Service
- » Understanding the world of SQL on Hadoop.
- » How to Develop Hadoop Tests
- » MRUnit Tutorial
- » HIVE TPC-DS Benchmark
- » Distributed Computing with Spark
- » Why Oracle Big Data SQL Potentially Solves a Big Issue with Hadoop Security
- » In-Stream Big Data Processing
- » Apache Hadoop YARN ResourceManager
- » Introduction To Pivotal's Next Generation Analytic Data Lake
- » War of the Hadoop SQL engines. And the winner is …?
- » Exploring Pivotal HD Single Node (VM)
- » How Pivotal Works
- » Parquet - Columnar Storage for the People
- » Pivotal Big Data Suite
- » Pivotal HD Introduction & Demo
- » Special "Use Cases" Big Data & Brews - Hortonworks, Pivotal, MapR
- » What is Apache Drill?
- » Drilling into Big Data with Apache Drill
- » Fun with TPC-H (Part 1)
- » Implementing Drill Math Functions
- » Presto By Facebook – A SQL Engine
- » Big Data - The Hadoop Data Warehouse
- » Building a Data Warehouse with Hadoop
- » Why does MapReduce + GPU Computing?
- » Apache Spark Documentation
- » Google Cloud Dataflow, a Cloud-native data processing service
- » Selecting the right SQL-on-Hadoop engine to access big data
- » Big Data Benchmark
- » Apache Spark
- » Optiq
- » Adding Acid Transactions in Hive
- » Yarn The Key to Overcoming the Challenges of Broad based Hadoop Adoption
- » Challenges of Implementing an Advanced SQL Engine on Hadoop
- » SparkR Enabling Interactive Data Science at Scale on Hadoop
- » Hbase Read High Availability Using Eventually Consistent Region Replicas
- » How Hadoop and Hive Change the Data Warehousing Game Forever
- » Big Data Management System Smart SQL Processing
- » Query Optimization Techniques and JIT based Vectorized Engine
- » Pig on Tez - Low Latency ETL with Big Data
- » Cost Based Query Optimization in Hive
- » Low Latency SQL on Hadoop
- » Building a Unified Data Pipeline in Apache Spark
- » Apache Storm Basics
- » M7 - Native Storage for MapR Tables
- » Hadoop Sector will Have Annual Growth of 58% for 2013-2020
- » Why Impala Continues to Lead?
- » Development Tools for Big Data
- » Apache Tajo JDBC Driver
- » Apache Tajo HCatalog Integration
- » Spark for Data Science; A Case Study
- » Apache Tajo Backup and Restore
- » Apache Tajo Table Partitioning
- » An Insight into Hadoop Yarn Resource Manager
- » Apache Tajo SequenceFile
- » Apache Tajo Parquet
- » How Apache Hadoop YARN HA Works
- » Apache Tajo RCFile
- » Apache Tajo Table Management
- » Apache Tajo CSV (TextFile)
- » Papers to Read
- » Apache Tajo Functions
- » Apache Tajo SQL Language
- » Apache Tajo Shell
- » Apache Tajo™ 0.8.0 Configuration
- » Apache Tajo™ 0.8.0 Released!
- » Apache Tajo™ 0.8.0 Documentation
- » The Stinger Initiative From HortonWorks With Hadoop
- » Bigtable
- » Chubby
- » Big Data & Brews - Ari Zilka, CTO of Hortonworks' Talk
- » The Google File System (GFS)
- » Stinger Intiative - Making Apache Hive 100 Times Faster
- » Stinger - Interactive Query for Hive
- » Overview of Data Storage and Coordination Services
- » Underlying Communication Paradigms
- » Overall Architecture and Design Philosophy
- » Comparing Apache Tez and Microsoft Dryad
- » Apache Spark and Apache Tez
- » Introducing the case study - Google
- » Integration Tajo with Hive
- » Choosing the Right SQL-on-Hadoop
- » How to Run a Simple Apache Spark App in CDH 5
- » NewSQL Vs. SQL on Hadoop
- » Presto as a Service
- » Apache Hadoop YARN – Related Work
- » Adding ACID to Apache Hive
- » Eight SQL on Hadoop Challenges
- » 11 SQL-on-Hadoop Tools
- » Healthcare Does Hadoop
- » Processing streaming data in Hadoop with Apache Storm
- » Interactive Query for Hadoop with Apache Hive on Apache Tez
- » Introducing Apache Tez 0.4
- » Big Data Genomics Sequencing
- » Failures in YARN
- » Failures in Classic MapReduce
- » YARN Framework and Fault Tolerance
- » Apache Hadoop YARN – Node Manager (NM)
- » Hardening Hadoop for Healthcare with Project Rhino
- » Apache Tajo
- » Build Instructions for Apache Tajo
- » Apache Hadoop YARN – Application Master (AM)
- » Apache Hadoop in 2013 - The State of the Platform
- » Apache Hadoop YARN – Architecture Overview
- » Apache Hadoop YARN – Resource Manager (RM)
- » Apache Hadoop YARN – History and Rationale, Shared Clusters
- » Network Topology and Hadoop
- » Modern Oil & Gas Architectures Built with Hadoop
- » Using Avro in MapReduce Jobs with Pig
- » Using Avro in MapReduce Jobs with Java
- » Using Avro in MapReduce Jobs with Hive
- » Using Avro in MapReduce Jobs with Hadoop Streaming
- » MapReduce and Hadoop Papers in the VLDB
- » MapReduce and Hadoop Algorithmic in Academic Papers
- » Hive on Tez
- » Functional Requirements of Hive on Tez Phase I
- » Query Planning of Hive on Tez
- » Design of Hive on Tez
- » Apache Tez 0.3 Released!
- » Apache Drill
- » Apache Hadoop YARN – History and Rationale
- » As MapReduce fades, Apache Spark is now a top-level project
- » Apache Hadoop YARN – ResourceManager
- » Apache Hadoop YARN – Yet Another Resource Negotiator
- » Apache Hadoop YARN – NodeManager
- » Apache Hadoop YARN – Concepts and Applications
- » Apache Hadoop YARN – Background and Overview
- » Papers about MapReduce
- » Introducing Apache Hadoop YARN
- » Tez Design - Introduction
- » Building a Hybrid Modern Data Architecture for Apache Hadoop
- » Writing a Tez Input, Processor and Output
- » Runtime API in Apache Tez
- » Re-Using Containers in Apache Tez
- » Introducing Tez Sessions
- » Data Processing API in Apache Tez
- » Apache Tez Dynamic Graph Reconfiguration
- » Apache Tez A New Chapter in Hadoop Data Processing
- » Introduction to the Tez
- » YARN (MapReduce 2)

- » A Better B-Tree in Hekaton
- » The Bw-Tree; A B-tree for New Hardware
- » Hekaton Research
- » Distributed Transactions
- » ACID Isolation Level
- » Notes About Memory Allocation Redesign
- » Postgres Modifications
- » The Databaseology Lectures
- » C++ Arena Allocation Guide
- » Region-based Memory Management
- » What is VoltDB?
- » Peloton Architecture
- » Insert-Only Implementation Strategies
- » Understanding the SQL Server Columnstore Index
- » Hekaton Breaks Through
- » Compensating Transaction
- » In-Memory Column Store Architecture Overview
- » Advantages & Disadvantages of an In-Memory Database
- » Main Memory DBMS
- » HYBRID WORKLOADS AND HTAP
- » Peloton
- » MVCC Overview
- » SAP HANA
- » HYRISE
- » Main Compensation and Delta Compensation
- » Aggregate Cache
- » MonetDB Source compilation on OS X
- » What is HTAP?
- » Using HyPer with PostgreSQL Drivers
- » Hyper
- » From Big Data to Fast Data
- » SAP Hana In-Memory Explained In Nine Minutes
- » Business unIntelligence
- » Sharing Experiences in Cloud Adoption
- » IBM Hybrid Transaction Analytic Processing HTAP
- » CIOs and the Ongoing Data Persistence Struggle
- » Hybrid Trasansaction/Analytical Processing (HTAP)
- » Hybrid Trasansactional/Analytical Processing (HTAP)
- » HTAP and The Future of Data

- » Activate Your Modern MetaData Stack
- » Databricks Lakehouse makes payments ingestion and analytics simple
- » The Essential Guide to Data Lineage
- » Data Governance Explained in 5 Minutes
- » Automated Data Lineage with Unity Catalog
- » Active Metadata - Understanding the magic behind the Data Fabric
- » Introduction to Data Mesh
- » PostgreSQL vs MySQL
- » Activate Your Metadata to Empower Innovation
- » Accelerating Hybrid Data Mesh Implementation
- » Deep-Dive into Delta Lake
- » Delta Lake 2.0 Overview
- » Advancing Spark - The Photon Whitepaper
- » Column-Level Lineage and Active Metadata
- » Five Things to Consider About Data Mesh and Data Governance
- » Data Mesh Implementation Patterns
- » Intro to Databricks Lakehouse Platform Architecture and Security
- » Meshing About with Databricks
- » Enterprise Data Fabric
- » Converging Advances to Accelerate Molecular Simulation
- » Learning Delaunay Surface Elements for Mesh Reconstruction
- » Data Fabric for Self-Driving Cars
- » Knowledge Graphs Seminar
- » Data Cataloging with Knowledge Graphs
- » The Foundation of a Data Fabric
- » Data Mesh, Data Fabric, Data Lakehouse
- » Tractable Probabilistic Circuits
- » Distributed Analytical Database Systems
- » GraphZeppelin - Streaming Graph Connectivity at Scale
- » Understanding Graph Data Representations in Triplestores
- » Graph-Powered Data Exploration
- » Google BigQuery
- » Conformal Prediction Tutorial
- » Traffic Prediction Paper Collection
- » End-to-end Optimization of Machine Learning Prediction Queries
- » Compute Complex Temporal Join Queries Efficiently
- » Making Learned Query Optimization Practical
- » Universal Database Optimizer
- » Machine Learning for Query Optimization
- » ML Explainability
- » Towards Generalizable Autonomy
- » Building an AI and ML ready Modern Data Platform
- » Diffusion Probabilistic Modelling of Protein Backbones in 3D
- » ICCV Authors Guidelines
- » Diffusion Probabilistic Models
- » Stable Diffusion - What, Why, How?
- » Deep Energy-Based Learning
- » Incident Prediction in Spatio-Temporal Road Graph Networks
- » Diffusion probabilistic modelling of protein backbones in 3D
- » Contrastive Learning in PyTorch
- » Neural Rendering
- » Anatomy of a Research Paper
- » Geometric Deep Learning for Drug Discovery
- » Deploy Custom Python Model Server
- » Contrastive Learning in PyTorch
- » InferenceService using a Custom Torchserve Image
- » From Machine Learning to Autonomous Intelligence
- » Towards a Learned Index Structure for ANN Search
- » Graph Processing; from Academic Research to Industrial Application
- » Katana Graph; A Graph Intelligence Platform
- » Machine Learning for Scientific Discovery
- » Graph Neural Networks as Gradient Flows
- » Ab-Initio Potential Energy Surfaces
- » Reinforcement Learning via Sequence Modeling
- » Recipe for a General, Powerful, Scalable Graph Transformer
- » Spatial Hash Grids & Tales from Game Development
- » Cardinality Estimation Benchmark
- » Spatial Indexing in PostGIS
- » Data-driven Learned Metric Index; an Unsupervised Approach
- » Synopses - Samples, Histogram, Wavelets and Sketches
- » Query Execution in MonetDB
- » Low overhead self-optimizing storage for compression
- » A Database Tuning Tool that "Reads the Manual"
- » Rethinking Graph Transformers with Spectral Attention
- » Introduction to TorchServe
- » MicroK8s Installation & Simple Commands
- » Diffusion Models from Scratch in PyTorch
- » Deep Learning Theory Lectures
- » Kubeflow Fairing
- » Kubeflow Fairing, Pipelines, and Training
- » On Recoverability of Graph Neural Network Representation
- » Deploy Model to KServe
- » TorchScript and PyTorch JIT
- » Spectra of Graphs and Hypergraphs
- » How to Serve PyTorch Models with TorchServe
- » A Novel Data Set for Information Retrieval on the Basis of Subgraph Matching
- » Graph Embedding in Vector Spaces Using Matching-Graphs
- » Google Cloud BigQuery ML Using SQL
- » Open World Lifelong Learning
- » On Machines that can Learn Continually
- » Introduction to Continual Learning
- » Introducing OpenAPI Generator
- » Kubeflow Development Environment
- » KFServing Deep Dive
- » Protein structure prediction with AlphaFold
- » Deep Learning for Scientific Computation
- » Started with AI in Drug Discovery
- » Istio Simplified
- » Knative and Microk8s with Multipass
- » KServe in Kubenetes
- » Graph Ordering Attention Networks
- » Introduction to KServe
- » Position-Aware Graph Neural Network using Reachability Estimations
- » Isomorphism Using Adjacency Matrix
- » Efficient Probabilistic Truss Indexing on Uncertain Graphs
- » Toward Verified Artificial Intelligence
- » Modern Data Stack
- » PyTorchJobClient
- » KServe (Kubeflow KFServing) Live Coding Session
- » Workflow for ML Projects — MLOps
- » Kubeflow Setup
- » Introduction to Data Mesh
- » Theory of Graph Neural Networks
- » Representational Power of Graph Nerual Networks
- » Task structure and generalization in graph neural networks
- » Geometry Processing with Neural Fields
- » Trends in AI
- » RelationalAI Knowledge Graph Management System
- » Topological Graph Neural Networks
- » Causality and (Graph) Neural Networks
- » Using Graph Neural Networks for Multi-Node Representation Learning
- » Epistemic Uncertainty Estimation for Efficient Search of Drug Candidates
- » Toward Neuro Causality
- » Connect to MySQL Database from Visual Studio Code
- » Data-Efficient Graph Grammar Learning for Molecular Generation
- » Rethinking Graph Transformers with Spectral Attention
- » How GNNs and Symmetries can help to solve PDEs
- » Neural diffusion PDEs, differential geometry, and graph neural networks
- » BigQuery ML; Machine Learning with Standard SQL
- » GNN with Learnable Structural and Positional Presentations
- » Istio Service Mesh 101
- » Graph Neural Networks and Diffusion PDEs
- » Recommender Systems using Graph Neural Networks
- » Manage Multi-tenant ML Workloads Using Istio
- » Graph Neural Networks as Neural Diffusion PDEs
- » Exploring ML Model Serving with KServe
- » Kubeflow Tutorial | Model Serving
- » Using AI to accelerate scientific discovery
- » Java to MySQL using Visual Studio Code
- » Developing the Kubernetes Python Client
- » Kubernetes Patterns
- » Using Visual Studio Code for Java Maven Project
- » Mapping External Services
- » MiniKF; Kubeflow on your laptop
- » Gateway Routing Pattern
- » Kubeflow v1.5 Release Overview
- » TensorFlow Distributed Training on Kubeflow
- » Sidecar Pattern
- » YAML
- » Kubeflow on macOS
- » Why is Kubernetes called K8s?
- » Kubernetes Crash Course for Absolute Beginners
- » Marrying Top k with Skyline Queries
- » Spatial Skyline Queries on Triangulated Irregular Networks
- » Challenges and Solutions of Using Kubernetes for Blockchain Applications
- » PyTorch Training (PyTorchJob)
- » Kubeflow Architecture
- » Istio and Service Mesh
- » Kubernetes Tutorial for Beginners
- » Serving Machine Learning Models at Scale Using KServe
- » Advanced Model Inferencing Leveraging Kubeflow Serving
- » PyTorch via SQL Commands
- » Scaling Shortest Path Graph Queries on Very Large Networks
- » Hierarchical Core Maintenance on Large Dynamic Graphs
- » Shortest Paths and Centrality in Uncertain Networks
- » Bag of Tricks for Node Classification with Graph Neural Networks
- » The Case for Learned Index Structures
- » Deep Learning for Graph Similarity Search
- » HyperSPNs; Compact and Expressive Probabilistic Circuits
- » Learned Index Structures for Dynamic and Multi-Dimensional Data
- » Blame the Data, Not the System
- » Hyperbolic Embeddings in Machine Learning and Deep Learning
- » What are Temporal Databases?
- » Git Commit Message Style Guide
- » What is PrestoDB?
- » Spatial, High Dimensional, Temporal Data Indexing and Querying
- » A Learned Spatial Index for Range and kNN Queries
- » An Introduction to Spatial Data and its Applications
- » Robustness/Interpretability in Vision & Language Models
- » Visual Data Analysis; How? When? Why?
- » SageDB; A Self-Assembling Database System
- » Towards Zero-Shot Learning for Databases
- » From Workload-Driven to Zero-Shot Learning
- » From Notebook to Kubeflow Pipelines to KFServing
- » Improving Transfer and Robustness of Supervised Contrastive Learning
- » Approximate Query Processing
- » What are Probabilistic Data Structures?
- » Approximation Algorithms for Large Scale Data Analysis
- » Modeling Relational Data with Graph Convolution Network
- » Open-source Change Data Capture With Debezium
- » Finding Approximately Repeated Patterns in Time Series
- » Looper; An End-to-End ML Platform for Product Decisions
- » Learned Indexing and Sampling for Improving Query Performance
- » Principles of Good Machine Learning Systems Design
- » Deep Learning Design Patterns
- » Tips for Thinking Like a Machine Learning Architect
- » Machine Learning Design Patterns
- » Introduction to Kubeflow
- » Martingale Theory
- » Making Architecture Matter
- » Bayesian Inference for Big Data
- » Architecture and Cardinality Estimations for Graph Queries
- » What causes congestion?
- » Optimization problems in graphs with locational uncertainty
- » Deep Implicit Layers
- » Convex Functions
- » Quantum Deep Learning
- » Uncertainty and Out-of-Distribution Robustness in Deep Learning
- » Probabilistic Methods for Increased Robustness in Machine Learning
- » Probabilistic Circuits - Representations, Inference, Learning and Theory
- » FLAT-Fast, Lightweight and Accurate Method for Cardinality Estimation
- » Learned Query Scheduling
- » A 10 Minute Introduction to Kubeflow
- » Ethics of Artificial Intelligence
- » What is AI Ethics?
- » What is the Cardinality Estimator?
- » Qd-tree - Learning Data Layouts for Big Data Analytics
- » Sum-Product Networks
- » Towards Zero-Shot Learning for Databases
- » Machine Learning Design Patterns
- » Learned DBMS Components 2.0
- » Deep Multi-task and Meta Learning
- » Machine Learning Design Patterns - Rebalancing
- » Calcite Tutorial
- » NeuroCard - One Cardinality Estimator for All Tables
- » GNN based Recommender Systems
- » On the Nature of Data Science
- » Breaking the Limit of Graph Neural Networks using Local Mixing Patterns
- » Performance Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks
- » Data Fabrics for Enterprise Data Management?
- » Query Optimization and Acceleration at Dremio
- » AI4DB & DB4AI-Papers
- » Permissioned Blockchains
- » Apache Spark, Data Science + Machine Learning
- » Introduction to Temporal Graph Neural Networks
- » Traffic Forecasting with Pytorch Geometric Temporal
- » Geometric Deep Learning
- » What Is NFT? - Non Fungible Token
- » Deep Recommender Systems
- » Weisfeiler and Lehman Go Cellular; CW Networks
- » Matrix Multiplication, and the Asymptotic Spectrum Of Tensors
- » Traffic Prediction for Intelligent Transport System
- » Revisiting Knowledge Graph Completion From a Practical Perspective
- » Applying Machine Learning-based Database Tuning in Production
- » Cloud-Native Ledger Graph Database
- » A Probabilistic Model for Joint Inference from Differential Equations
- » Optimization Frameworks for Graph Clustering
- » Neuralising a Computer Scientist
- » Preparing For Your Dissertation Defense
- » Oral Dissertation Defense Preparation
- » How To Defend Your Thesis? Top 10 Tips For Success
- » How to Get a PhD in Computer Science
- » 100 Thesis Defense Questions in 3 Categories
- » 10 Mistakes to Avoid When Defending Your Thesis
- » How to prepare for your PhD thesis defence
- » Geometric Deep Learning-Past, Present, And Future Optimization
- » Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
- » How to write a literature review - my simple 5 step process!
- » Geometric Deep Learning - from Euclid to drug design
- » Graph Isomorphism in Quasipolynomial Time
- » Geometric Deep Learning Blueprint
- » Geometric Deep Learning-The Erlangen Programme of ML
- » KDD Cup OGB Large-Scale Challenge
- » Theoretical Foundations of Data-Driven Algorithm Design
- » Relational Inductive Biases, Deep Learning, and Graph Networks
- » The Future Is Big Graphs
- » The Universal Approximation Theorem
- » Equivariant Networks and Natural Graph Networks
- » An Introduction to Hamiltonian Monte Carlo Method for Sampling
- » Recurrent Neural Networks for Cognitive Neuroscience
- » Permutation Equivariance of Graph Filters
- » Graph Convolutional and Isomorphism Networks
- » Combinatorial Properties of the Weisfeiler-Leman Algorithm
- » Advances in Self-Supervised Learning
- » Contrastive Loss, Deep Metric Learning
- » Self-Supervised Learning
- » Contrastive Learning; A General Self-supervised Learning Approach
- » Supervised Contrastive Learning
- » What is Noise-Contrastive Estimation?
- » GNNs with Learnable Structural and Positional Representation
- » Knowledge Graph Representation
- » Temporal Graph Networks (TGN)
- » Building a Recommender System using Graph Neural Networks
- » Junction Tree Variational Autoencoder for Molecular Graph Generation
- » Understanding Graph Attention Networks
- » GVAE Training and Adjacency Reconstruction
- » Graph Representation Learning for Drug Discovery
- » Graph Neural Networks for Traffic Prediction
- » Visual Exploration of Trajectory Data
- » GPS Data Analysis with Python
- » 22 Python libraries for Geospatial Data Analysis
- » Clustering Trajectories
- » Towards Causal Representation Learning
- » Random Sum Product Networks
- » Understanding & Generating Source Code with Graph Neural Networks
- » Sum-Product Networks; The Next Generation of Deep Models
- » Deep Learning to Discover Coordinates for Dynamics
- » The Power of Graph Signal Processing
- » Some Mathematical Problems in Graph Signal Processing
- » On Laplacian Eigenmaps for Dimensionality Reduction
- » Overview of Graph Embeddings
- » Sum-Product Networks; Powerful Models with Tractable Inference
- » Sum-Product Networks
- » Geometric Deep Learning and Reinforcement Learning
- » Discovering Genetic Medicines with our AI Drug Discovery Platform
- » Proof of Euler's Formula
- » GNNs and RL in Traffic Optimization in Cities
- » Graph Neural Networks and Applications to Deep Reinforcement Learning
- » Writing Scientific Papers
- » Drug Discovery with GANs
- » PyTorch nn.Embedding()
- » Reproducing Kernel Hilbert Spaces
- » Dynamical Systems and Machine Learning
- » An Introduction to Hilbert Spaces
- » Graph Neural Networks in Computational Biology
- » What is Euler's Number 'e'?
- » Euler's Formula
- » The Characteristic Equation and Eigenvalues
- » Spanning Trees and Kirchhoff's Theorem
- » Acceptance Rate of AI Conferences
- » Deep Graph Learning Foundations, Advances and Applications
- » Learning Graphs from Data; A Signal Processing Perspective
- » Making Graphs Compact by Lossless Contraction
- » A Short Course in Spectral Graph Theory
- » An Introduction to Spectral Graph Theory
- » Spectral Graph Theory; Enter Linear Algebra
- » Spectral Graph Theory; The Standard Random Walk
- » Spectral Graph Theory; The Quadratic Form
- » The Laplacian Matrices of Graphs; Algorithms and Applications
- » Pragmatic Ridge Spectral Sparsification for Large-Scale Graph Learning
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- » Neo4j
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- » GridGain In-Memory Data Fabric
- » Advantages of Functional Programming
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- » Apache Hadoop vs. IBM Platform Symphony and Infosphere BigInsights
- » How HPC is Hacking Hadoop
- » High-Performance Computing Cluster
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- » Introduction to Docker
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- » Why Scala?
- » Introduction to Scala
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- » Why Functional Programming?
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- » Introduction to Transactional Memory
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- » Wide & Deep Learning; Memorization + Generalization with TensorFlow
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- » Understanding the world of SQL on Hadoop.
- » Apache Tez Presentation
- » Announcing Apache Pig 0.13.0
- » Benchmarking Apache Hive 13 for Enterprise Hadoop
- » The Stinger Initiative From HortonWorks With Hadoop
- » Big Data & Brews - Ari Zilka, CTO of Hortonworks' Talk
- » Comparing Apache Tez and Microsoft Dryad
- » Apache Spark and Apache Tez
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- » Interactive Query for Hadoop with Apache Hive on Apache Tez
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- » Apache Tez A New Chapter in Hadoop Data Processing
- » Introduction to the Tez

- » Min-cost Flow Network Algorithms
- » Elasticity with YARN
- » Broadcast Join in Tajo
- » Tajo Cluster Architecture
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- » What is Mesos?
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- » Build YARN Apps on Hadoop with Apache Slider
- » Apache Hadoop YARN Architecture
- » Apache Spark Resource Management and YARN App Models
- » Development Tools for Big Data
- » Apache Tajo JDBC Driver
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- » Spark for Data Science; A Case Study
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- » An Insight into Hadoop Yarn Resource Manager
- » Apache Tajo SequenceFile
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- » Apache Tajo RCFile
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- » Apache Tajo™ 0.8.0 Configuration
- » Apache Tajo™ 0.8.0 Released!
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- » The Stinger Initiative From HortonWorks With Hadoop
- » Simple YARN Application
- » Apache Hadoop YARN – Related Work
- » Failures in YARN
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- » YARN Framework and Fault Tolerance
- » Apache Hadoop YARN – Node Manager (NM)
- » Apache Hadoop YARN – Application Master (AM)
- » Apache Hadoop in 2013 - The State of the Platform
- » Apache Hadoop YARN – Architecture Overview
- » Apache Hadoop YARN – Resource Manager (RM)
- » Apache Hadoop YARN – History and Rationale, Shared Clusters
- » Apache Hadoop YARN – History and Rationale
- » Apache Hadoop YARN – ResourceManager
- » Apache Hadoop YARN – Yet Another Resource Negotiator
- » Apache Hadoop YARN – NodeManager
- » Apache Hadoop YARN – Concepts and Applications
- » Apache Hadoop YARN – Background and Overview
- » Introducing Apache Hadoop YARN
- » YARN (MapReduce 2)

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Sung-Soo Kim

Principal Research Scientist

sungsoo@etri.re.kr