Sungsoo Kim's Blog

- 4 June 2024 » Python RAG Tutorial with Local LLMs
- 3 June 2024 » Learn RAG From Scratch
- 2 June 2024 » PostgreSQL as a Vector Database
- 1 June 2024 » Kernels for Graphs
- 31 May 2024 » Milvus 101- Most Advanced Vector Database
- 30 May 2024 » Vector Similarity Search & Indexing Methods
- 29 May 2024 » Combine Your Data with LLMs with Advanced Search
- 28 May 2024 » Learn How to Build Multimodal Search and RAG
- 27 May 2024 » Many shades of Machine Learning Acceleration
- 26 May 2024 » Charm++ and Adaptive MPI
- 25 May 2024 » GenAI in Life Sciences Summit
- 24 May 2024 » Grounding for Gemini with Vertex AI Search and DIY RAG
- 23 May 2024 » HPC and AI/ML; A Synergistic Relationship
- 22 May 2024 » Multimodal Retrieval-Augmented Generation with Gemini

## Sungsoo 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)

[52] **Sungsoo Kim**, Choon Seo Park, Taewhi Lee, Kihyuk Nam, Constrained Approximate Query Processing with Error and Response Time-Bound Guarantees for Efficient Big Data Analytics, *ACM HPDC 2024 (Poster)*, 2024. 06. (To appear)

[51] **Sungsoo Kim**, Moonyoung Chung, Fast Graph Learning for Traffic Prediction, *IEEE BigData 2023 (Poster)*, pp.6186-6188, 2023. 12.

[50] Seunghoon Han, **Sungsoo Kim**, Sungsu Lim, Improved Dynamic Coupled Graph Convolutional Recurrent Networks for Traffic Forecasting, *IEEE BigData 2023 (Poster)*, pp.6159-6161, 2023. 12.

[49] Jeongseon Kim, **Sungsoo Kim**, Sungsu Lim, Performance Evaluation of Data Imputation Methods for Graph Deep Learning-Based Traffic Prediction, *IEEE BigData 2023 (Poster)*, pp.6192-6194, 2023. 12.

[48] Kihyuk Nam, Taewhi Lee, **Sungsoo Kim**, Insik Shin, Learning for Spatio-temporal and Relational Data, *IEEE BigData 2023 (Poster)*, pp.6239-6241, 2023. 12.

[47] Kihyuk Nam, **Sung-Soo Kim**, Choon Seo Park, Taek Yong Nam, Taewhi Lee, A Framework for Learned Approximate Query Processing for Tabular Data with Trajectory, *The 14th International Conference on ICT Convergence (ICTC) 2023*, pp.1122-1124, 2023. 10.

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

[52] 김성수, 이태휘, 최병준, 이용진, 남기혁, 데이터 분석의 진화: 보다 빠르고 스마트한 의사결정을 위한 액티브 메타데이터 기반 빠른 그래프 학습 접근법, 2024년도 한국인터넷정보학회 춘계학술발표대회 논문집 제25권 1호, pp.153-154, 2024. **[우수논문상 수상]**

[51] 한승훈, 김정선, 이혜원, 김성수, 임성수, 그래프 딥러닝 기반 교통 예측을 위한 결측치 대체 방법 성능 비교, 2023년 한국소프트웨어종합학술대회 논문집, pp.119-121, 2023.

[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
^{358} - computer graphics
^{71} - computer networks
^{16} - data curation
^{13} - data management
^{287} - data science
^{39} - design patterns
^{14} - developments
^{78} - foundation models
^{1} - gpgpu
^{23} - graph mining
^{501} - hadoop & mapreduce
^{168} - htap
^{39} - machine learning
^{1220} - 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
- » Burndown Chart
- » Wiki Tutorial
- » Adapting to Change
- » Agile Big Data
- » TDD Best practices
- » Test-Driven Development in a nutshell
- » Scrum Events
- » Effective Team Membership
- » 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
- » Mesoscopic Simulation
- » Distributed Systems
- » High-Performance Mesoscopic Traffic Simulation
- » Microscopic Simulation
- » Macroscopic Simulation
- » Traffic Simulation Background
- » T tree
- » Cellular Automata
- » Graph Partitioning
- » Computability, Complexity, and Theory - Georgia Tech
- » PageRank Algorithm
- » PageRank History
- » Bloom Filter
- » Confluent and Functional Persistence
- » Full Persistence
- » Partial Persistence
- » Persistent Data Structures

- » Graphs in Machine Learning
- » Social and Information Network Analysis
- » 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
- » 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
- » 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
- » Solving the Wanamaker Problem for Healthcare
- » Parallel R
- » Real-time Operational Intelligence
- » Analytics vs. Predictive Analytics
- » The evolution of analytics

- » What's the difference between programming and coding
- » Prompt Engineering Overview
- » Cross-Platform Data Lineage with OpenLineage
- » Continuous Training and Deployment with ZenML and Seldon
- » CVPR Highlights 2023
- » Robust and Trustworthy Deep Learning
- » PFNs; Use neural networks for 100x faster Bayesian predictions
- » Multimodal Deep Learning for Protein Engineering
- » Topological Deep Learning; Going Beyond Graph Data
- » Is Distance Matrix Enough for Geometric Deep Learning?
- » Sign and Basis Invariant Networks for Spectral Graph Representation Learning
- » Distributed and Multiagent Reinforcement Learning
- » A Distributional Multi-agent Reinforcement Learning Approach
- » Deploy and Scale Models with BentoML
- » Tool Agnostic MLOps with ZenML
- » The Four Pillars of Machine Learning
- » When to use Kubernetes natively over Kubeflow for ML
- » Deploy ZenML + Kubeflow + MLflow + Minio
- » MLOps Pipeline Tutorial with ZenML and Kubeflow
- » ML models with ZenML and BentoML
- » Recent Advances in Vision Foundation Models
- » CVPR 2023 Tutorials
- » Model Registry and Deployment with MLflow
- » How to Deploy ML Models in Production with BentoML
- » Data Science–A Systematic Treatment
- » LEARN ENGLISH with STEVE JOBS
- » DuckDB Internals
- » How to Master the Art of Leadership
- » Getting Started with ZenML
- » Generative Models for Language and Vision
- » ChatGPT, LLMs & Generative AI; What Your Business Needs to Know
- » Differential Privacy & Variants
- » Towards Learned Database Systems
- » Deep Lake; a Lakehouse for Deep Learning
- » Towards Natural Language Query Answering
- » Do We Still Need People To Write Database Systems?
- » Generative AI and Databases
- » Contrastive Learning in PyTorch
- » Self-Supervised Learning; Self-Prediction and Contrastive Learning
- » Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
- » Bayesian Neural Network
- » Study Less Study Smart
- » Kubeflow v1.7 Release Overview
- » Amazon Redshift Internals
- » ChatGPT Course – Use The OpenAI API to Code 5 Projects
- » Installing and Using NVIDIA Docker
- » Hypertree Decompositions
- » What is Platform Engineering?
- » Databricks Spark SQL / Photon
- » Google BigQuery / Dremel
- » Personalized Image Generation
- » Kubeflow Pipelines 2.0
- » Learning to Transfer Knowledge Through Embedding Spaces
- » Traffic Prediction with Transfer Learning
- » First-Passage Percolation and Related Models
- » Sparse Fourier Transform Algorithm for Real-Time Applications
- » The Frontier of Deep Learning for Robotics
- » Microsoft's Products Will Soon Access Open AI Tools Like ChatGPT
- » LLaMA & Alpaca; "ChatGPT" On Your Local Computer
- » Multi-Objective Recommender Systems
- » Hands-on Explainable Recommender Systems with Knowledge Graphs
- » Improving Recommender Systems with Human in the Loop
- » Open-Source Systems for Federated Learning
- » An Introduction to Federated Computation
- » Data Governance and Sharing on Lakehouse
- » Data Mesh and Lakehouse
- » Master Data Management
- » Setting up Big Data Fabric
- » On Future of the Modern Data Stack
- » Data Virtualization in Data Fabric
- » Recommender Systems using Graph Neural Networks
- » 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
- » Big Data and Smart Data
- » 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
- » Connected Components in MapReduce and Beyond
- » Analytics in Motion (AIM)
- » Big Data is (at least) Four Different Problems
- » Real-time Big Data Analytics
- » HiBench Suite
- » Map-D
- » MultiQx-GPU
- » Concurrent Analytical Query Processing with GPUs
- » Where are the GPU based SQL databases?
- » Comparing Relational Databases, Memory Cache, and NoSQL
- » Oracle In-Memory Database Cache Overview
- » MapGraph
- » Introducing TPCx-HS Benchmark for Big Data
- » Big Data, Fast Data The Need for In Memory Database Technology
- » Arrary DBMS
- » Arrary Databases
- » Michael Stonebraker's Talk
- » Sample Data
- » Building Tachyon Master Branch
- » Creating Applications using Spark Streaming
- » Creating a Spark Applications
- » Working with RDD Operations
- » RAMCloud Presentations
- » Teaching creative computer science
- » RAMCloud Training
- » Kudu - New Apache Hadoop Storage for Fast Analytics on Fast Data
- » GridGain In-Memory Data Fabric
- » Discardable Memory and Materialized Queries
- » 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
- » Text Mining and Knowledge Graphs in the Cloud
- » Knowledge Graph and Cognitive Computing
- » Using Presto to combine data from Hive and MySQL
- » IBM Watson at Work
- » Using HyPer with PostgreSQL Drivers
- » Knowledge Graph
- » Big Data and Machine Learning - Keys to AI Progress
- » Distributed Computing Conferences
- » Building Good Docker Images
- » Cognitive Computing
- » 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
- » 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
- » Future of big data analytics
- » Five most popular similarity measures implementation in python
- » Overview of the Apache Flink
- » Apache Flink Seminar
- » MapD (Massively Parallel Database) Overview
- » Data Science using GPUs
- » Apache Spark - Caching and Checkpointing Under the Hood
- » Tensor Decomposition
- » Higher-order Singular Value Decomposition
- » Smoothed Analysis of Tensor Decompositions
- » Spark for Unconventional Cores
- » Aparapi for Unconventional Cores
- » Apache Phoenix
- » Immutability
- » Parallelism
- » Column Orientation
- » Composing and Scaling Data Platforms
- » Elements of Scale
- » What is Mesos?
- » Data Governance Initiative
- » 39 Data Visualization Tools for Big Data
- » Twitter Scalding
- » Apache Zeppelin
- » Transaction Processing on Confidential Data using Cipherbase
- » Introduction to the Spark
- » Big Spatial Data Processing using Spark
- » SpatialSpark
- » New Directions for Spark in 2015
- » The Spark SQL Optimizer and External Data Sources API
- » DataFrame and SQL Operations
- » Design Patterns for using foreachRDD
- » Output Operations on DStreams
- » Transformations on DStreams
- » DStream Sources
- » Discretized Streams
- » Spark Streaming Basic Concepts
- » Spark Streaming Quick Example
- » Spark Streaming Overview
- » Useful Research Related to Apache Drill
- » Apache Spark
- » 10 ways to query Hadoop with SQL
- » Analytic Processing Environment (APE)
- » Analytics vs. OLTP
- » Layers of Query Processing
- » Database Workloads
- » Workload Types
- » Detailed Characteristics of an Analytic Workload
- » 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
- » HIVE as a Service
- » Understanding the world of SQL on Hadoop.
- » Serialization
- » HIVE TPC-DS Benchmark
- » Distributed Computing with Spark
- » 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

- » NVIDIA Keynote at SIGGRAPH 2023
- » 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

- » Awesome Vector Database
- » 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

- » What's the difference between programming and coding
- » Prompt Engineering Overview
- » Cross-Platform Data Lineage with OpenLineage
- » Continuous Training and Deployment with ZenML and Seldon
- » CVPR Highlights 2023
- » Robust and Trustworthy Deep Learning
- » PFNs; Use neural networks for 100x faster Bayesian predictions
- » Multimodal Deep Learning for Protein Engineering
- » Topological Deep Learning; Going Beyond Graph Data
- » Is Distance Matrix Enough for Geometric Deep Learning?
- » Sign and Basis Invariant Networks for Spectral Graph Representation Learning
- » Distributed and Multiagent Reinforcement Learning
- » A Distributional Multi-agent Reinforcement Learning Approach
- » Deploy and Scale Models with BentoML
- » Tool Agnostic MLOps with ZenML
- » The Four Pillars of Machine Learning
- » When to use Kubernetes natively over Kubeflow for ML
- » Deploy ZenML + Kubeflow + MLflow + Minio
- » MLOps Pipeline Tutorial with ZenML and Kubeflow
- » ML models with ZenML and BentoML
- » Recent Advances in Vision Foundation Models
- » CVPR 2023 Tutorials
- » Model Registry and Deployment with MLflow
- » How to Deploy ML Models in Production with BentoML
- » Data Science–A Systematic Treatment
- » LEARN ENGLISH with STEVE JOBS
- » DuckDB Internals
- » How to Master the Art of Leadership
- » Getting Started with ZenML
- » Generative Models for Language and Vision
- » ChatGPT, LLMs & Generative AI; What Your Business Needs to Know
- » Differential Privacy & Variants
- » Towards Learned Database Systems
- » Deep Lake; a Lakehouse for Deep Learning
- » Towards Natural Language Query Answering
- » Do We Still Need People To Write Database Systems?
- » Generative AI and Databases
- » Contrastive Learning in PyTorch
- » Self-Supervised Learning; Self-Prediction and Contrastive Learning
- » Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
- » Bayesian Neural Network
- » Study Less Study Smart
- » Kubeflow v1.7 Release Overview
- » Amazon Redshift Internals
- » ChatGPT Course – Use The OpenAI API to Code 5 Projects
- » Installing and Using NVIDIA Docker
- » Hypertree Decompositions
- » What is Platform Engineering?
- » Databricks Spark SQL / Photon
- » Google BigQuery / Dremel
- » Personalized Image Generation
- » Kubeflow Pipelines 2.0
- » Learning to Transfer Knowledge Through Embedding Spaces
- » Traffic Prediction with Transfer Learning
- » First-Passage Percolation and Related Models
- » Sparse Fourier Transform Algorithm for Real-Time Applications
- » The Frontier of Deep Learning for Robotics
- » Microsoft's Products Will Soon Access Open AI Tools Like ChatGPT
- » LLaMA & Alpaca; "ChatGPT" On Your Local Computer
- » Multi-Objective Recommender Systems
- » Hands-on Explainable Recommender Systems with Knowledge Graphs
- » Improving Recommender Systems with Human in the Loop
- » Open-Source Systems for Federated Learning
- » An Introduction to Federated Computation
- » Data Governance and Sharing on Lakehouse
- » Data Mesh and Lakehouse
- » Master Data Management
- » Setting up Big Data Fabric
- » On Future of the Modern Data Stack
- » Data Virtualization in Data Fabric
- » Recommender Systems using Graph Neural Networks
- » 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
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- » Digital Transformation in a Connected World
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- » MapD (Massively Parallel Database) Overview
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- » What is Apache Drill?
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- » Big Data - The Hadoop Data Warehouse
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- » 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
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- » Why Impala Continues to Lead?
- » Development Tools for Big Data
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- » Apache Tajo HCatalog Integration
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- » 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
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- » 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
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- » Choosing the Right SQL-on-Hadoop
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- » Apache Tajo
- » Build Instructions for Apache Tajo
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- » Apache Hadoop YARN – ResourceManager
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- » Apache Hadoop YARN – NodeManager
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- » Introduction to the Tez
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- » Advanced Analytics with Spark
- » Queries on Compressed RDDs
- » Twitter Analysis with Apache Spark and IBM Watson
- » The Pushdown of Everything
- » A Taste of Random Decision Forests on Apache Spark
- » ADAM Project
- » Spark - The Ultimate Scala Collections
- » BDAS Software Stack
- » Spark Cluster Configuration
- » Building Tachyon Master Branch
- » Creating Applications using Spark Streaming
- » Creating a Spark Applications
- » Working with RDD Operations
- » BlinkDB Review
- » Sparrow Paper Review
- » Spark Architecture Shuffle
- » Explore In-Memory Data Store Tachyon
- » Tachyon Overview
- » AMP Camp 6
- » Spark 1.5 DataFrame API Highlights
- » Project Tungsten
- » SparkR Setting for RStudio
- » SparkR
- » Spark Lab 3
- » Spark Lab 2
- » Diving into Spark Streaming's Execution Model
- » Lambda Architecture
- » Zeppelin Tutorial
- » Introduction to Data Science with Apache Spark
- » The Internet of Things Transforms Transactions with Apache Spark
- » Improving Spark for Data Pipelines with Native YARN Integration
- » BlinkDB User Guide
- » Recent performance improvements in Apache Spark
- » Bringing Spark Closer to Bare Metal
- » Data Exploration Using BlinkDB
- » Programming With SampleClean
- » SampleClean
- » Running BlinkDB Locally
- » Apache Spark - Caching and Checkpointing Under the Hood
- » Introduction to BlinkDB
- » Spark with Python Notebook on Mac
- » Spark Notebook
- » Spark-GPU Cluster Development in a Notebook
- » Deep Dive into Spark SQL’s Catalyst Optimizer
- » Introducing DataFrames in Spark for Large Scale Data Science
- » Introduction to the Spark
- » Big Spatial Data Processing using Spark
- » SpatialSpark
- » New Directions for Spark in 2015
- » The Spark SQL Optimizer and External Data Sources API
- » DataFrame and SQL Operations
- » Design Patterns for using foreachRDD
- » Output Operations on DStreams
- » Transformations on DStreams
- » DStream Sources
- » Discretized Streams
- » Spark Streaming Basic Concepts
- » Spark Streaming Quick Example
- » Spark Streaming Overview

- » Lightning Fast Social Media Analytics
- » Statistics-Driven OLAP Acceleration using Query Column Sets
- » Apache Calcite Overview
- » Zeppelin Tajo Interpreter
- » Release Notes - Tajo - Version 0.11.1
- » Rethinking SQL for Big Data with Apache Drill
- » BlinkDB Review
- » Approximate Aggregation Queries in Presto
- » Using Presto to combine data from Hive and MySQL
- » Apache MRQL Installation Instructions
- » Common Misconceptions about SQL on Hadoop
- » Apache MRQL
- » Apache Drill Introduction
- » Teradata Bets Big on Presto for Hadoop SQL
- » Totally Lazy
- » A SQL Query Compiler
- » Best SQL-on-hadoop Tool

- » Wide & Deep Learning; Memorization + Generalization with TensorFlow
- » Keynote - TensorFlow Dev Summit 2017
- » Off-heap Memory in Apache Flink and the curious JIT compiler
- » High-throughput, low-latency, and exactly-once stream processing with Apache Flink
- » Overview of the Apache Flink
- » Apache Flink Seminar
- » Lambda Architecture
- » Apache Storm Basics
- » Processing streaming data in Hadoop with Apache Storm
- » Navier-Stokes Equations
- » Theory of Everything
- » Facebook Presto
- » The Real-Time Big Data Architecture (RTDBA) Stack
- » The Five Phases of Real Time
- » Big Data in Motion
- » Continuous Quries Languages
- » Semantics of Relations in Continuous Queries
- » Continuous Quries as Views
- » Continuous Query Semantics and Operators (Part II)
- » Stream Windows
- » Continuous Query Semantics and Operators (Part I)
- » Writing Input And Output Adapters
- » Stream Models
- » Understanding Human Dynamics
- » Esper Relational Database Adapter
- » Event Rendering to XML and JSON
- » Esper Socket Adapter
- » Esper Adapter Concept
- » Data Stream Management
- » Physical Properties and selection of thermodynamic models
- » Centrifugal Compressor Efficiency - Part 4
- » Centrifugal Compressor Efficiency - Part 3
- » Centrifugal Compressor Efficiency - Part 2
- » Centrifugal Compressor Efficiency - Part 1
- » In-Memory Caches
- » Pattern Matching Techniques
- » Monte-Carlo Methods An Introduction
- » PRODML Data Schema and API Specifications
- » The Invention of Stream Computing
- » Real-time Operational Intelligence
- » Introduction to Esper for Java
- » Analyzing Data in Motion A Real-World View
- » Stream Data Schema Design
- » Introduction to Stream Computing
- » Streams Application Design
- » Filter pattern
- » Streams design patterns
- » Thermodynamics Basic Concepts

- » 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
- » Eight SQL on Hadoop Challenges
- » 11 SQL-on-Hadoop Tools
- » Interactive Query for Hadoop with Apache Hive on Apache Tez
- » Introducing Apache Tez 0.4
- » Big Data Genomics Sequencing
- » YARN Framework and Fault Tolerance
- » Apache Tajo
- » Build Instructions for Apache Tajo
- » 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!
- » Tez Design - Introduction
- » 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

- » Min-cost Flow Network Algorithms
- » Elasticity with YARN
- » Broadcast Join in Tajo
- » Tajo Cluster Architecture
- » Improving Spark for Data Pipelines with Native YARN Integration
- » What is Mesos?
- » FAQ for the REEF
- » Introduction to REEF
- » Application Interaction with the ResourceManager
- » Resource Manager
- » 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
- » 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)
- » 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
- » Simple YARN Application
- » Apache Hadoop YARN – Related Work
- » Failures in YARN
- » Failures in Classic MapReduce
- » 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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Sungsoo Kim

Principal Research Scientist

sungsoo@etri.re.kr