Article Source

• Title: CSCI 2270: Advanced Topics in Database Management

Topics in Main Memory and Streaming Databases

Main Memory Databases

1. IEEE Data Engineering Bulletin, Special Issue on Main Memory Databases June, 2013, Paul Larson (editor). Google (IEEE Data Engineering Bulletin)

Main Memory Databases 2

1. OLTP through the looking glass, and what we found there S. Harizopoulos, D. J. Abadi, S. Madden, and M. Stonebraker, SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, 2008, pp. 981-992.
2. The End of an Architectural Era: (It’s Time for a Complete Rewrite)M. Stonebraker, S. Madden, D. J. Abadi, S. Harizopoulos, N. Hachem, and P. Helland, VLDB ’07: Proceedings of the 33rd international conference on Very large databases, 2007, pp. 1150-1160.
3. H-Store: a High-Performance, Distributed Main Memory Transaction Processing SystemR. Kallman, H. Kimura, J. Natkins, A. Pavlo, A. Rasin, S. Zdonik, E. P. C. Jones, S. Madden, M. Stonebraker, Y. Zhang, J. Hugg, and D. J. Abadi, Proc. VLDB Endow., vol. 1, iss. 2, pp. 1496-1499, 2008.
4. Anti-Caching: A New Approach to Database Management System ArchitectureJ. DeBrabant, A. Pavlo, S. Tu, M. Stonebraker, and S. Zdonik, Proc. VLDB Endow., vol. 6, pp. 1942-1953, 2013.

Main Memory Databases 3

1. HyPer: A Hybrid OLTP&OLAP Main Memory Database System Based on Virtual Memory SnapshotsAlfons Kemper and Thomas Neumann, ICDE 2011.
2. Trekking Through Siberia: Managing Cold Data in a Memory-Optimized DatabaseAhmed Eldawy, Justin Levandoski, and Paul Larson, International Conference on Very Large Databases (PVLDB Vol. 7, Issue. 11), June 2014, VLDB – Very Large Data Bases, September 2014
3. HYRISE: a main memory hybrid storage engineGrund, M., Krüger, J., Plattner, H., Zeier, A., Cudre-Mauroux, P., & Madden, S. (2010), Proceedings of the VLDB Endowment, 4(2), 105-116.

Streaming Systems

1. STREAM: The Stanford Data Stream Management System. Book chapter - to appear. Arasu, A., A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., Ito, K., Motwani, R., Srivastava, U., and Widom, J.
2. The Aurora and Borealis Stream Processing Engines. Cetintemel, U., Abadi,D., Ahmad, Y., Balakrishnan, H., Balazinska, M., Cherniack, M., Hwang, J., Lindner, W., Madden, S., Maskey, A., Rasin, A., Ryvkina, E., Stonebraker, M., Tatbul, N., Xing, Y., and Zdonik, S. Book chapter in Data Stream Management: Processing High-Speed Data Streams. Edited by M. Garofalakis, J. Gehrke, R. Rastogi, Springer, 2007
3. Telegraph CQ: Continuous Dataflow Processing for an Uncertain WorldChandrasekaran, S., et al. CIDR 2003.
4. Introducing Microsoft StreamInsight. T. Grabs, R. Schindlauer, R. Krishnan, J. Goldstein. Microsoft White Paper, September 2009, Revised May 2010.
5. Storm@Twitter, ProceedingsProceedings of the 2014 ACM SIGMOD international conference on Management of data Pages 147-156

Stream Processing

1. Models and Issues in Data Stream Systems Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J. (PODS 2002), Madison, WI, June 2002.
2. Remembrance of Streams Past: Overload-Sensitive Management of Archived Data StreamsChandrasekaran, S. and Franklin, M. In Proceedings of the 30th International Conference on Very Large Data Bases (VLDB 2004). Toronto, Canada. August 2004.
3. Joining Punctuated Streams Ding, L., Mehta, N., Rudensteiner, E., and Heineman, G.T. EDBT 2004
4. Semantics and Evaluation Techniques for Window Aggregates in Data Streams Li, J., Maier, D., Tufte, K., Papadimos, V., and Tucker, P. In Proceedings of the 2005 ACM SIGMOD Conference on Management of Data, Toronto, Canada, June 2005.

Query Languages

1. The CQL Continuous Query Language: Semantic Foundations and Query Execution Arasu, A., Babu, S., and Widom, J. Technical Report, October 2003.
2. Hancock: A Language for Extracting Signatures from Data Streams Cortes, C., Fisher, K., Pregibon, D., Rogers, A., Smith, F.

Optimization

1. Design and Evaluation of Alternative Selection Placement Strategies in Optimizing Continuous Queries Chen J., DeWitt, D., and Naughton, J. IDEC 2002
2. Evaluating Window Joins over Unbounded Streams Kang, J., Naughton, J., Viglas, S. VLDB 2003
3. No Pane, No Gain: Efficient Evaluation of Sliding-Window Aggregates over Data Streams Li, J. Maier, D., Tufte, K., Papadimos, V., Tucker, P. SIGMOD Record, March 2005.
4. Rate-Based Query Optimization for Streaming Information Sources Viglas, S. and Naughton, J. In Proceedings of the 2002 ACM SIGMOD Conference on Management of Data, Madison, WI, June 2002.

Distributed Systems/Fault Tolerance

1. Minimizing Latency in Fault-Tolerant Distributed Stream Processing Systems Brito, A., Fetzer, C., Felber, P. ICDCS’09 (Slides)
2. High-Availability Algorithms for Distributed Stream Processing Hwayng, J., Balazinska, M. et. al. ICDE 2005
3. Towards Autonomic Fault Recovery in System-S Jacques-Silva, G., Chalenger, J., Degenaro, L., Giles, J., Wagle, R. ICAC’07

Scaling Data Stream Systems

1. Processing high data rate streams in System S H. Andrade, B. Gedik, K. -L. Wu, and P. S. Yu. 2011. J. Parallel Distrib. Comput. 71, 2 (February 2011), 145-156.
2. Optimized Processing of Multiple Aggregate Continuous Queries Guirguis, S., Sharaf, M., Chrysanthis, P., Labrinids, A. CIKM 11
3. Query-aware partitioning for monitoring massive network data streams Johnson, T., Muthukrishnan, S.M., Shkapenyuk, V., Spatscheck, O. SIGMOD 2008.
4. From a stream of relational queries to distributed stream processing Qiong Zou, Huayong Wang, Robert SouléMartin Hirzel, Henrique Andrade, Bugra Gedik, and Kun-Lung Wu. 2010.Proc. VLDB Endow. 3, 1-2 (September 2010), 1394-1405.