Jin Li
Abstract
Windows queries are proving essential to data-stream processing. In this paper, we present an approach for evaluating sliding-window aggregate queries that reduces both space and computation time for query execution. Our approach divides overlapping windows into disjoint panes, computes sub-aggregates over each pane, and "rolls up" the pane-aggregates to computer window-aggregates. Our experimental study shows that using panes has significant performance benefits.
- A. Arasu, S. Babu, and J. Widom. The CQL Continuous Query Language: Semantic Foundations and Query Execution. Stanford University Technical Report, October 2003.Google Scholar
- A. Arasu, J. Widom. Resource Sharing in Continuous Sliding-Window Aggregates. In Proceedings of the 30th International Conference on Very Large Databases (VLDB 2004). Google ScholarDigital Library
- B. Babcock et al. Models and Issues in Data Stream Systems. In Proc. of the 2002 ACM Symp. on Principles of Database Systems (PODS 2002). Google ScholarDigital Library
- D. Carney et al. Monitoring Streams - A New Class of Data Management Applications. In Proceedings of the 28th International Conference on Very Large Databases (VLDB 2002). Google ScholarDigital Library
- G. Cormode el al. Holistic UDAFs at streaming speeds. In Proceedings of the 2004 ACM SIGMOD International Conference on the Management of Data (SIGMOD 2004). Google ScholarDigital Library
- C. Cranor, T. Johnson, O. Spatashek. Gigascope: A Stream Database for Network Applications. In Proceedings of the 2003 ACM SIGMOD International Conference on the Management of Data (SIGMOD 2003). Google ScholarDigital Library
- S. Chandrasekaran et al. TelegraphCQ: Continuous Dataflow Processing for an Uncertain World. In Proceedings of the 2003 Conference on Innovative Data Systems Research.Google Scholar
- J. Gray et al. Data Cube: A Relational Aggregation Operator generalizing Group-by, Cross-Tab, and Sub Totals. Data Mining and Knowledge Discovery 1(1), 1997, 29--53. Google ScholarDigital Library
- J. Li et al. Evaluating window aggregate queries over streams. Technical Report, May 2004, OGI/OHSU. http://www.cse.ogi.edu/~jinli/papers/WinAggrQ.pdfGoogle Scholar
- J. Naughton et al. The Niagara Internet Query System. IEEE Data Engineering Bulletin, 24(2), 27--33, (June 2001).Google Scholar
- U. Srivastava, J. Widom. Flexible Time Management in Data Stream Systems. Technical Report 2003-40, Stanford University, Stanford, CA (July 2003).Google Scholar
- The STREAM Group. STREAM: The Stanford STREAM Data Manager. IEEE Data Engineering Bulletin, 26(1), (March 2003).Google Scholar
- XMark Benchmark. http://www.xml-benchmark.org.Google Scholar
Index Terms
- No pane, no gain: efficient evaluation of sliding-window aggregates over data streams
Recommendations
Payload pendulation and position control systems for an offshore container crane with adaptive‐gain sliding mode control
AbstractWhen container ports are not available for heavy ships, the offshore ship‐to‐ship transfer operation is an alternative method to an inland container terminal. This process is performed between a large container ship and a smaller ship, which is ...
Reducing the mast vibration of single-mast stacker cranes by gain-scheduled control
Abstract In the frame structure of stacker cranes harmful mast vibrations may appear due to the inertial forces of acceleration or the braking movement phase. This effect may reduce the stability and positioning accuracy of these machines. Unfortunately,...
Dynamic rolling strategy for multi-vessel quay crane scheduling
This paper focuses on the container loading and unloading problem with dynamic ship arrival times. Using a determined berth plan, in combination with the reality of a container terminal production scheduling environment, this paper proposes a scheduling ...
Comments