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Window-Slicing Techniques Extended to Spanning-Event Streams

Authors Aurélie Suzanne , Guillaume Raschia , José Martinez, Damien Tassetti

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Aurélie Suzanne
  • Université de Nantes, France
  • Expandium, Saint-Herblain, France
Guillaume Raschia
  • Université de Nantes, France
José Martinez
  • Université de Nantes, France
Damien Tassetti
  • Université de Nantes, France

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Aurélie Suzanne, Guillaume Raschia, José Martinez, and Damien Tassetti. Window-Slicing Techniques Extended to Spanning-Event Streams. In 27th International Symposium on Temporal Representation and Reasoning (TIME 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 178, pp. 10:1-10:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.TIME.2020.10

Abstract

Streaming systems often use slices to share computation costs among overlapping windows. However they are limited to instantaneous events where only one point represents the event. Here, we extend streams to events that come with a duration, denoted as spanning events. After a short review of the new constraints ensued by event lifespan in a temporal sliding-window context, we propose a new structure for dealing with slices in such an environment, and prove that our technique is both correct and effective to deal with such spanning events.

Subject Classification

ACM Subject Classification
  • Information systems → Stream management
Keywords
  • Data Stream
  • Spanning-events
  • Temporal Aggregates
  • Sliding Windows

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References

  1. James F. Allen. Maintaining knowledge about temporal intervals. Communications of ACM, 26(11):832-843, 1983. Google Scholar
  2. Arvind Arasu, Shivnath Babu, and Jennifer Widom. The CQL continuous query language: semantic foundations and query execution. The VLDB Journal, 15(2):121-142, 2006. Google Scholar
  3. Arvind Arasu and Jennifer Widom. Resource Sharing in Continuous Sliding-Window Aggregates. VLDB '04, 30:336-347, 2004. Google Scholar
  4. Michael H. Böhlen, Anton Dignös, Johann Gamper, and Christian S. Jensen. Temporal Data Management : An Overview. In eBISS 2017, volume 324, pages 51-83, 2017. Google Scholar
  5. Paris Carbone, Jonas Traub, Asterios Katsifodimos, Seif Haridi, and Volker Markl. Cutty: Aggregate Sharing for User-Defined Windows. In CIKM '16, pages 1201-1210, 2016. Google Scholar
  6. Jim Gray, Surajit Chaudhuri, Adam Bosworth, Andrew Layman, Don Reichart, Murali Venkatrao, Frank Pellow, and Hamid Pirahesh. Data Cube : A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. Data Mining and Knowledge Discovery, 1(1):29-53, 1997. Google Scholar
  7. Martin Hirzel, Scott Schneider, and Kanat Tangwongsan. Tutorial: Sliding-Window Aggregation Algorithms. In DEBS '17, pages 11-14, 2017. Google Scholar
  8. Hyeon Gyu Kim and Myoung Ho Kim. A review of window query processing for data streams. Journal of Computing Science and Engineering, 7(4):220-230, 2013. Google Scholar
  9. Sailesh Krishnamurthy, Michael J. Franklin, Jeffrey Davis, Daniel Farina, Pasha Golovko, Alan Li, and Neil Thombre. Continuous analytics over discontinuous streams. In SIGMOD '10, pages 1081-1092, 2010. Google Scholar
  10. Sailesh Krishnamurthy, Chung Wu, and Michael Franklin. On-the-fly sharing for streamed aggregation. In SIGMOD '06, pages 623-634, 2006. Google Scholar
  11. Jin Li, David Maier, Kristin Tufte, Vassilis Papadimos, and Peter A. Tucker. No Pane, No Gain: Efficient Evaluation of Sliding-Window Aggregates over Data Streams. ACM SIGMOD Record, 34(1):39-44, 2005. Google Scholar
  12. Jin Li, Kristin Tufte, David Maier, and Vassilis Papadimos. AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation. IEEE Internet Computing, 12(6):22-29, 2008. Google Scholar
  13. Jin Li, Kristin Tufte, Vladislav Shkapenyuk, Vassilis Papadimos, Theodore Johnson, and David Maier. Out-of-order processing: a new architecture for high-performance stream systems. Proceedings of the VLDB Endowment, 1(1):274-288, 2008. Google Scholar
  14. Kostas Patroumpas and Timos Sellis. Window Specification over Data Streams. EDBT '06, pages 445-464, 2006. Google Scholar
  15. Danila Piatov and Sven Helmer. Sweeping-based temporal aggregation. SSTD 2017: Advances in Spatial and Temporal Databases, LNCS 10411:125-144, 2017. Google Scholar
  16. Anatoli U. Shein, Panos K. Chrysanthis, and Alexandros Labrinidis. FlatFIT: Accelerated incremental sliding-window aggregation for real-time analytics. SSDBM '17, pages 1-12, 2017. Google Scholar
  17. Anatoli U. Shein, Panos K. Chrysanthis, and Alexandros Labrinidis. SlickDeque: High Throughput and Low Latency Incremental Sliding-Window Aggregation. EDBT '18, pages 397-408, 2018. Google Scholar
  18. Kanat Tangwongsan, Martin Hirzel, and Scott Schneider. Low-Latency Sliding-Window Aggregation in Worst-Case Constant Time. DEBS '17, pages 66-77, 2017. Google Scholar
  19. Kanat Tangwongsan, Martin Hirzel, and Scott Schneider. Optimal and general out-of-order sliding-window aggregation. Proceedings of the VLDB Endowment, 12(10):1167-1180, 2019. Google Scholar
  20. Kanat Tangwongsan, Martin Hirzel, Scott Schneider, and Kun-Lung Wu. General incremental sliding-window aggregation. Proceedings of the VLDB Endowment, 8(7):702-713, 2015. Google Scholar
  21. Jonas Traub, Philipp Grulich, Alejandro Rodríguez Cuéllar, Sebastian Breß, Asterios Katsifodimos, Tilmann Rabl, and Volker Markl. Efficient Window Aggregation with General Stream Slicing. In EDBT '19', pages 97-108, 2019. Google Scholar
  22. Jonas Traub, Philipp Marian Grulich, Alejandro Rodriguez Cuellar, Sebastian Bress, Asterios Katsifodimos, Tilmann Rabl, and Volker Markl. Scotty: Efficient Window Aggregation for Out-of-Order Stream Processing. In ICDE '18', pages 1300-1303, 2018. Google Scholar
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