Lazy data structure maintenance for main-memory analytics over sliding windows

Chang Ge, Lukasz Golab

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of maintaining data structures used by memory-resident data warehouses that store sliding windows. We propose a framework that eagerly expires data from the sliding window to save space and/or satisfy data retention policies, but lazily maintains the associated data structures to reduce maintenance overhead. Using a dictionary as an example, we show that our framework enables maintenance algorithms that outperform existing approaches in terms of space overhead, maintenance overhead, and dictionary lookup overhead during query execution.

Original languageEnglish (US)
Title of host publicationDOLAP 2013 - Proceedings of the 16th International Workshop on Data Warehousing and OLAP, Co-located with CIKM 2013
Pages33-38
Number of pages6
DOIs
StatePublished - 2013
Externally publishedYes
EventDOLAP 2013 - Proceedings of the 16th ACM International Workshop on Data Warehousing and OLAP, Co-located with the 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 28 2013Oct 28 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

ConferenceDOLAP 2013 - Proceedings of the 16th ACM International Workshop on Data Warehousing and OLAP, Co-located with the 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period10/28/1310/28/13

Keywords

  • Dictionary encoding
  • Main-memory analytics
  • Sliding windows

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