Durable queries over historical time series

Hao Wang, Yilun Cai, Yin Yang, Shiming Zhang, Nikos Mamoulis

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

This paper studies the problem of finding objects with durable quality over time in historical time series databases. For example, a sociologist may be interested in the top 10 web search terms during the period of some historical events; the police may seek for vehicles that move close to a suspect 70 percent of the time during a certain time period and so on. Durable top-(k) (DTop-(k)) and nearest neighbor (Dk NN) queries can be viewed as natural extensions of the standard snapshot top-(k) and NN queries to timestamped sequences of values or locations. Although their snapshot counterparts have been studied extensively, to our knowledge, there is little prior work that addresses this new class of durable queries. Existing methods for DTop-(k) processing either apply trivial solutions, or rely on domain-specific properties. Motivated by this, we propose efficient and scalable algorithms for the DTop-(k) and (Dk NN) queries, based on novel indexing and query evaluation techniques. Our experiments show that the proposed algorithms outperform previous and baseline solutions by a wide margin.

Original languageEnglish
Article number6409352
Pages (from-to)595-607
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number3
DOIs
Publication statusPublished - 1 Mar 2014

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Keywords

  • Durable query
  • Historical data
  • Spatiotemporal databases
  • Time series

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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