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Durable Queries over Historical Time Series
March 2014 (vol. 26 no. 3)
pp. 595-607
Hao Wang, The University of Hong Kong, Hong Kong
Yilun Cai, The University of Hong Kong, Hong Kong
Yin Yang, Advanced Digital Sciences Center, Singapore
Shiming Zhang, Noah's Ark Lab, Hong Kong
Nikos Mamoulis, The University of Hong Kong, Hong Kong
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 ($({\rm D}k{\rm NN})$) 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 $({\rm D}k{\rm NN})$ 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.
Index Terms:
Time series analysis,Indexing,Trajectory,Knowledge discovery,Data engineering,Search problems,spatiotemporal databases,Durable query,time series,historical data
Citation:
Hao Wang, Yilun Cai, Yin Yang, Shiming Zhang, Nikos Mamoulis, "Durable Queries over Historical Time Series," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 595-607, March 2014, doi:10.1109/TKDE.2013.10
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