Query refinement for correlation-based time series exploration

Abdullah M. Albarrak, Mohamed A. Sharaf

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


In this paper, we focus on the problem of exploring sequential data to discover time sub-intervals that satisfy certain pairwise correlation constraints. Differently than most existing works, we use the deviation from targeted pairwise correlation constraints as an objective to minimize in our problem. Moreover, we include users preferences as an objective in the form of maximizing similarity to users’ initial sub-intervals. The combination of these two objectives are prevalent in applications where users explore time series data to locate time sub-intervals in which targeted patterns exist. Discovering these sub-intervals among time series data is extremely useful in various application areas such as network and environment monitoring. Towards finding the optimal sub-interval (i.e., optimal query) satisfying these objectives, we propose applying query refinement techniques to enable efficient processing of candidate queries. Specifically, we propose QFind, an efficient algorithm which refines a user’s initial query to discover the optimal query by applying novel pruning techniques. QFind applies two-level pruning techniques to safely skip processing unqualified candidate queries, and early abandon the computations of correlation for some pairs based on a monotonic property. We experimentally validate the efficiency of our proposed algorithm against state-of-the-art algorithm under different settings using real and synthetic data.

Original languageEnglish
Title of host publicationDatabases Theory and Applications - 28th Australasian Database Conference, ADC 2017, Proceedings
EditorsXiaokui Xiao, Xin Cao, Zi Huang
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783319681542
Publication statusPublished - 2017
Externally publishedYes
Event28th Australasian Database Conference, ADC 2017 - Brisbane, Australia
Duration: Sept 25 2017Sept 28 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10538 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference28th Australasian Database Conference, ADC 2017

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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