Information Retrieval with Time Series Query

  • Authors:
  • Hyun Duk Kim;Danila Nikitin;ChengXiang Zhai;Malu Castellanos;Meichun Hsu

  • Affiliations:
  • Dept. of Computer Science, University of Illinois at Urbana-Champaign;Dept. of Computer Science, University of Illinois at Urbana-Champaign;Dept. of Computer Science, University of Illinois at Urbana-Champaign;Information Analytics Lab, HP Laboratories;Information Analytics Lab, HP Laboratories

  • Venue:
  • Proceedings of the 2013 Conference on the Theory of Information Retrieval
  • Year:
  • 2013

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Abstract

We study a novel information retrieval problem, where the query is a time series for a given time period, and the retrieval task is to find relevant documents in a text collection of the same time period, which contain topics that are correlated with the query time series. This retrieval problem arises in many text mining applications where there is a need to analyze text data in order to discover potentially causal topics. To solve this problem, we propose and study multiple retrieval algorithms that use the general idea of ranking text documents based on how well their terms are correlated with the query time series. Experiment results show that the proposed retrieval algorithm can effectively help users find documents that are relevant to the time series queries, which can help users analyze the variation patterns of the time series.