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Information retrieval with time series query
Kim H., Nikitin D., Zhai C., Castellanos M., Hsu M.  ICTIR 2013 (Proceedings of the 4th International Conference on the Theory of Information Retrieval, Copenhagen, Denmark, Sep 29-Oct 2, 2013)56-63.2013.Type:Proceedings
Date Reviewed: May 29 2014

This work is devoted to a novel information retrieval problem. In contrast to the common action of simple retrieving of time-stamped data, this work is aimed at the discovery of time-related patterns.

The query presents a time series, and the retrieval task is to find relevant documents in a text collection of the same time period. This retrieval problem arises in many applications where there is a need to analyze text data in order to discover potential causal factors. For example, reporting a negative event in the news might affect a company’s stock prices. The alleged causal relationship could be suspected as soon as a correlation between the two time series is revealed.

Traditional information retrieval is aimed primarily at the matching of query terms and does not consider their dynamics. The presented technique measures the correlation between the time characterization of each term and the given sequence of events in the considered time series query. The established correlation could present a decisive factor among the other issues that are used for determining the ranking of text documents.

Besides the most obvious stock prices example, the developed approach can also be applied to other types of time-stamped text collections to be correlated with various time series curves. This may be related to certain signals from web traffic or from physical sensors.

An important aspect of the presented concept could be the consideration of time series problems in the information processing of the brain, like in the identification of musical tunes or the recognition of gait patterns.

Reviewer:  Simon Berkovich Review #: CR142334 (1408-0675)
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Retrieval Models (H.3.3 ... )
 
 
Search Process (H.3.3 ... )
 
 
Text Analysis (I.2.7 ... )
 
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