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Information Retrieval with Time Series Query

Authors:

Meichun HsuAuthors Info & Claims

ICTIR '13: Proceedings of the 2013 Conference on the Theory of Information Retrieval

Pages 56 - 63

https://doi.org/10.1145/2499178.2499195

Published: 29 September 2013 Publication History

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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.

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Cited By

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Santos BRossi RMarcacini RRoesler VValdeni de Lima JSaibel Santos CWillrich R(2017)Transductive Event Classification through Heterogeneous NetworksProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3126893(285-292)Online publication date: 17-Oct-2017
https://dl.acm.org/doi/10.1145/3126858.3126893
Atlam EGhaleb FTaha AIsmail A(2017)A new retrieval method based on time series variation using field association termsMathematical Methods in the Applied Sciences10.1002/mma.471341:15(5780-5791)Online publication date: 14-Dec-2017
https://doi.org/10.1002/mma.4713
Moulahi BTamine LYahia S(2016)When time meets information retrievalJournal of Information Science10.1177/016555151560727742:6(725-747)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1177/0165551515607277
Show More Cited By

Index Terms

Information Retrieval with Time Series Query
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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Reviews

Reviewer: Simon Berkovich

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. Online Computing Reviews Service

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Information & Contributors

Information

Published In

ICTIR '13: Proceedings of the 2013 Conference on the Theory of Information Retrieval

September 2013

148 pages

ISBN:9781450321075

DOI:10.1145/2499178

Editors:
Oren Kurland
Technion - Israel Institute of Technology
,
Donald Metzler
Google, USA
,
Christina Lioma
University of Copenhagen, Denmark
,
Birger Larsen
University of Copenhagen, Denmark
,
Peter Ingwersen
University of Copenhagen, Denmark

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

SIGIR: ACM Special Interest Group on Information Retrieval
British Computer Society: BCS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 September 2013

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ICTIR '13

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Findwise
Spinque
Univ. of Copenhagen
LARM
Royal School of Library and Information Science

ICTIR '13: International Conference on the Theory of Information Retrieval

September 29 - October 2, 2013

Copenhagen, Denmark

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ICTIR '13 Paper Acceptance Rate 11 of 51 submissions, 22%;

Overall Acceptance Rate 235 of 527 submissions, 45%

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Citations

Cited By

View all

Santos BRossi RMarcacini RRoesler VValdeni de Lima JSaibel Santos CWillrich R(2017)Transductive Event Classification through Heterogeneous NetworksProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3126893(285-292)Online publication date: 17-Oct-2017
https://dl.acm.org/doi/10.1145/3126858.3126893
Atlam EGhaleb FTaha AIsmail A(2017)A new retrieval method based on time series variation using field association termsMathematical Methods in the Applied Sciences10.1002/mma.471341:15(5780-5791)Online publication date: 14-Dec-2017
https://doi.org/10.1002/mma.4713
Moulahi BTamine LYahia S(2016)When time meets information retrievalJournal of Information Science10.1177/016555151560727742:6(725-747)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1177/0165551515607277
Khurana UParthasarathy STuraga D(2014)FAQProceedings of the 3rd International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications - Volume 3610.5555/2999973.2999977(29-45)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.5555/2999973.2999977

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Recommendations

Exploration of query context for information retrieval

A GA-based query optimization method for web information retrieval

Query expansion techniques for information retrieval: A survey

Reviews

Access critical reviews of Computing literature here