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Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series

Published: 27 June 2017 Publication History

Abstract

In recent years, the research community, inspired by its success in dealing with single-dimensional time series, has turned its attention to dealing with multidimensional time series. There are now a plethora of techniques for indexing, classification, and clustering of multidimensional time series. However, we argue that the difficulty of exploratory search in large multidimensional time series remains underappreciated. In essence, the problem reduces to the "chicken-and-egg" paradox that it is difficult to produce a meaningful query without knowing the best subset of dimensions to use, but finding the best subset of dimensions is itself query dependent. In this work we propose a solution to this problem. We introduce an algorithm that runs in the background, observing the user's search interactions. When appropriate, our algorithm suggests to the user a dimension that could be added or deleted to improve the user's satisfaction with the query. These query dependent suggestions may be useful to the user, even if she does not act on them (by reissuing the query), as they can hint at unexpected relationships or redundancies between the dimensions of the data. We evaluate our algorithm on several real-world datasets in medical, human activity, and industrial domains, showing that it produces subjectively sensible and objectively superior results.

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  • (2020)Correlation-based search for time series dataInternational Journal of Computer Applications in Technology10.1504/ijcat.2020.10468462:2(158-174)Online publication date: 1-Jan-2020

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  1. Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series

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    cover image ACM Other conferences
    SSDBM '17: Proceedings of the 29th International Conference on Scientific and Statistical Database Management
    June 2017
    373 pages
    ISBN:9781450352826
    DOI:10.1145/3085504
    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 ACM 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]

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    Published: 27 June 2017

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    1. Multidimensional Time Series
    2. Query Suggestion

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    • (2020)Correlation-based search for time series dataInternational Journal of Computer Applications in Technology10.1504/ijcat.2020.10468462:2(158-174)Online publication date: 1-Jan-2020

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