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A dynamic query interface for finding patterns in time series data

Published: 20 April 2002 Publication History

Abstract

Identification of patterns in time series data sets is a task that arises in a wide variety of application domains. This demonstration presents the timebox model of rectangular regions that specify constraints for dynamic queries over time series data sets, and the TimeSearcher application, which uses timeboxes as the basis of an interactive query tool.

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MP4 File (17_chi_2002_dynamic_query_interface.mp4)

References

[1]
Agrawal, R., Plaila, G., Wimmers, E.L, and Zaft, M. Querying shapes of histories, in Proc. of the 21st Int'l Conference on Very Large Databases (1995) 502--514.
[2]
Little, J.B., and Rhodes, L. Understanding Wall Street. Cockeysville, MD, Liberty Publishing (1978).
[3]
Morrill, J.P. Distributed recognition of patterns in time series data. Comm. ACM 45, 5 (May 1998), 45--51.
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Spotfire. http://www.spotfire.com (2001).
[5]
Wattenberg, M. Sketching a graph to query a time-series database, in Proc. of CHI '01, Extended Abstracts (Seattle WA, March--April 2001), ACM Press, 379--380.

Cited By

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  • (2021)Towards semantic search in building sensor dataProceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3486611.3486647(164-167)Online publication date: 17-Nov-2021
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  • (2020)Qute: Query by Text Search for Time Series DataProceedings of the Future Technologies Conference (FTC) 2020, Volume 210.1007/978-3-030-63089-8_27(412-427)Online publication date: 1-Nov-2020
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Information

Published In

cover image ACM Conferences
CHI EA '02: CHI '02 Extended Abstracts on Human Factors in Computing Systems
April 2002
488 pages
ISBN:1581134541
DOI:10.1145/506443
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2002

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Author Tags

  1. dynamic queries
  2. information visualization
  3. time series

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CHI02
Sponsor:
CHI02: Human Factors in Computing Systems
April 20 - 25, 2002
Minnesota, Minneapolis, USA

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

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Cited By

View all
  • (2021)Towards semantic search in building sensor dataProceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3486611.3486647(164-167)Online publication date: 17-Nov-2021
  • (2020)Stance DetectionACM Computing Surveys10.1145/336902653:1(1-37)Online publication date: 6-Feb-2020
  • (2020)Qute: Query by Text Search for Time Series DataProceedings of the Future Technologies Conference (FTC) 2020, Volume 210.1007/978-3-030-63089-8_27(412-427)Online publication date: 1-Nov-2020
  • (2019)Putting the Human in the Time Series Analytics LoopCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317308(635-644)Online publication date: 13-May-2019
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  • (2018)ChronodesACM Transactions on Interactive Intelligent Systems10.1145/31528888:1(1-21)Online publication date: 6-Feb-2018
  • (2018)Cytoscape StringApp: Network Analysis and Visualization of Proteomics DataJournal of Proteome Research10.1021/acs.jproteome.8b0070218:2(623-632)Online publication date: 19-Nov-2018
  • (2013)Visual Analytics Tools – A Lens into Player’s Temporal Progression and BehaviorGame Analytics10.1007/978-1-4471-4769-5_19(435-470)Online publication date: 6-Mar-2013
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