research-article

Qetch: Time Series Querying with Expressive Sketches

Authors:

Miro Mannino,

Azza AbouziedAuthors Info & Claims

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Pages 1741 - 1744

https://doi.org/10.1145/3183713.3193547

Published: 27 May 2018 Publication History

Get Access

Abstract

Query-by-sketch tools allow users to sketch a pattern to search a time series database for matches. Prior work adopts a bottom-up design approach: the sketching interface is built to reflect the inner workings of popular matching algorithms like Dynamic time warping (DTW) or Euclidean distance (ED). We design Qetch, a query-by-sketch tool for time series data, top-down. Users freely sketch patterns on a scale-less canvas. By studying how humans sketch time series patterns we develop a matching algorithm that accounts for human sketching errors. Qetch's top-down design and novel matching algorithm enable the easy construction of expressive queries that include regular expressions over sketches and queries over multiple time series. Our demonstration showcases Qetch and summarizes results from our evaluation of Qetch's effectiveness.

References

[1]

P. Cortez, M. Rio, M. Rocha, and P. Sousa. 2006. Internet Traffic Forecasting using Neural Networks The 2006 IEEE International Joint Conference on Neural Network Proceedings. 2635--2642. /10.1145/634067.634292

Crossref

Google Scholar

[2]

Kostas Zoumpatianos, Stratos Idreos, and Themis Palpanas. 2015. RINSE: Interactive Data Series Exploration with ADS. Proc. VLDB Endow., Vol. 8, 12 (Aug. 2015), 1912--1915. showISSN2150--8097

Digital Library

Google Scholar

Cited By

View all

Zhang ZShao ZCrotty A(2024)DeepSketch: A Query Sketching Interface for Deep Time Series Similarity SearchProceedings of the VLDB Endowment10.14778/3685800.368587717:12(4369-4372)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685877
Troidl JWarchol SChoi JMatelsky JDhanyasi NWang XWester BWei DLichtman JPfister HBeyer J(2024)- Visual Analysis of Neuronal Connectivity MotifsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332738830:1(748-758)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327388
Luo YZhou YTang NLi GChai CShen L(2023)Learned Data-aware Image Representations of Line Charts for Similarity SearchProceedings of the ACM on Management of Data10.1145/35889421:1(1-29)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588942
Show More Cited By

Index Terms

Qetch: Time Series Querying with Expressive Sketches
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Temporal data

Recommendations

Expressive Time Series Querying with Hand-Drawn Scale-Free Sketches
CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

We present Qetch, a tool where users freely sketch patterns on a scale-less canvas to query time series data without specifying query length or amplitude. We study how humans sketch time series patterns --- humans preserve visually salient perceptual ...
A Demonstration of Scale-Free Query by Sketching with Qetch
CHI EA '18: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems

We demonstrate Qetch [1], a time series querying tool, where users freely sketch patterns of interest, without specifying query length or amplitude, on a scale-free canvas. The design of Qetch's interface and matching algorithm were motivated by a crowd ...
A System for Specification and Rapid Prototyping of Application Command Languages

The XS-2 system that integrates specification, rapid prototyping, and the actual use of application dialogs is described. The XS-2 command language grammar, a nonprocedural description language based on regular expressions, is used to specify commands ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

May 2018

1874 pages

ISBN:9781450347037

DOI:10.1145/3183713

General Chairs:
Gautam Das
University of Texas at Arlington, USA
,
Christopher Jermaine
Rice University, USA
,
Philip Bernstein
Microsoft Research, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGMOD/PODS '18

Sponsor:

SIGMOD

SIGMOD/PODS '18: International Conference on Management of Data

June 10 - 15, 2018

TX, Houston, USA

Acceptance Rates

SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
483
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)2

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhang ZShao ZCrotty A(2024)DeepSketch: A Query Sketching Interface for Deep Time Series Similarity SearchProceedings of the VLDB Endowment10.14778/3685800.368587717:12(4369-4372)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685877
Troidl JWarchol SChoi JMatelsky JDhanyasi NWang XWester BWei DLichtman JPfister HBeyer J(2024)- Visual Analysis of Neuronal Connectivity MotifsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332738830:1(748-758)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327388
Luo YZhou YTang NLi GChai CShen L(2023)Learned Data-aware Image Representations of Line Charts for Similarity SearchProceedings of the ACM on Management of Data10.1145/35889421:1(1-29)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588942
Yu YBecker TTrinh LBehrisch M(2023)SAXRegEx: Multivariate time series pattern search with symbolic representation, regular expression, and query expansionComputers & Graphics10.1016/j.cag.2023.03.002112(13-21)Online publication date: May-2023
https://doi.org/10.1016/j.cag.2023.03.002
Nobre CWootton DCutler ZHarrison LPfister HLex AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)reVISit: Looking Under the Hood of Interactive Visualization StudiesProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445382(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445382
Chibah AAmer-Yahia SBerti-Equille L(2021)QeNoBi: A System for QuErying and mining BehavIoral Patterns2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00301(2673-2676)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00301
Khelifati AKhayati MCudre-Mauroux PHanni ALiu QHauswirth M(2021)VADETIS: An Explainable Evaluator for Anomaly Detection Techniques2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00298(2661-2664)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00298
Gathani SLim PBattle LBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Debugging Database Queries: A Survey of Tools, Techniques, and UsersProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376485(1-16)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376485
Echihabi KZoumpatianos KPalpanas TBenbrahim H(2019)Return of the Lernaean HydraProceedings of the VLDB Endowment10.14778/3368289.336830313:3(403-420)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.14778/3368289.3368303
Imani SAlaee SKeogh E(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
https://dl.acm.org/doi/10.1145/3308560.3317308
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Expressive Time Series Querying with Hand-Drawn Scale-Free Sketches

A Demonstration of Scale-Free Query by Sketching with Qetch

A System for Specification and Rapid Prototyping of Application Command Languages