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Guided interaction: rethinking the query-result paradigm

Authors:

H. V. JagadishAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 4, Issue 12

Pages 1466 - 1469

https://doi.org/10.14778/3402755.3402797

Published: 01 August 2011 Publication History

Abstract

Many decades of research, coupled with continuous increases in computing power, have enabled highly efficient execution of queries on large databases. In consequence, for many databases, far more time is spent by users formulating queries than by the system evaluating them. It stands to reason that, looking at the overall query experience we provide users, we should pay attention to how we can assist users in the holistic process of obtaining the information they desire from the database, and not just the constituent activity of efficiently generating a result given a complete precise query.

In this paper, we examine the conventional query-result paradigm employed by databases and demonstrate challenges encountered when following this paradigm for an information seeking task. We recognize that the process of query specification itself is a major stumbling block. With current computational abilities, we are at a point where we can make use of the data in the database to aid in this process.

To this end, we propose a new paradigm, guided interaction, to solve the noted challenges, by using interaction to guide the user through the query formulation, query execution and result examination processes. The user can be given advance information during query specification that can not only assist in query formulation, but may also lead to abandonment of an unproductive query direction or the satisfaction of information need even before the query specification is complete. There are significant engineering challenges to constructing the system we envision, and the technological building blocks to address these challenges exist today.

References

[1]

S. Acharya, P. Gibbons, and V. Poosala. AQUA: A Fast Decision Support Systems using Approximate Query Answers. VLDB, pages 754--757, 1999.

Digital Library

[2]

P. Anick et al. The Paraphrase Search Assistant: Terminological Feedback for Iterative Seeking. SIGIR, pages 153--159, 1999.

Digital Library

[3]

B. Bailey et al. The Effects of Interruptions on Task Performance in the User Interface. INTERACT, pages 593--601, 2001.

[4]

H. Bast and I. Weber. Completesearch: Interactive, Efficient, & Towards IR/DB Integration. CIDR, 2007.

[5]

B. Britton et al. Effects of Prior Knowledge on Use of Cognitive Capacity. Verbal Learning & Behavior, 21(4):421--436, 1982.

[6]

U. Fayyad, G. Grinstein, and A. Wierse. Info. Vis. in Data Mining & Knowledge Discovery. M. Kaufmann, 2002.

[7]

M. Ferreira de Oliveira and H. Levkowitz. Visual Data Exploration to Visual Data Mining. Visualization & Computer Graphics, pages 1--8, 2003.

Digital Library

[8]

M. Hearst. Design Recommendations for Hierarchical Faceted Search Interfaces. ACM Faceted Search, 2006.

[9]

J. Hellerstein, R. Avnur, et al. Interactive Data Analysis: the Control Project. Computer, 32(8):51--59, 2002.

Digital Library

[10]

J. Hellerstein et al. Adaptive Query Processing: Technology in Evolution. TCDE, pages 7--18, 2000.

[11]

E. Horvitz. Principles of Mixed-Initiative User Interfaces. Human Factors, pages 159--166, 1999.

[12]

H. Jagadish et al. Making Database Systems Usable. SIGMOD, pages 13--24, 2007.

Digital Library

[13]

N. Khoussainova et al. SnipSuggest: A Context-Aware SQL-Autocomplete System. PVLDB, 4(1):22--33, 2011.

[14]

J. Mackinlay, P. Hanrahan, and C. Stolte. Automatic Presentation for Visual Analysis. IEEE TVCG, pages 1137--1144, 2007.

[15]

A. Motro. VAGUE: A user interface to relational databases that permits vague queries. TOIS, 6(3):187--214, 1988.

Digital Library

[16]

A. Nandi and H. Jagadish. Assisted Querying Using Instant-Response Interfaces. SIGMOD, page 1156, 2007.

[17]

M. T. Ozsu, L. Chang, and J. Yu. Keyword Search in Databases. Morgan & Claypool Publishers, 2010.

[18]

M. Zloof. Query-By-Example: a Data Base Language. IBM Systems Journal, 16(4):324--343, 1977.

Digital Library

Cited By

Yoo HNandi AFekete JOmidvar-Tehrani BRong KShraga R(2024)Guided Querying over Videos using Autocompletion SuggestionsProceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics10.1145/3665939.3665964(1-7)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665939.3665964
Singh V(2023)Promoting Document Relevance Using Query Term Proximity for Exploratory SearchInternational Journal of Information Retrieval Research10.4018/IJIRR.32507213:1(1-22)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.4018/IJIRR.325072
Clark MNewman MDutta P(2022)ARticulateProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172356:1(1-24)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517235
Show More Cited By

Guided interaction: rethinking the query-result paradigm
1. Information systems
  1. Data management systems
    1. Database management system engines
  2. Information retrieval
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 4, Issue 12

August 2011

303 pages

ISSN:2150-8097

Editor:
H. V. Jagadish

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VLDB Endowment

Publication History

Published: 01 August 2011

Published in PVLDB Volume 4, Issue 12

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Cited By

Yoo HNandi AFekete JOmidvar-Tehrani BRong KShraga R(2024)Guided Querying over Videos using Autocompletion SuggestionsProceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics10.1145/3665939.3665964(1-7)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665939.3665964
Singh V(2023)Promoting Document Relevance Using Query Term Proximity for Exploratory SearchInternational Journal of Information Retrieval Research10.4018/IJIRR.32507213:1(1-22)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.4018/IJIRR.325072
Clark MNewman MDutta P(2022)ARticulateProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172356:1(1-24)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517235
El-Mahgary SSoisalon-Soininen EOrponen PRönnholm PHyyppä H(2022)OVI-3: A NoSQL visual query system supporting efficient anti-joinsJournal of Intelligent Information Systems10.1007/s10844-022-00742-460:3(777-801)Online publication date: 21-Sep-2022
https://dl.acm.org/doi/10.1007/s10844-022-00742-4
Singh V(2019)Predicting Search Intent Based on In-Search Context for Exploratory SearchInternational Journal of Advanced Pervasive and Ubiquitous Computing10.4018/IJAPUC.201907010411:3(53-75)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.4018/IJAPUC.2019070104
Bonifati AMartens WTimm T(2019)An analytical study of large SPARQL query logsProceedings of the VLDB Endowment10.14778/3167892.316789511:2(149-161)Online publication date: 17-Jan-2019
https://dl.acm.org/doi/10.14778/3167892.3167895
Omidvar-Tehrani BAmer-Yahia SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Data Pipelines for User Group AnalyticsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314028(2048-2053)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3314028
Bonifati AMartens WTimm T(2018)An analytical study of large SPARQL query logsProceedings of the VLDB Endowment10.14778/3149193.314919611:2(149-161)Online publication date: 5-Oct-2018
https://dl.acm.org/doi/10.14778/3149193.3149196
Bonifati AMartens WTimm T(2017)An analytical study of large SPARQL query logsProceedings of the VLDB Endowment10.5555/3167892.316789511:2(149-161)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.5555/3167892.3167895
Kahng MNavathe SStasko JChau D(2016)Interactive browsing and navigation in relational databasesProceedings of the VLDB Endowment10.14778/2994509.29945209:12(1017-1028)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.14778/2994509.2994520
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