research-article

Dynamic faceted search for discovery-driven analysis

Authors:

Debabrata Dash,

Nimrod Megiddo,

Anastasia Ailamaki,

Guy LohmanAuthors Info & Claims

CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management

Pages 3 - 12

https://doi.org/10.1145/1458082.1458087

Published: 26 October 2008 Publication History

Abstract

We propose a dynamic faceted search system for discovery-driven analysis on data with both textual content and structured attributes. From a keyword query, we want to dynamically select a small set of "interesting" attributes and present aggregates on them to a user. Similar to work in OLAP exploration, we define "interestingness" as how surprising an aggregated value is, based on a given expectation. We make two new contributions by proposing a novel "navigational" expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values. Through a user survey, we find the new expectation and interestingness metric quite effective. We develop an efficient dynamic faceted search system by improving a popular open source engine, Solr. Our system exploits compressed bitmaps for caching the posting lists in an inverted index, and a novel directory structure called a bitset tree for fast bitset intersection. We conduct a comprehensive experimental study on large real data sets and show that our engine performs 2 to 3 times faster than Solr.

References

[1]

Sanjay Agrawal, et al: DBXplorer: A System for Keyword-Based Search over Relational Databases. In ICDE 2002: 5--16

Digital Library

[2]

http://base.google.com/

[3]

http://clusty.com/

[4]

Thomas M. Cover and Joy a. Thomas. Elements of Information Theory. 1992.

Digital Library

[5]

W. Dakka, et al: Automatic discovery of useful facet terms. In SIGIR Faceted Search Workshop, 2006

[6]

DBLP dataset: http://dblp.uni-trier.de/xml/

[7]

Bradley Efron and Robert J. Tibshirani: An introduction to the bootstrap. Chapman & Hall, 1993

[8]

http://endeca.com/

[9]

The Flamenco Search Interface Project. http://flamenco.berkeley.edu/

[10]

Roy Goldman and Jennifer Widom, DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases, In VLDB 1997

Digital Library

[11]

Jim Gray, et al: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total. ICDE 1996: 152--159

Digital Library

[12]

Vagelis Hristidis, et al: DISCOVER: Keyword Search in Relational Databases. VLDB 2002

Digital Library

[13]

Ihab F. Ilyas, et al: CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies. In SIGMOD 2004

Digital Library

[14]

Xiaohui Long et al: Optimized Query Execution in Large Search Engines with Global Page Ordering, VLDB 2003

Digital Library

[15]

http://lucene.apache.org/

[16]

Tom M. Mitchell: Machine learning. McGraw-Hill,1997

Digital Library

[17]

Patent dataset: http://www.nber.org/patents

[18]

John Roddick, et al: A Survey of Temporal Knowledge Discovery Paradigms and Methods. In TKDE, 2002

Digital Library

[19]

Sunita Sarawagi: User-Adaptive Exploration of Multidimensional Data. VLDB 2000: 307--316

[20]

Sunita Sarawagi, et al: Discovery-Driven Exploration of OLAP Data Cubes. In EDBT 1998

Digital Library

[21]

http://incubator.apache.org/solr/

[22]

Jayme Luiz Szwarcfiter: Optimal multiway search trees for variable size keys, In Acta Informatica, 1984

Digital Library

[23]

http://www.ibm.com/software/data/discovery/content

[24]

Witten, I.H., et al: Managing Gigabytes: Compressing and Indexing Documents and Images. 1994

Digital Library

[25]

http://www.research.ibm.com/UIMA/

[26]

Ping Wu, et al: From Keyword-based Retrieval to Keyword-driven Analytical Processing: A Multi-faceted Approach. SIGMOD 2007

[27]

Kesheng Wu, et al: Optimizing bitmap indices with efficient compression. ACM TODS, 31(1), 2006

Digital Library

[28]

http://commons.apache.org/math/

[29]

Hellerstein, et al: Generalized search trees for database systems. In VLDB, 1995.

Digital Library

[30]

Liqiang Geng, et al. Interestingness measures for data mining: A survey. In ACM Computing Surveys, 2006.

Digital Library

[31]

Friedman, et al: Exploratory Projection Pursuit. In JASA, 1987.

[32]

Swayne, et al: XGobi: Interactive Dynamic Data Visualization in the X Window System. In JCGS, 1998.

Cited By

Deng ZDou ZWen J(2023)DeepQFM: a deep learning based query facets mining methodInformation Retrieval Journal10.1007/s10791-023-09427-026:1-2Online publication date: 30-Oct-2023
https://doi.org/10.1007/s10791-023-09427-0
Huang EDiao YLiu APeng LPalma L(2023)Efficient and robust active learning methods for interactive database explorationThe VLDB Journal10.1007/s00778-023-00816-x33:4(931-956)Online publication date: 16-Nov-2023
https://doi.org/10.1007/s00778-023-00816-x
Mahdi MAhmad ANatiq HSubhi MQassim Q(2021)Comprehensive Review and Future Research Directions on Dynamic Faceted SearchApplied Sciences10.3390/app1117811311:17(8113)Online publication date: 31-Aug-2021
https://doi.org/10.3390/app11178113
Show More Cited By

Index Terms

Dynamic faceted search for discovery-driven analysis
1. Information systems
  1. Information retrieval
  2. Information storage systems

Recommendations

Extending Faceted Search to the General Web
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Faceted search helps users by offering drill-down options as a complement to the keyword input box, and it has been used successfully for many vertical applications, including e-commerce and digital libraries. However, this idea is not well explored for ...
Personalized interactive faceted search
WWW '08: Proceedings of the 17th international conference on World Wide Web

Faceted search is becoming a popular method to allow users to interactively search and navigate complex information spaces. A faceted search system presents users with key-value metadata that is used for query refinement. While popular in e-commerce and ...
Minimum-effort driven dynamic faceted search in structured databases
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management

In this paper, we propose minimum-effort driven navigational techniques for enterprise database systems based on the faceted search paradigm. Our proposed techniques dynamically suggest facets for drilling down into the database such that the cost of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management

October 2008

1562 pages

ISBN:9781595939913

DOI:10.1145/1458082

General Chair:
James G. Shanahan
Church and Duncan Group Inc, USA
,
Program Chairs:
Sihem Amer-Yahia
Yahoo! Research, USA
,
Ioana Manolescu
INRIA, France
,
Yi Zhang
University of California, Santa Cruz, USA
,
David A. Evans
JustSystems Evans Research, USA
,
Alek Kolcz
Microsoft Live Labs, USA
,
Key-Sun Choi
KAIST, Korea
,
Abdur Chowdury
Twitter, USA

Copyright © 2008 ACM.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

CIKM08

Sponsor:

CIKM08: Conference on Information and Knowledge Management

October 26 - 30, 2008

California, Napa Valley, USA

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

52
Total Citations
View Citations
1,112
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)2

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Deng ZDou ZWen J(2023)DeepQFM: a deep learning based query facets mining methodInformation Retrieval Journal10.1007/s10791-023-09427-026:1-2Online publication date: 30-Oct-2023
https://doi.org/10.1007/s10791-023-09427-0
Huang EDiao YLiu APeng LPalma L(2023)Efficient and robust active learning methods for interactive database explorationThe VLDB Journal10.1007/s00778-023-00816-x33:4(931-956)Online publication date: 16-Nov-2023
https://doi.org/10.1007/s00778-023-00816-x
Mahdi MAhmad ANatiq HSubhi MQassim Q(2021)Comprehensive Review and Future Research Directions on Dynamic Faceted SearchApplied Sciences10.3390/app1117811311:17(8113)Online publication date: 31-Aug-2021
https://doi.org/10.3390/app11178113
Kahil MBouramoul ADerdour M(2021)GreedyBigVis – A greedy approach for preparing large datasets to multidimensional visualizationInternational Journal of Computers and Applications10.1080/1206212X.2021.192067044:8(760-769)Online publication date: 2-May-2021
https://doi.org/10.1080/1206212X.2021.1920670
Ge XZhang XChrysanthis P(2020)ExNav: An Interactive Big Data Exploration Framework for Big Unstructured Data2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377741(503-512)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377741
Mihindukulasooriya NMahindru RChowdhury MDeng YFauceglia NRossiello GDash SGliozzo ATao S(2020)Dynamic Faceted Search for Technical Support Exploiting Induced KnowledgeThe Semantic Web – ISWC 202010.1007/978-3-030-62466-8_42(683-699)Online publication date: 2-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-62466-8_42
Zhang CHan J(2019)Multidimensional Mining of Massive Text DataSynthesis Lectures on Data Mining and Knowledge Discovery10.2200/S00903ED1V01Y201902DMK01711:2(1-198)Online publication date: 21-Mar-2019
https://doi.org/10.2200/S00903ED1V01Y201902DMK017
Niu XFan XZhang T(2019)Understanding Faceted Search from Data Science and Human Factor PerspectivesACM Transactions on Information Systems10.1145/328410137:2(1-27)Online publication date: 11-Jan-2019
https://dl.acm.org/doi/10.1145/3284101
Giuzio AMecca GQuintarelli ERoveri MSantoro DTanca L(2019)INDIANAInformation Systems10.1016/j.is.2019.01.00383:C(40-56)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1016/j.is.2019.01.003
Virgil Jeny JRoshini G(2018)A Framework for Dynamic Facet Ordering Search in E-CommerceInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT183828(71-77)Online publication date: 30-Nov-2018
https://doi.org/10.32628/CSEIT183828
Show More Cited By

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents