DivIDE: efficient diversification for interactive data exploration

Khan, Hina A., Sharaf, Mohamed A. and Albarrak, Abdullah (2014). DivIDE: efficient diversification for interactive data exploration. In: Christian S. Jensen, Hua Lu, Torben Bach Pedersen, Christian Thomsen and Kristian Torp, SSDBM 2014 - Proceedings of the 26th International Conference on Scientific and Statistical Database Management. 26th International Conference on Scientific and Statistical Database Management, SSDBM 2014, Aalborg, Denmark, (). 30 June-2 July 2014. doi:10.1145/2618243.2618253

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Khan, Hina A.
Sharaf, Mohamed A.
Albarrak, Abdullah
Title of paper DivIDE: efficient diversification for interactive data exploration
Conference name 26th International Conference on Scientific and Statistical Database Management, SSDBM 2014
Conference location Aalborg, Denmark
Conference dates 30 June-2 July 2014
Proceedings title SSDBM 2014 - Proceedings of the 26th International Conference on Scientific and Statistical Database Management
Journal name ACM International Conference Proceeding Series
Series ACM International Conference Proceeding Series
Place of Publication New York, NY, USA
Publisher Association for Computing Machinery (ACM)
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1145/2618243.2618253
Open Access Status
ISBN 9781450327220
Editor Christian S. Jensen
Hua Lu
Torben Bach Pedersen
Christian Thomsen
Kristian Torp
Total pages 12
Collection year 2015
Language eng
Abstract/Summary Today, Interactive Data Exploration (IDE) has become a main constituent of many discovery-oriented applications, in which users repeatedly submit exploratory queries to identify interesting subspaces in large data sets. Returning relevant yet diverse results to such queries provides users with quick insights into a rather large data space. Meanwhile, search results diversification adds additional cost to an already computationally expensive exploration process. To address this challenge, in this paper, we propose a novel diversification scheme called DivIDE, which targets the problem of efficiently diversifying the results of queries posed during data exploration sessions. In particular, our scheme exploits the properties of data diversification functions while leveraging the natural overlap occurring between the results of different queries so that to provide significant reductions in processing costs. Our extensive experimental evaluation on both synthetic and real data sets shows the significant benefits provided by our scheme as compared to existing methods.
Keyword Algorithms
Design
Experimentation
Performance
Query processing
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 05 Aug 2014, 01:59:11 EST by System User on behalf of School of Information Technol and Elec Engineering