Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
7-2008
Abstract
Sequence, widely appearing in various applications (e.g. event logs, text documents, etc) is an ordered list of objects. Exploring correlated objects in a sequence can provide useful knowledge among the objects, e.g., event causality in event log and word phrases in documents. In this paper, we introduce correlation query that finds correlated pairs of objects often appearing closely to each other in a given sequence. A correlation query is specified by two control parameters, distance bound, the requirement of object closeness, and correlation threshold, the minimum requirement of correlation strength of result pairs. Instead of processing the query by scanning the sequence multiple times, that is called Multi-Scan Algorithm (MSA), we propose One-Scan Algorithm (OSA) and Index-Based Algorithm (IBA). OSA accesses a queried sequence once and IBA considers correlation threshold in the execution and effectively eliminates unneeded candidates from detail examination. An extensive set of experiments is conducted to evaluate all these algorithms. Among them, IBA, significantly outperforming the others, is the most efficient.
Discipline
Software Engineering
Research Areas
Software Systems
Publication
Scientific and Statistical Database Management: 20th International Conference, SSDBM 2008, Hong Kong, July 9-11, 2008: Proceedings
Volume
5069
First Page
436
Last Page
454
ISBN
9783540694977
Identifier
10.1007/978-3-540-69497-7_28
Publisher
Springer Verlag
City or Country
Hong Kong
Citation
LEE, Ken C. K.; LEE, Wang-chien; Peuquet, Donna; and ZHENG, Baihua.
Searching correlated objects in a long sequence. (2008). Scientific and Statistical Database Management: 20th International Conference, SSDBM 2008, Hong Kong, July 9-11, 2008: Proceedings. 5069, 436-454.
Available at: https://ink.library.smu.edu.sg/sis_research/378
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1007/978-3-540-69497-7_28