Title

Knowledge discovery from series of interval events

Authors

Authors

R. Villafane; K. A. Hua; D. Tran;B. Maulik

Comments

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Abbreviated Journal Title

J. Intell. Inf. Syst.

Keywords

data mining; knowledge discovery; time series; event sequence; temporal; SEQUENCES; Computer Science, Artificial Intelligence; Computer Science, Information; Systems

Abstract

Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been considered. Such time series are common in many applications. In this paper, we propose mining techniques to discover temporal containment relationships in such series. Specifically, an item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships provides insight about temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.

Journal Title

Journal of Intelligent Information Systems

Volume

15

Issue/Number

1

Publication Date

1-1-2000

Document Type

Article; Proceedings Paper

Language

English

First Page

71

Last Page

89

WOS Identifier

WOS:000087869400005

ISSN

0925-9902

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