Knowledge Discovery from Series of Interval Events

Authors: Villafane R.¹; Hua K.A.²; Tran D.³; Maulik B.⁴

Source: Journal of Intelligent Information Systems, Volume 15, Number 1, 8 July 2000 , pp. 71-89(19)

Publisher: Springer

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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.

Keywords: data mining; knowledge discovery; time series; event sequence; temporal

Language: English

Document Type: Regular paper

Affiliations: 1: University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL 32816. villafan@cs.ucf.edu 2: University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL 32816. kienhua@cs.ucf.edu 3: University of Central Florida, School of Electrical Engineering and Computer Science, Orlando, FL 32816. dtran@cs.ucf.edu 4: Oracle Corporation. bmaulik@us.oracle.com

Publication date: 2000-07-08