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A complete chronicle discovery approach: application to activity analysis
Author(s) -
Cram Damien,
Mathern Benoît,
Mille Alain
Publication year - 2012
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2011.00591.x
Subject(s) - computer science , business process discovery , knowledge extraction , process (computing) , data mining , drug discovery , k optimal pattern discovery , sequence (biology) , false discovery rate , artificial intelligence , information retrieval , theoretical computer science , data science , bioinformatics , business process , work in process , programming language , genetics , marketing , business process modeling , biology , business , biochemistry , chemistry , gene
Absrtact Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non‐complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process.