Activity mining: Challenges and prospects

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4093 LNAI pp. 582 - 593
Issue Date:
2006-01-01
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Activity data accumulated in real life, e.g. in terrorist activities and fraudulent customer contacts, presents special structural and semantic complexities. However, it may lead to or be associated with significant business impacts. For instance, a series of terrorist activities may trigger a disaster to the society, large amounts of fraudulent activities in social security program may result in huge government customer debt. Mining such data challenges the existing KDD research in aspects such as unbalanced data distribution and impact-targeted pattern mining. This paper investigates the characteristics and challenges of activity data, and the methodologies and tasks of activity mining. Activity mining aims to discover impact-targeted activity patterns in huge volumes of unbalanced activity transactions. Activity patterns identified can prevent disastrous events or improve business decision making and processes. We illustrate issues and prospects in mining governmental customer contacts. © Springer-Verlag Berlin Heidelberg 2006.
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