ACTIVITY MINING: FROM ACTIVITIES TO ACTIONS

Author:

CAO LONGBING1,ZHAO YANCHANG1,ZHANG CHENGQI1,ZHANG HUAIFENG1

Affiliation:

1. Faculty of Information Technology, University of Technology, Sydney, P.O. BOX 123, Broadway, NSW 2007, Australia

Abstract

Activity data accumulated in real life, such as terrorist activities and governmental customer contacts, present special structural and semantic complexities. Activity data may lead to or be associated with significant business impacts, and result in important actions and decision making leading to business advantage. For instance, a series of terrorist activities may trigger a disaster to society, and large amounts of fraudulent activities in social security programs may result in huge government customer debt. Uncovering these activities or activity sequences can greatly evidence and/or enhance corresponding actions in business decisions. However, 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 based on case-study experience in the area of social security. Activity mining aims to discover high impact activity patterns in huge volumes of unbalanced activity transactions. Activity patterns identified can be used to prevent disastrous events or improve business decision making and processes. We illustrate the above issues and prospects in mining governmental customer contacts data to recover customer debt.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Science (miscellaneous),Computer Science (miscellaneous)

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2. Identification of Active Valuable Nodes in Temporal Online Social Network with Attributes;International Journal of Information Technology & Decision Making;2014-07

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