To appear at ERCIM News No. 54 (July 2003) - Applications and Service Platforms for the Mobile User IN SEARCH OF KNOWLEDGE ABOUT MOBILE USERS Ee-Peng Lim, Yida Wang, Kok-Leong Ong Center for Advanced Information Systems Nanyang Technological University Blk N4, #2a-32, Nanyang Avenue, SINGAPORE 639798 aseplim@ntu.edu.sg, wyd66@pmail.ntu.edu.sg, ongkl@pmail.ntu.edu.sg San-Yih Hwang Department of Information Management National Sun Yat-Sen University, Kaohsiung, Taiwan 80424 syhwang@mis.nsysu.edu.tw Mobile phones and other mobile devices are fast becoming indispensible in our modern society. According to a recent survey by Frank N. Magid Associates and Upoc.com, 59 percent of Americans age 12 and over (about 140 millions of them) own mobile phones, and that almost a quarter of non-owners plan to buy a mobile phone in the near future [1]. The sales of mobile phones worldwide was 385 millions in 2001 and it has been predicted to reach 675 million in 2006 [2]. In tandem to this growth trend, we also witness the emergence of many new applications and businesses that exploit mobile phone technologies in different ways [3,4,5]. Before other wearable computing gadgets become more feasible and popular, mobile phones are likely to remain as the more dominant wearable devices in the coming years. Mobile phones, unlike computers connected to wired networks, are highly personalizable. While it is common for a user to own a few mobile phones, it is very unlikely for different users to share a mobile phone. Also unlike other personalized accessories such as watches, walkmans, etc., many of the mobile phones are trackable. They are trackable because they have to maintain regular contacts with the mobile telecommunication networks in order to receive and make calls. With these trackability and personalization features, one can conceive many unique and interesting mobile applications for end users. At the Center for Advanced Information Systems, we conduct extensive research on using knowledge discovery techniques such as frequent pattern mining, classification, and clustering to create new mobile applications or to enhance existing mobile applications. Examples of such mobile applications include e-commerce systems, databases, email systems, search engines and web browsers. We investigate both the functionalities and operational efficiencies of mobile applications, and determine the kinds of knowledge required for enhancing them. We also develop algorithms for discovering knowledge from mobile user data and evaluate their performance. We further study how the discovered knowledge and mining algorithms can be integrated with the operational systems turning the knowledge into actions. In the e-commerce application domain, we envisage the importance of using mobile user movement data to derive knowledge about mobile users. As users’ purchase behaviors are often highly correlated to their group affiliations, knowing the latter well will allow e-commerce vendors to develop group-specific pricing models and marketing strategies to better meet the buying needs