Abstract

The advent of mobile computing and sensing technologies, in conjunction with omni-present and high-speed mobile networks, allow nowadays the capture of human mobility data at an extremely high fidelity. Modern mobile computing services not only have the capacity to store spatio-temporal mobile data, these nowadays also have the capability to process incoming data in near-real time. As a result, we have a better chance to develop effective strategies and build intelligent systems that play critical roles in areas like public health, traffic engineering, urban planning and economic forecasting. On the other hand, detailed movement data often poses a threat to the privacy and security of users and companies, given that mobile devices are associated with real human custodians. One fundamental question is whether human mobility computing and privacy can co-exist under the same roof, given different cultural, religious, legal, technological and socio-economic backgrounds of societies. This panel will explore how the academia and industry are tackling human mobility computing and privacy challenges at a global scale. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management community should contribute. © 2015 IEEE. 2 42 43 "p"Sponsors: Aalborg University et al. IEEE Computer Society IEEE Technical Committee on Data Engineering The National Science Foundation University of Pittsburgh Conference code: 118117

Abstract

Rapid adoption of smartphones and the business success of the Apple App Store have resulted in the rampant growth of mobile applications. Seeking new revenue opportunities from application development has created a gold rush. However, free or very cheap applications constitute a great bulk of the application downloads putting great pricing pressure on the developers. Furthermore, usage statistics suggest that most of the applications have been either one-trick applications or are downright useless, meriting no attention from the user beyond the first day. This is not surprising since cheap prices will dissuade developers from investing large sums of money to continue to develop more sophisticated, high quality applications. Developers have been complaining about the lack of visibility of their applications in stores that are beginning to resemble a high volume warehouse. It is clear that enhancing application discovery and building better marketing tools will be essential for the continued success of the mobile application marketplace and application stores. This paper proposes and investigates techniques for effective discovery of applications by matching user interests with application characteristics, with a special focus on adapting classical data mining techniques to user ratings of the applications. The user ratings are leveraged to make recommendations on potential applications of interest. Publisher's Version