Mining mobility data

The fairly recent explosion in the availability of reasonably fast wireless and mobile data networks has spurred demand for more capable mobile computing devices. Conversely, the emergence of new devices increases demand for better net- works, creating a virtuous cycle. The current concept of a smartphone as an always-connected computing device with multiple sensing modalities was brought into the mainstream by the Apple iPhone just a few years ago. Such devices are now seeing an explosive growth. Additionally, for many people in the world, such devices will be the first comput- ers they use. Furthermore, small, cheap, always-connected devices (standalone or peripheral) with additional sensing capabilities are very recently emerging, further blurring the lines between the Web, mobile applications (a.k.a. apps), and the real world. All of this opens up countless possibil- ities for data collection and analysis, for a broad range of applications. In this tutorial, we survey the state-of-the-art in terms of mining mobility data across different application areas such as ads, geo-social, privacy and security. Our tutorial consists of three parts. (1) We summarize the possibilities and challenges in the collection of data from various sensing modalities. (2) We cover cross-cutting challenges such as real-time analysis and security; and we outline cross-cutting algorithms for mobile data mining such as network inference and streaming algorithms. (3) We focus on how all of this can be usefully applied to broad classes of applications, no- tably mobile and location-based social, mobile advertising and search, mobile Web, and privacy and security. We con- clude by showcasing the opportunities for new data collec- tion techniques and new data mining methods to meet the challenges and applications that are unique to the mobile arena (e.g., leveraging emerging embedded computing and sensing technologies to collect a large variety and volume of new kinds of big data"). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for thirdparty components of this work must be honored. For all other uses, contact.