Mobile Context Data Mining

Abstract

The mobile devices, such as iPhone, iPad, and Android are becoming more popular than ever before. Many mobile-based intelligent applications and services are emerging, especially those location-based and context-aware services, e.g. Foursquare and Google Latitude. The mobile device is important since it can detect a user’s rich context information with its in-device sensors, e.g. GPS, Cell ID, and accelerometer. With such data and suitable data mining methods better understanding of users is possible; smart and intelligent services thus can be provided. In this chapter, the authors introduce some mobile context mining applications and methods. To be specific, they first show some typical mobile context data types with a mobile phone which can be detected. Then, they briefly introduce mining methods that are related to two mostly used types of mobile context data, location, and accelerometer. In the following, we illustrate in detail two context data mining methods that process multiple types of context data and can deal with the more general problem of user understanding: how to mine users’ behavior patterns and how to model users’ significant contexts from the users’ mobile context log. In each section, the authors show some state-of-the-art works.