KeyGraph-based chance discovery for mobile contents management system

Abstract

Chance discovery provides a way to find rare but very important events for future decision making. It can be applied to stock market prediction, earthquake alarm, intrusion detection and social community evolution modeling. Similarly, contents management procedure can be regarded as a new application of chance discovery. By combining context information with contents, user's trivial behavior at a specific area can result in sudden photo creation, long-time mp3 listening, and going to specific area for content creation. Using chance discovery, such novel situation can be modeled and predictable. Recently mobile devices are regarded as a content storage with their functions such as camera, camcorder, and music player. It creates massive new data and downloads contents from desktop or wireless internet. Because of the massive size of digital contents in the mobile devices, user feels difficulty to recall or find information from the personal storage. We propose a KeyGraph-based reorganization method of mobile device storage for better accessibility to the data based on chance discovery. It can help user not only find useful information from the storage but also refresh his/her memory by using the summary of novel events. User can recall his/her memory from the contents and contexts. Using artificially generated logs from a pre-defined scenario, the proposed method is tested and analyzed to check the possibility.