Efficient Data Mining for Calling Path Patterns in GSM Networks

• Main Authors: Yao-Te Wang, 王耀德
• Other Authors: Anthony J.T. Lee
• Format: Others
• Language: en_US
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/88660551240606674417

博士 === 國立臺灣大學 === 資訊管理研究所 === 91 === With the volumes of applications for mobile computing, the moving paths of users reveal much more valuable for the location-based services. In this dissertation, we explore two data mining capabilities that involve mining both primitive and periodic potential maximal frequent calling path patterns in GSM networks. Our proposed methods consist of two phases. First, we devise a data structure to convert the original calling paths in the log file into a frequent calling path graph. Second, we design an algorithm to mine the calling path patterns from the frequent calling path graph obtained. By using the frequent calling path graph to mine the calling path patterns, our proposed algorithm does not generate unnecessary candidate patterns and requires less database scans. If the corresponding calling path graph of the GSM network can be held in the main memory, our proposed algorithm scans the database only once. Otherwise, the cellular structure of the GSM network is divided into several partitions so that the corresponding calling path sub-graph of each partition can be held in the main memory. The number of database scans for this case is equal to the number of partitioned sub-graphs. Therefore, our proposed algorithm is more efficient than the PrefixSpan and Apriori-like approaches. The experimental results show that our proposed algorithm outperforms the Apriori-like and PrefixSpan approaches by several orders of magnitude.