Research Issues in Web Structural Delta Mining

Web structure mining has been a well-researched area during recent years. Based on the observation that data on the web may change at any time in any way, some incremental data mining algorithms have been proposed to update the mining results with the corresponding changes. However, none of the existing web structure mining techniques is able to extract useful and hidden knowledge from the sequence of historical web structural changes. While the knowledge from snapshot is important and interesting, the knowledge behind the corresponding changes may be more critical and informative in some applications. In this paper, we propose a novel research area of web structure mining called web structural delta mining. The distinct feature of our research is that our mining object is the sequence of historical changes of web structure (also called web structural deltas). For web structural delta mining, we aim to extract useful, interesting, and novel web structures and knowledge considering their historical, dynamic, and temporal properties. We propose three major issues of web structural delta mining, identifying useful and interesting structures, discovering associations from structural deltas, and structural change pattern based classifier. Moreover, we present a list of potential applications where the web structural delta mining results can be used.