Continuous Top-k Monitoring on Document Streams (Extended Abstract)

Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Abstract: The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach, and follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a locally adaptive technique, our method offers (i) optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time than the state-of-the-art.