PROJECT TITLE :

Continuous Top-k Monitoring on Document Streams - 2017

ABSTRACT:

The efficient processing of document streams plays an necessary role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting finish-users with the most relevant content to their preferences. In this work, user preferences are indicated by a collection of keywords. A central server monitors the document stream and continuously reports to every user the prime-k documents that are most relevant to her keywords. Our objective is to support giant numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. Instead, it follows an identifier-ordering paradigm that suits higher the character of the matter. When complemented with a completely unique, regionally adaptive technique, our technique offers (i) proven optimality w.r.t. the amount of thought-about queries per stream event, and (ii) an order of magnitude shorter response time (i.e., time to refresh the query results) than this state-of-the-art.