Efficient Group Processing for Multiple Reverse Top-k Geo-Social Keyword Queries

Abstract

A Reverse Top-k Geo-Social Keyword Query (RkGSKQ) aims to find all the users who have a given geo-social object in their top-k geo-social keyword query results. This query is practical in detecting prospective customers for online business in social networks. Existing work on RkGSKQ only explored efficient approaches in answering a single query per time, which could not be efficient in processing multiple queries in a query batch. In many real-life applications, multiple RkGSKQs for multiple query objects can be issued at the same time. To this end, in this paper, we focus on the efficient batch processing algorithm for multiple RkGSKQs. To reduce the overall cost and find concurrently results of multiple queries, we present a group processing framework based on the current state-of-the-art indexing and group pruning strategies to answer multiple RkGSKQs by sharing common CPU and I/O costs. Extensive experiments on three data sets demonstrate the effectiveness and efficiency of our proposed methods.