Efficient Processing of Distributed Top-k Queries

Ranking-aware queries, or top-k queries, have received much attention recently in various contexts such as web, multimedia retrieval, relational databases, and distributed systems. Top-k queries play a critical role in many decision-making related activities such as, identifying interesting objects, network monitoring, load balancing, etc. In this paper, we study the ranking aggregation problem in distributed systems. Prior research addressing this problem did not take data distributions into account, simply assuming the uniform data distribution among nodes, which is not realistic for real data sets and is, in general, inefficient. In this paper, we propose three efficient algorithms that consider data distributions in different ways. Our extensive experiments demonstrate the advantages of our approaches in terms of bandwidth consumption.