Thematic ranking of object summaries for keyword search

In this paper, we propose a model that ranks OSs containing a set of identifying keywords (e.g., Chen) according to their relevance to a set of thematic keywords (e.g. Mining). We argue that the effective thematic ranking of OSs should combine gracefully IR-style properties, authoritative ranking and affinity. Our ranking problem is modeled and solved as a top-k group-by join; we propose an algorithm that computes the join efficiently, taking advantage of appropriate count statistics and compare it with baseline approaches. An experimental evaluation on the DBLP and TPC-H databases verifies the effectiveness and efficiency of our proposal.