Special issue on best papers of VLDB 2012

Abstract

726 participants. The conference received a record number of submissions as compared to previous years of VLDB.• The Research Track received 659 submissions, of which 134 papers (20 %) were accepted. Out of 134 accepted papers, 78 were accepted in the first round, and 56 were accepted after they were resubmitted with revisions and a second round of reviewing. In addition, VLDB 2012 also included three keynote talks, pre-and post-conference workshops, tutorials, and panels. The journal versions of seven best papers that appeared in the conference are included in this special issue of the VLDB Journal. These invited papers are substantially improved, revised, and extended as compared to their original conference versions and were accepted for publication after several rounds of journal-style reviewing. These papers cover a wide range of current database research topics, namely realtime graph data management, graph querying and indexing, social tagging of web data, probabilistic data consistency, adaptive indexing for transactional workloads, incremental view maintenance under high-rate updates, and storage provisioning.The first paper of this special issue "Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification" (by Albert Angel, Nick Koudas, Nikos Sarkas, Divesh Srivastava, Michael Svendsen, and Srikanta Tirthapura) is also selected for the best paper award for VLDB 2012 conference. This paper addresses the problem of real-time story identification using social media data, such as daily blog posts and status updates, posted by millions of people around the globe. The main challenge the authors address is the efficient maintenance of dense subgraphs corresponding to groups of tightly coupled entities, under streaming updates of edge weights. Based on novel theoretical results on the amount of change resulting from a single edge weight update, authors present an algorithm for dense subgraph maintenance and demonstrate its performance with a thorough experimental evaluation.