Guest Editorial: Special issue on VLDB 2019
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EDITORIAL
Guest Editorial: Special issue on VLDB 2019 Fatma Özcan1 · Lei Chen2
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
This is the special issue of best papers selected from VLDB 2019 Conference, which was held in Los Angeles, California, from August 26 to August 30, 2019. VLDB 2019 covered many aspects of data management and analytics, including data integration, cloud databases, distributed transactions, query processing and optimization, crowdsourcing, graph analytics, scalable machine learning, and distributed systems. VLDB 2019 received 677 research submissions. Out of 677 submissions includes 587 regular research papers, 8 vision papers, 31 innovation systems and applications papers, and 51 experimental and analysis papers. The Review Board, Associate Editors, and Editors-in-Chief of PVLDB volume 12 have worked very hard and have selected 128 papers to be presented at VLDB 2019, with an acceptance rate of 18.9%. Based on the recommendation from the associate editors and the program chairs, seven outstanding papers were selected as best paper candidates from the accepted ones. The Best Paper Selection Committee, consisting of M. Tamer Özsu (chair), Divesh Srivastava, Ashraf Aboulnaga, Georgia Koutrika, Fatma Özcan, and Lei Chen, reviewed all the seven papers thoroughly and selected the best paper awards for the conference. We invited the authors of the seven selected papers to submit an extended version of their papers, and five of them submitted to this special issue. The reviewers for the manuscripts submitted to the journal were a mix of those who had originally reviewed the conference versions, as well as additional experts who reviewed only the extended submissions. After two rounds of reviewing, all five papers were accepted for publication in this issue, covering a diverse spectrum of topics ranging from distributed consistency and concurrency control, cloud storage scheme to data explanation tools, and subjective database querying. In the paper “Autoscaling Tiered Cloud Storage in Anna”, C. Wu, V. Sreekanti and J. M. Hellerstein proposed a novel
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Lei Chen [email protected]
1
IBM Research - Almaden, San Jose, USA
2
Hong Kong University of Science and Technology, Kowloon, Hong Kong
solution to extend, Anna, a distributed key-value store into an autoscaling, multi-tier service for the cloud. The goals of traditional cloud storage services lead to poor cost-performance trade-offs for applications. To improve performance, developers are inhibited by two key types of barriers, costperformance barriers and static deployment barriers. As a distributed key-value store, Anna was initially developed based on a fully shared-nothing, thread-per-core architecture with background gossip protocol across the cores and nodes. The authors have extended Anna by incorporating the following new designs: multi-master selective replication, a vertical tiering of storage layers and elasticity of each tier, making it to dynamically adjust configuration and match resources to workloads, thus, ove
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