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EDBT 2017 Awards
We are very pleased to announce the following EDBT 2017 Awards:
EDBT 2017 Test of Time Award
In 2014, the Extended Database Technology conference (EDBT http://www.edbt.org) began awarding the EDBT test-of-time (ToT) award, with the goal of recognising papers presented at EDBT Conferences that have had the most impact in terms of research, methodology, conceptual contribution, or transfer to practice.
The EDBT ToT award for 2017 will be presented during the EDBT/ICDT 2017 Joint Conference, March 21-24, 2017 - Venice, Italy (http://edbticdt2017.unive.it).
The EDBT 2017 Test of Time Award committee was formed by:
Gustavo Alonso, ETH Zurich, Switzerland
Sihem Amer-Yahia, LIG, CNRS Grenoble, France
Ralf Hartmut Güting, University of Hagen, Germany
Volker Markl, TU Berlin, Germany
Peter Triantafillou, University of Glasgow, UK
The committee was asked to select a paper from the proceedings of previous editions of EDBT, specifically EDBT 1996, 1998, 2000 and 2002. After careful consideration, the committee and the EDBT executive board have decided to select the following paper as the EDBT ToT Award winner for 2017:
by Ramakrishnan Srikant and Rakesh Agrawal.
In Proceedings of EDBT 1996, LNCS 1057, pp 1-17, Springer.
This paper has made substantial contributions to Data Mining, and has had great influence on the work of others, as reflected by 2911 citations of this paper alone on Google Schoolar. The paper formalizes a new variant of the problem of mining sequential patterns and discusses GSP, an algorithm to solve this problem. Specifically, the paper extends the definition of sequence mining, which was introduced by the same authors in a previous and highly cited publication: "Mining Sequential Patterns", ICDE 1995. The goal of sequence mining is to discover all sequential patterns with a user-specified minimum support from a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items.
The extensions proposed in the EDBT 2017 ToT paper are:
- Time constraints: the authors generalized their previous definition of sequential patterns to admit max-gap and min-gap time constraints between adjacent elements of a sequential pattern.
- Sliding windows: the authors relaxed the restriction that all the items in an element of a sequential pattern must come from the same transaction, and allowed a user-specified window-size within which the items can be present.
- Taxonomies: the sequential patterns may include items across different levels of a taxonomy.
GSP guarantees that all rules that have a user-specified minimum support are extracted. It is shown to be much faster than the AprioriAll algorithm in the previous publication (on both synthetic and real data). GSP has been implemented as part of the Quest data mining prototype at IBM Research, and is incorporated in the IBM data mining product.
EDBT 2017 Best Paper Award
The EDBT 2017 Best Paper Award committee was formed by:
Stefano Ceri, Politecnico di Milano, Italy
Rainer Gemulla, Universität Mannheim, Germany
Volker Markl, TU Berlin, Germany
Wang-Chiew Tan, Recruit Institute of Technology and UCSC, USA
by Marc Seidemann and Bernhard Seeger.
The paper proposes a novel log-based database system for durably storing events with very high arrival rates (e.g., as arising in Internet of Things applications). ChonicleDB achieves high query performance and fast recovery in case of faults, thereby addressing an important gap in the current system landscape. The paper combines established approaches with novel ideas, including a storage layout designed to avoid random I/Os, efficient handling of out-of-order events, an integrated index that exploits temporal correlation, and generally a careful design of what to keep in memory and what to store on disk (and when). The paper also includes a thorough evaluation and comparison with known open-source and commercial systems. The committee considers this work to be an outstanding, foundational systems paper with a very high potential impact.
EDBT 2017 Best Demonstration Award
The EDBT 2017 Best Demonstration committee was formed by:
Angela Bonifati, Lyon 1 University, France
Alfredo Cuzzocrea, University of Trieste, Italy
Stefan Dessloch, TU Kaiserslautern, Germany
Kai-Uwe Sattler (Chair), TU Ilmenau, Germany
Norman Paton, University of Manchester, UK
by Jonas Traub, Nikolaas Steenbergen, Philipp Grulich, Tilmann Rabl, and Volker Markl.
The demo presents an interactive development environment for real-time visualization of stream analytics that coordinates running cluster applications and corresponding visualization. By coordinating the visualization properties, such as filter predicates, window properties or aggregates, and the analysis program running on a cluster the data to be transferred between cluster and visualization can be minimized. The committee has selected this demo for the award because of its innovative idea to the problem of visualizing data streams and its interesting combination of demonstrating a data management system with an attractive and interactive user interface.