Never before it was so easy and inexpensive to gather data in amounts which were beyond imagination only a few years in the past. However, as we all are aware, this richness in data goes hand in hand with a poverty in insight, as data understanding cannot keep up with this data deluge. Today, this phenomenon is not confined to highly specialized applications like particle physics at CERN - all areas suffer, from science and engineering over business and administration to society at large, with imminent implications for all of us. There was and is a severe need for theories, methods, tools, and best practices that help us cope with the "volume, velocity, variety, and veracity" of data.
The aim of the International Conference on Scientific and Statistical Database Management (SSDBM) series is to bring database researchers, practitioners and developers together with scienfic domain experts to exchange the most recent research results on database techniques, concepts, tools and applications for scientific and statistical applicat-ions. The 28th SSDBM took place in Budapest, Hungary, between June 18-20, 2016, organized by the Hungarian Academy of Sciences Wigner Research Centre for Physics.
The conference this year had ten sessions, five for presenting research papers, two for keynote talks, one dedicated to demonstrations and poster viewing, a tutorial session, and a panel discussion. The research and poster committee has enjoyed the variety and interest of the submissions, which came from quite different angles and sub-disciplines of the broad areas of statistical and scientific data management. Altogether, 63 papers were submitted for review out of which 21 were accepted as full papers, three as posters and four as demonstrations. The full paper acceptance rate thus was 33%, the overall acceptance rate was 44%. An innovation this year was the introduction of a tutorial session to advocate tools and technologies which might be of wide interest to the research community. The tutorial on array databases has solicited keen interest, given the prevalence of array data in applications from the scientific domain (think time series or sequences of biological observations) as well as beyond (financial and transport data etc). The committee also decied to provide an opportunity to students participating and the conference to present non-peer-reviewed research posters in a special section so as to lower the entry barrier into scientific publishing.
The keynote speakers were invited to represent the two main areas of scientific data research: data-intensive system development and scientific applications. Prof. Volker Markl from the Technical University Berlin gave a keynote speech about Apache Flink, an open source scalable system for batch and stream processing developed by a team under his supervision, an how this system helps researchers implement deep data analysis workflows by automatic parallelization, optimization and efficient execution. Prof. István Csabai from the Eötvös Loránd University of Buda-pest discussed the challenges of massive data analysis in a wide range of scientific, fields from cosmology via gen-omics to social sciences.
In an interdisciplinary panel titled Bye Bye Big Data - all problems solved, finally? a lively discussion took place on the state of affairs in Big Data, how much the database field is contributing visibly, and where future avenues in terms of research areas and technological contributions can be found.
The conference organizers are grateful to all paper authors for the high-quality submissions, and to the research program committee, the demo committee, and the external reviewers for the thorough and timely reviews.
We hope you will find, like we do, the program of this year's conference interesting, inspiring and relevant for the scientific and statistical data management community.
Peter Baumann - General Chair
Ioana Manolescu - Program Chair
P Baumann, I Manolescu-Goujot, L Trani, Y Ioannidis, G G Barnaföldi, L Dobos, E Banyai. Proceedings of the 28th International Conference on Scientific and Statistical Database Management