SSDBM 2019 : 31st International Conference on Scientific & Statistical Database Management
Conference Series : Statistical and Scientific Database Management
Call For Papers
SSDBM 2019 – CALL FOR PAPERS
31st International Conference on Scientific & Statistical Database Management
July 23-25, 2019, Santa Cruz, California, USA
UPDATE: Accepted papers and the program are now on the website. Registration is open!
The SSDBM international conference brings together scientific domain experts, database researchers, practitioners, and developers for the presentation and exchange of current research results on concepts, tools, and techniques for scientific and statistical databases and applications. The 31st SSDBM provides a forum for original research contributions and practical system designs, implementations and evaluations. The program of the research track will be supplemented with invited talks and demonstrations.
SSDBM 2019 will continue the tradition of past SSDBM meetings in providing a stimulating environment to encourage discussion and exchange of ideas on all aspects of research related to scientific and statistical data management.
All accepted papers will be published by ACM – International Conference Proceedings Series (ICPS) and will be available in the ACM Digital Library.
TOPICS OF INTEREST
SSDBM 2019 will have a focus on high-performance data analysis tools and techniques for large data sets, with a special emphasis on genomics, astrophysics, and high-energy physics. The conference encourages authors to make their experimental results reproducible and include reproducibility experiences in their submissions .
Topics of particular interest include, but are not limited to, the following, as they relate to scientific and statistical data management:
System architectures for scientific and statistical data management and analysis
Querying of scientific data, including spatial, temporal, and streaming data
Mining and analysis of large-scale datasets, especially on new and emerging hardware and environments:
- Data flow management in high performance computing
- Techniques for comparing simulation and experimental data
- Cloud computing issues in large-scale data management
- Provenance data management
- Design, implementation, optimization, and reproducibility of scientific workflows
- Integration and exchange of data, including the federation and management of institutional data repositories
- Visualization and exploration of large datasets
- Information retrieval and text mining
- Knowledge discovery, clustering, graph analysis
- Case studies, particularly those at scale-of-consequence for genomics, astrophysics, and high-energy physics
- Stream data management, e.g., storage, organization, compression, indexing and querying
- Stream data analysis, e.g., summarization, statistical analysis, pattern matching, pattern discovery, learning, and prediction
- Modeling and representation of streaming data
We solicit papers describing original work relevant to the management of scientific and statistical data and not published or under review elsewhere. SSDBM 2019 is single-blind reviewed. Therefore, authors must include their names and affiliations on the first page. SSDBM submissions can be research, reproducibility study, or demo papers:
RESEARCH PAPERS (LONG and SHORT): We solicit both full papers (12 pages) and short papers (4 pages). The former tend to be descriptions of complete technical work, while the latter tend to be descriptions of interesting, innovative ideas, which nevertheless require more work to mature. The program committee may decide to accept some full papers as short papers. Full papers will be given a presentation slot in the conference, while short papers will be presented in the form of posters. All papers, regardless of size, will be given an entry in the conference proceedings. Authors may optionally include reproducibility information that allows for automated validation of experimental results (see artifact evaluation criteria). Accepted submissions passing automated validation will earn a prestigious “Results Replicated” Badge in the ACM DL in accordance with ACM’s artifact review and badging policy.
NEW! REPRODUCIBILITY STUDY PAPERS: We also call for reproducibility studies (12 pages) that for the first time reproduce experiments from papers previously published in SSDBM or in other peer-reviewed conferences with similar topics of interest (see reproducibility study instructions). Reproducibility study submissions are selected by the same peer-reviewed competitive process as regular research papers, except these submissions must pass automated validation of experimental results (see artifact evaluation criteria). Accepted submissions passing automated validation will earn the prestigious ACM “Results Replicated” Badge and, if the work under study was successfully reproduced, the associated paper will earn the ACM “Results Reproduced” Badge in the ACM DL in accordance with ACM’s artifact review and badging policy.
DEMO PAPERS: We solicit demonstration proposals (4 pages) which should provide the motivation for the demonstrated concepts, the information about the technology and the system to be demonstrated (including a system description, functionality and figures when applicable), and should state the significance of the contribution. Selection criteria for the demonstration proposals evaluation include: the novelty, the technical advances and challenges, and the overall practical attractiveness of the demonstrated system. Demo papers will also be given an entry in the conference proceedings.
The submission website is https://easychair.org/conferences/?conf=ssdbm19. Submissions are accepted in PDF format using the new ACM proceedings LATEX or Word templates. Authors should use the sigconf proceedings template. Please see instructions at the ACM web site: http://www.acm.org/publications/article-templates/proceedings-template.html. If you are encountering any problems using the LATEX templates, please contact firstname.lastname@example.org.
The following deadlines apply to long, short and demo papers:
Paper submission: March 11, 2019, 11:59pm AoE (Anywhere on Earth)
Notification of acceptance: April 26, 2019
Camera ready copy: June 3, 2019
General Chair: Carlos Maltzahn, University of California, Santa Cruz
Program Committee Chair: Tanu Malik, DePaul University, Chicago
Reproducibility Chair: Ivo Jimenez, University of California, Santa Cruz
Local Arrangements Chair: Lavinia Preston, University of California, Santa Cruz
Gagan Agrawal, Ohio State University
Peter Baumann, Jacobs University, Bremen
Khalid Bellhajahme, University Paris-Dauphine
Souvik Bhattacharjee, University of Maryland, College Park
Tamas Budavari, Johns Hopkins University
Lazlo Doblos, Eötvös Loránd University, Budapest
Shawfeng Dong, SLAC National Accelerator Laboratory
Ahmed Eldawy, University of California, Riverside
Thomas Heinis, Imperial College, UK
Ashish Gehani, SRI International
Boris Glavic, Illinois Institute of Technology, Chicago
Pascal Grosset, Los Alamos National Laboratory
Ian Foster, University of Chicago, Chicago
Verena Kantere, University of Ottawa, Ontario
Jeff LeFevre, University of California, Santa Cruz
Ulf Leser, Humboldt-Universität zu Berlin
Qing Gary Liu, New Jersey Institute of Technology
Marjan Mernik, University of Maribor
Paolo Missier, Newcastle University
Beth Plale, Indiana University, Bloomington
Neolkis Polyzotis, Google, Inc.
Dave Pugmire, Oakridge National Laboratory
Maya Ramanath, Indian Institute of Technology, Delhi
Alexander Rasin, DePaul University, Chicago
Tore Risch, Uppsala University
Florin Rusu, University of California, Merced
Iulian Sandu-Popa, University of Versailles Saint-Quentin and INRIA
Galen Shipman, Los Alamos National Laboratory
Douglas Thain, University of Notre Dame
Srikanta Tirthapura, Iowa State University
Yicheng T,u University of South Florida, Tampa
Jon Woodring, Los Alamos National Laboratory
K. John Wu, Lawrence Berkeley National Laboratory
Hongfeng Yu, University of Nebraska, Lincoln
Xuechen Zhang, Washington State University
Ming Zhao, Arizona State University, Phoenix
Qiang Zhi, University of Michigan, Dearborn