International Conference on Scientific and Statistical Database Management
会议地点: Santa Cruz, CA, USA
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