Call for Papers

Submission Guidelines

Authors are invited to submit original, unpublished manuscripts. We solicit research papers (long and short), and demo papers. All submissions should be formatted according to the ACM Master Article “sigconf” proceedings template. SSDBM 2021 is single-blind reviewed; authors must include their names and affiliations on the first page.

The submission site is on Easychair.

The submission deadline is March 7, 2021 March 21, 2021 at 23:59 AoE (Anywhere on Earth) time.

RESEARCH PAPERS (LONG and SHORT)

Long papers are up to 12 pages (including references and appendices), and short papers are up to 4 pages (including references and appendices). The former should be descriptions of complete technical work, while the latter should describe interesting, innovative ideas, which nevertheless require more work to mature, or are vision papers. The program committee may decide to accept some long papers as short papers. Long 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.

DEMO PAPERS

Demo papers are up to 4 pages (including references and appendices). Proposals 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 be given an entry in the conference proceedings.

Topics of Interest

Authors are invited to submit original, unpublished manuscripts. 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:

• Database support of machine learning and AI
• Modeling of scientific data
• Indexing and querying scientific data, including spatial and temporal data
• Provenance data management
• Data integration
• Visualization and exploration of large datasets
• Spatial, temporal, and spatio­temporal scientific data
• Geographical information retrieval
• Stream data representation and management
• Stream data analysis, e.g., summarization, statistical analysis, pattern matching, pattern discovery, learning, and prediction
• Design, implementation, optimization, and reproducibility of scientific workflows
• Security and privacy
• Cloud computing issues in large­scale data management
• Information retrieval and text mining
• System architectures
• Case studies (e.g., astrophysics, climate, energy, sustainability, biomedicine)
• Distributed systems and devices
• Internet of Things data analytics
• Smart city applications and services