SIGMOD 2021 : International Conference on Management of Data (2nd Cycle)

The annual ACM SIGMOD conference is a leading international forum for data management researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences.

There are three research tracks in SIGMOD 2021:

Data Management Track
We invite the submission of original research contributions relating to all aspects of data management.
Data Science and Engineering Track
We invite the submission of original research in data science and engineering, inspired by real applications. Such papers are expected to focus on data-intensive components of data science pipelines; and solve problems in areas of interest to the community (e.g., data curation, optimization, performance, storage, systems).
Applications Track
We invite the submission of novel applications of data management systems and technologies from outside the core data management community (e.g., astronomy, computer graphics, computer networking, genomics).
HIGHLIGHTS
Paper submission deadlines: Tue July 7, 2020 (Round 1), Tue September 22, 2020 (Round 2)
Submission website: https://cmt3.research.microsoft.com/SIGMOD2021 (open for submission starting June 23, 2020 for Round 1 and September 8, 2020 for Round 2).
Submissions must use the latest ACM format in the default 9pt font.
Data Management submissions must be at most 12 pages plus unlimited number of pages for citations.
Data Science and Engineering submissions must be at most 8 pages plus unlimited number of pages for citations.
Applications submissions must be at most 4 pages plus unlimited number of pages for citations.
TOPICS OF INTEREST
Topics of interest include but are not limited to the following:

Benchmarking and performance evaluation
Crowdsourcing
Data models, semantics, query languages
Data provenance
Data visualization
Data warehousing, OLAP, SQL Analytics
Database monitoring and tuning
Database security, privacy, access control
Database usability
Databases for emerging hardware
Data Systems and Data Management for Machine Learning
Distributed and parallel databases
Graph data management, RDF, social networks
Information extraction
Information retrieval and text mining
Knowledge discovery, clustering, data mining
Machine learning for data management and data systems
Query processing and optimization
Schema matching, data integration, and data cleaning
Scientific databases
Semi-structured data
Spatio-temporal databases
Storage, indexing, and physical database design
Streams, sensor networks, complex event processing
Transaction processing
Uncertain, probabilistic, and approximate databases
Very large data science applications/pipelines
SIGMOD welcomes submissions on inter-disciplinary work, as long as there are clear contributions to management of data.