Ranking & Metrics Conference Call for Papers Other Conferences in United States
ACM SIGMOD/PODS International Conference on Management of Data

ACM SIGMOD/PODS International Conference on Management of Data

Seattle, United States

Submission Deadline: Saturday 15 Oct 2022

Conference Dates: Jun 18, 2023 - Jun 23, 2023

Research
Impact Score 10.06

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Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing the best scientists in addition to the h-index estimated from the scientific papers published by the best scientists. See more details on our methodology page.

Research Impact Score: 10.06
Contributing Best Scientists:
H5-index: 66
Papers published by Best Scientists
Research Ranking (Computer Science) 24

Conference Call for Papers

There are three research tracks in SIGMOD 2023:

Regular Track
We invite the submission of original research contributions relating to all aspects of data management.
Data Management for Data Science (DMDS) Special Track
We invite the submission of original data science research targeting the data life cycle of real applications, studying phenomena at scales, complexities, and granularities never before possible. This data life cycle encompasses databases/data management/data systems/data engineering often leveraging statistical, machine learning, and artificial intelligence methods and, in many instances, using massive and heterogeneous collections of potentially noisy datasets. Such papers are expected to focus on data-intensive components of data science pipelines; and solve problems in areas of interest to our community (e.g., data curation, optimization, performance, storage, systems). Submissions are expected to describe (a) deployed solutions to data science pipelines and/or (b) fundamental experiences and insights from evaluating real-world data science problems. We expect that the related systems and/or datasets (including possibly query logs) will be accessible for the data management research community in order to promote future research directions.
Data-intensive Applications (DIA) Special Track
We invite the submission of papers, from outside of the data management community, describing applications, systems, and datasets (e.g. content, creation, quality), along with the underlying practical data management problems and related research challenges. These applications stem from outside the core data management community (e.g., computer graphics, computer networking) or even from outside computer science (e.g., astronomy, finance, genomics, healthcare), but have clearly demonstrated non-trivial data-centric challenges necessitating novel systems and technologies. Therefore, papers are expected to have as the primary author a researcher or practitioner from a different research community or application area. Submissions are expected to describe (a) deployed solutions to data-intensive applications and/or (b) fundamental experiences and insights from evaluating real-world data-intensive applications. We expect that the relat ed systems and/or datasets (including possibly query logs) will be accessible for the data management research community in order to promote future research directions.
We invite submissions relating to all aspects of the data life cycle. Topics of interest include, but are not limited to:

Benchmarking, database monitoring, and performance tuning
Cloud data management and HPC
Crowdsourced and collaborative data management
Data models and semantics
Data provenance and workflows
Data exploration, visualization, query languages, and user interfaces
Data integration, information extraction, and schema matching
Data quality, data cleaning, and database usability
Data warehousing, OLAP, SQL Analytics
Data security, privacy, and access control
Data sparsity, boosting, simulated data, and digital twins
Data platforms for emerging hardware/Emerging hardware for data management
Data systems for knowledge discovery, data mining, machine learning, and artificial intelligence
Distributed, decentralized, and parallel data management, distributed ledgers, and blockchainsGraphs, social networks, and semantic web
Machine learning and artificial intelligence for data management and data systems
Multimedia and information retrieval
Query processing and optimization
Responsible data management and data fairness
Self-driving databases
Semistructured, partially structured, and unstructured data
Sensor networks and IoT
Spatial data management
Storage, indexing, and physical database design
Streams and complex event processing
Temporal databases
Transaction processing
Uncertain, probabilistic, and approximate databases

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