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VLDB 2027: International Conference on Very Large Data Bases

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VLDB
截稿日期:
2027-03-01 还有 219 天
通知日期:
2027-04-15
会议日期:
2027-08-23
会议地点:
Athens, Greece
届数:
CCF: A   ICORE: A*   浏览: 1042788   关注: 237   参加: 24

征稿

VLDB 2027 (International Conference on Very Large Data Bases) is a CCF A / ICORE A* conference held in Athens, Greece on 2027-08-23. The paper submission deadline is 2027-03-01. Acceptance notifications are sent on 2027-04-15.

Overview The Proceedings of the VLDB (PVLDB), established in 2008, is a scholarly journal for short and timely research papers pursuing a strict quality assurance process. PVLDB is distinguished by a monthly submission process with rapid reviews. PVLDB issues are published regularly throughout the year. A paper will appear in PVLDB soon after acceptance, and possibly in advance of the VLDB Conference. All papers accepted for Volume 20 by July 1, 2027 will form the Research Track of the VLDB 2027 Conference, together with any rollover papers from Volume 19. Papers accepted to Volume 20 after July 1, 2027 will be rolled over to the VLDB 2028 Conference. At least one author of each accepted paper must attend the VLDB 2027 Conference. PVLDB is the only submission channel for research papers to appear in the VLDB 2027 Conference. Please see the Submission Guidelines for paper submission instructions. The submission process for other VLDB 2027 tracks, such as demonstrations or tutorials, is different, and is described in their respective calls for papers. Scope of PVLDB PVLDB welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission's topic of the journal's editorial board. Finally, the contributions in the submission should build on work already published in data management outlets, e.g., PVLDB, VLDB Journal, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation. Topics of Interest PVLDB welcomes original research papers on a broad range of topics related to all aspects of data management. The themes and topics listed below are intended to serve primarily as indicators of the kinds of data-centric subjects that are of interest to PVLDB – they do not represent an exhaustive list. Data Management for ML/AI Compilation and optimization in ML systems Data engineering and model management for ML Embeddings and vector databases New data system infrastructures and tools for applied ML Runtime strategies and data access in ML systems Data Mining and Analytics Data mining algorithms for various data types Data stream mining Data warehousing and OLAP Parallel and distributed data mining Data Privacy and Security Access control and privacy Blockchain Privacy-enhancing technologies Database Performance and Manageability Administration and manageability Tuning, benchmarking, and performance measurement DBMS Internals Access methods Concurrency control, recovery, and transactions Memory and storage management Multi-core processing and hardware acceleration Query processing and optimization Views, indexing, and search Distributed Database Systems Cloud data management, resource management, database as a service Data networking and content delivery Distributed analytics Distributed transactions Key-value databases Graph Data Management Graph data models, schemas, and query languages Graph database systems (storage, indexing, query optimization, etc.) Graph schemas and interoperability Knowledge graphs and knowledge management Web data management and Semantic Web Information Integration Data cleaning, data quality, and data preparation Data discovery and search Data lakes and data governance Heterogeneous and federated DBMS Metadata management Schema matching and mapping ML/AI for Data Management Learned algorithms for sorting, compressing, encoding data Learned index structures and storage layouts Learned query processing and optimization LLM-assisted data processing Self-tuning and instance-optimized database systems Network Data Graph algorithms for large-scale analysis Graph mining and pattern discovery Graph-based inference and application analytics Network data analysis (social networks, road networks, hypergraphs, etc.) Novel Database Architectures Data management on novel hardware Embedded and mobile databases Energy-efficient and sustainable data systems Video management and analytics systems Provenance and Workflows Debugging and explainable AI Process mining Profile-based and context-aware data management Provenance management and analysis Schema and Languages Data models and query languages Schema management and design Specialized and Domain-Specific Data Management Crowdsourcing Fuzzy, probabilistic, and approximate data Image and multimedia databases Quantum data management Responsible data management Scientific and medical data management Spatial and temporal databases Text and Semi-Structured Data Data extraction and processing Information retrieval Text in databases Time Series Data Real-time databases, sensors and IoT, stream databases Time series analytics (forecasting, anomaly detection, imputation, classification, clustering, similarity search, etc.) Time series data management and systems User Interfaces Data exploration Database support for visual analytics Database usability Interactive querying and visualization for large data NL interfaces to data
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