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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