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VLDB 2027: International Conference on Very Large Data Bases

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Submission Date:
2027-03-01 Due in 152 days
Notification Date:
2027-04-15
Conference Date:
2027-08-23
Location:
Athens, Greece
Years:
CCF: A   ICORE: A*   Viewed: 1052600   Tracked: 239   Attend: 25

Conference Partner Index (CP-I)

83.3 / 100
Ranked #155 of 5,687 conferences · Top 3%

#9 of 337 in Data Mining & Databases

Academic recognition (35%)
100
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
100
Community attention (10%)
78
Public record completeness (15%)
70

Inputs used: Listed as CCF A · Editions on record: 53 · Researchers following it here: 239 · Researchers who opened this page in the past 24 months: 26

Missing from the public record: Historical acceptance rates (+4.5)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-30

Call For Papers

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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Best Papers

YearBest Papers
2025AnyBlox: A Framework for Self-Decoding Datasets
2025Diva: Dynamic Range Filter for Var-Length Keys and Queries
2024OmniSketch: Efficient Multi-Dimensional High-Velocity Stream Analytics with Arbitrary Predicates
2024BOSS: An Architecture for Database Kernel Composition
2023DBSP: Automatic Incremental View Maintenance for Rich Query Languages
2023Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples
2022Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs
2022Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks
2022HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
2022Hardware Acceleration of Compression and Encryption in SAP HANA
2021Scaling Attributed Network Embedding to Massive Graphs
2020Opportunities for optimism in contended main-memory multicore transactions
2019Fine-Grained, Secure and Efficient Data Provenance for Blockchain
2018The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
2017Provenance for Natural Language Queries
2016Compressed Linear Algebra for Large-Scale Machine Learning
2015Constructing an interactive natural language interface for relational databases
2014The Uncracked Pieces in Database Cracking
2014epiC: an Extensible and Scalable System for Processing Big Data
2014On k-Path Covers and their Applications
2014Building Efficient Query Engines in a High-Level Language
2014M4: A Visualization-Oriented Time Series Data Aggregation
2013DisC Diversity: Result Diversification based on Dissimilarity and Coverage
2012Dense Subgraph Maintenance under Streaming Edge Weight Updates for Real-time Story Identification
2011RemusDB: Transparent High-Availability for Database Systems
2010Towards Certain Fixes with Editing Rules and Master Data
2009A Unified Approach to Ranking in Probabilistic Databases
2008Constrained Physical Design Tuning
2008Finding Frequent Items in Data Streams
2007Scalable Semantic Web Data Management Using Vertical Partitioning
2006Trustworthy Keyword Search for Regulatory-Compliant Record Retention
2005Cache-conscious Frequent Pattern Mining on a Modern Processor
2004Model-Driven Data Acquisition in Sensor Networks
2001Weaving Relations for Cache Performance
2000PicoDBMS: Scaling Down Database Techniques for the Smartcard
1997Integrating Reliable Memory in Databases
1994Fast Algorithms for Mining Association Rules in Large Databases
1987The R+-Tree: A Dynamic Index for Multi-Dimensional Objects

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