会议信息

VLDB 2026: International Conference on Very Large Data Bases

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截稿日期:
2026-03-01
通知日期:
2026-04-15
会议日期:
2026-08-31
会议地点:
Boston, Massachusetts, USA
届数:
52
CCF: a   CORE: a*   浏览: 1002462   关注: 238   参加: 24

征稿

VLDB 2026 (International Conference on Very Large Data Bases) is a CCF A / CORE A* conference held in Boston, Massachusetts, USA on 2026-08-31. The paper submission deadline is 2026-03-01. Acceptance notifications are sent on 2026-04-15.

The Very Large Data Bases (VLDB) is a premier annual international forum for data management, scalable data science, and database researchers, vendors, practitioners, application developers, and users. The forthcoming VLDB 2026 conference is poised to deliver a comprehensive program, featuring an array of research talks, keynote and invited talks, panels, tutorials, demonstrations, industrial tracks, and workshops. It will cover a spectrum 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. Key areas of interest for PVLDB include, but are not limited to, data mining and analytics, data privacy and security, database engines, database performance and manageability, distributed database systems, graph and network data, information integration and data quality, languages, machine learning, ai, and databases, novel database architectures, provenance and workflows, specialized and domain-specific data management, text and semi-structured data, and user interfaces. These thematic pillars represent the foundational elements underpinning the technological landscape of the emerging applications of the 21st century. The VLDB 2026 conference will take place from Aug 31st to 4th September, 2026 in Boston, MA, USA.
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最佳论文

时间最佳论文
2023DBSP: Automatic Incremental View Maintenance for Rich Query Languages
2023Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples
2022Hardware Acceleration of Compression and Encryption in SAP HANA
2022HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
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
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
2014Building Efficient Query Engines in a High-Level Language
2014The Uncracked Pieces in Database Cracking
2014epiC: an Extensible and Scalable System for Processing Big Data
2014On k-Path Covers and their Applications
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
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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