VLDB 2021: Call for Contributions - Research Track

VLDB 2021 invites submissions of original research papers to the Proceedings of the VLDB Endowment (PVLDB). Every submission can fall into one of the following categories, each one having different page limits:

Paper Category Page limit
Regular Research Papers Up to 12 pages, excluding references
Scalable Data Science Papers Up to 8 pages, excluding references
Experiment, Analysis and Benchmark Papers Up to 12 pages, excluding references
Vision Papers Up to 6 pages, excluding references

The details of the official PVLDB Call for Contributions for VLDB 2021, are listed at the official website: http://vldb.org/pvldb/contributions_vol14.html

The conference management tool for the submission of abstracts, papers, and supplemental material is accessible at: https://cmt3.research.microsoft.com/PVLDBv14_2021

Relevant Themes and Topics

The relevant 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 and they do not represent an exhaustive list

Data mining and analytics

  • Data Warehousing, OLAP, Parallel and Distributed Data Mining
  • Mining and Analytics for Scientific and Business data, Social Networks, Time Series, Streams, Text, Web, Graphs, Rules, Patterns, Logs, and Spatio-temporal Data

Data Privacy and Security

  • Blockchains
  • Access Control and Privacy

Database Engines

  • Access Methods, Concurrency Control, Recovery and Transactions
  • Hardware Accelerators
  • Query Processing and Optimization
  • Storage Management, Multi-core Databases, In-memory Data Management
  • Views, Indexing and Search

Database performance

  • Tuning, Benchmarking and Performance Measurement
  • Administration and Manageability

Distributed Database Systems

  • Content Delivery Networks, Database-as-a-service, and Resource Management
  • Cloud Data Management
  • Distributed Analytics
  • Distributed Transactions

Graphs, Networks, and Semistructured Data

  • Graph Data Management, Recommendation Systems, Social Networks
  • Hierarchical, Non-relational, and other Modern Data Models

Information Integration and Data Quality

  • Data Cleaning, Data Discovery and Data Exploration
  • Heterogeneous and Federated DBMS, Metadata Management
  • Web Data Management and Semantic Web
  • Knowledge Graphs and Knowledge Management


  • Data Models and Query Languages
  • Schema Management and Design

Machine Learning, AI and Databases

  • Data Management Issues and Support for Machine Learning and AI
  • Machine Learning and Applied AI for Data Management

Novel DB Architectures

  • Embedded and Mobile Databases
  • Real-time databases, Sensors and IoT, Stream Databases
  • Crowd-sourcing

Provenance and Workflows

  • Profile-based and Context-Aware Data Management
  • Process Mining

Specialized and Domain-Specific Data Management

  • Fuzzy, Probabilistic and Approximate Data
  • Image and Multimedia Databases
  • Spatial Databases and Temporal Databases
  • Scientific and Medical Data Management

Text, Semi-Structured Data, and IR

  • Information Retrieval
  • Text in Databases
  • Data Extraction

User Interfaces

  • Database Usability
  • Visualization