Distributed and parallel database technology has been the subject of intense research and development effort. Numerous practical application and commercial products that exploit this technology also exist. Since the mid-1990s, web-based information management has used distributed and/or parallel data management to replace their centralized cousins. The maturation of the field, together with the new issues that are raised by the changes in the underlying technology, requires a central focus for work in the area. Distributed and Parallel Databases provides such a focus for the presentation and dissemination of new research results, systems development efforts, and user experiences in distributed and parallel database systems.

Distributed and Parallel Databases publishes papers in all the traditional as well as most emerging areas of database research, including: Data Integration, Data Sharing, Security and Privacy, Transaction Management, Process and Workflow Management, Information Extraction, Query Processing and Optimization, the Analysis, Mining and Visualization of large data sets, Storage, Data Fragmentation, Placement and Allocation, Replication Protocols, Reliability, Fault Tolerance, Persistence, Preservations, Performance and Scalability, and Use of various communication and dissemination platforms and middleware.

Example sets of issues in the context of distributed and parallel systems include: Mobile, Service, P2P, grid and cloud computing for managing data and processes, Managing Heterogeneity and Autonomy in Distributed Systems, Semantic interoperability and integration (matching, mapping), Linked Data, Open Data, Mobile Data, Streaming Data, Sensor Data, Multimedia and Multimodal Data, Metadata, Knowledge Bases, Ontologies, Web scale data management, Relational, Object-Oriented, XML, Graph, RDF, Event data management, Supporting Group/Collaborative Work, Support for Non-Traditional Applications (e.g., Soft Computing applied to Data Processing, Translational medicine exploiting a variety of data), Alternative Software and Hardware Architectures Related to Data Management, The Use of Distributed and Parallel Database Technology in Managing Biological, Geographic, Spatial, Temporal, Scientific and Statistical Data, System Support and Interface Issues for Data Management.

  • Availability and reliability
  • Benchmarking and performance evaluation, and tuning
  • Big Data Storage and Processing
  • Cloud Computing and Database-as-a-Service
  • Crowdsourcing
  • Data curation, annotation and provenance
  • Data integration, metadata Management, and interoperability
  • Data models, semantics, query languages
  • Data mining and knowledge discovery
  • Data privacy, security, trust
  • Data provenance, workflows, Scientific Data Management
  • Data visualization and interactive data exploration
  • Data warehousing, OLAP, Analytics
  • Graph data management, RDF, social networks
  • Information Extraction and Data Cleaning
  • Middleware and Workflow Management
  • Modern Hardware and In-Memory Database Systems
  • Query Processing and Optimization
  • Semantic Web and open data
  • Social Networks
  • Storage, indexing, and physical database design
  • Streams, sensor networks, and complex event processing
  • Strings, Texts, and Keyword Search
  • Spatial, temporal, and spatio-temporal databases
  • Transaction processing
  • Uncertain, probabilistic, and approximate databases

Journal information

  • Divyakant Agrawal,
  • Mohamed Mokbel
Publishing model
Hybrid (Transformative Journal). Learn about publishing Open Access with us

Journal metrics

0.757 (2019)
Impact factor
1.032 (2019)
Five year impact factor
76 days
Submission to first decision
225 days
Submission to acceptance
37,251 (2020)

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