Distributed and Parallel Databases (DPD) is an academic journal published by Springer. (ISSN 0926-8782, impact factor 0.9, CCF C).
Aims and scope
Distributed and parallel databases technology has been the subject of intense research and development effort. Numerous practical applications and commercial products that exploit this technology also exist. 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:
· 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
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