Distributed and Parallel Databases

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.