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Distributed and Parallel Databases

An International Journal

Distributed and Parallel Databases OnlineFirst articles


A framework for parallel map-matching at scale using Spark

Map-matching is a problem of matching recorded GPS trajectories to a digital representation of the road network. GPS data may be inaccurate and heterogeneous, due to limitations or error on electronic sensors, as well as law restrictions. How to …


MDCUT2: a multi-density clustering algorithm with automatic detection of density variation in data with noise

Despite their adoption in many applications, density-based clustering algorithms perform inefficiently when dealing with data with varied density, imbricated and/or adjacent clusters. Clusters of lower density may be classified as outliers, and …

10.10.2018 Open Access

Optimizing data-flow implementations for inter-organizational processes

Inter-organizational business processes can be designed top-down: starting from a global process model, where each activity and gateway is assigned to one of the participating organizations, local processes (or views/stubs for refinement) are …


PLI: efficient clustering of cloud databases

Commercial cloud database services increase availability of data and provide reliable access to data. Routine database maintenance tasks such as clustering, however, increase the costs of hosting data on commercial cloud instances. Clustering …


Horizontal fragmentation for fuzzy querying databases

Fuzzy querying is one of the main research topics of database investigators. Several research works to date focused on building fuzzy data models, fuzzy query languages, and fuzzy database systems. However, such systems turn out to be less …

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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
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