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Distributed and Parallel Databases

An International Journal of Data Science, Engineering, and Management

Distributed and Parallel Databases OnlineFirst articles


MISS: finding optimal sample sizes for approximate analytics

Nowadays, sampling-based Approximate Query Processing (AQP) is widely regarded as a promising way to achieve interactivity in big data analytics. To build such an AQP system, finding the minimal sample size for a query regarding given error …


A framework for discovering popular paths using transactional modeling and pattern mining

While the problems of finding the shortest path and k-shortest paths have been extensively researched, the research community has been shifting its focus towards discovering and identifying paths based on user preferences. Since users naturally …


Mutual-contained access delegation scheme for the Internet of Things user services

Internet of Things (IoT) has gained significance in different real-time applications due to its pervasive access response to user demands. This is a heterogeneous and open platform in which the data provenance, access permissions, and concealed …


Double layer secure secret images sharing scheme for biometrics

Biometric strategy is primarily followed for authentication which incorporates the distinct recognition of the individual depending on their physical or behavioral features. Among all the biometrics techniques, ‘Iris scanning’ is touted to be the …


FIGS-DEAF: an novel implementation of hybrid deep learning algorithm to predict autism spectrum disorders using facial fused gait features

Autism spectrum disorder (A.S.D.) is considered a heterogeneous mental disorder, which is notoriously difficult to identify for a better diagnosis, especially among children. The current diagnosis methodology is purely based on the behavioural …

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About this journal

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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