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Distributed and Parallel Databases

An International Journal of Data Science, Engineering, and Management

Distributed and Parallel Databases OnlineFirst articles


Schema matching based on SQL statements

Schema matching is a critical step in numerous database applications such as web data sources integrating, data warehouse loading and information exchanging among several authorities. In this paper, we propose to exploit the similarities of the …

09.05.2019 Open Access

Enabling efficient process mining on large data sets: realizing an in-database process mining operator

Process mining can be used to analyze business processes based on logs of their execution. These execution logs are often obtained by querying a database and storing the results in a file. The mining itself is then done on the file, such that the …


A cost-based storage format selector for materialized results in big data frameworks

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously, by deploying data-intensive workflows (DIWs). These DIWs of different users share many common tasks (i.e, 50–80%), which can be …


Efficient and non-blocking agreement protocols

Large scale distributed databases are designed to support commercial and cloud based applications. The minimal expectation from such systems is that they ensure consistency and reliability in case of node failures. The distributed database …


High-dimensional similarity searches using query driven dynamic quantization and distributed indexing

The concept of similarity is used as the basis for many data exploration and data mining tasks. Nearest neighbor (NN) queries identify the most similar items, or in terms of distance the closest points to a query point. Similarity is traditionally …

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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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