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Zeitschrift

Distributed and Parallel Databases

An International Journal

Distributed and Parallel Databases OnlineFirst articles

21.09.2018

Concept acquisition and improved in-database similarity analysis for medical data

Efficient identification of cohorts of similar patients is a major precondition for personalized medicine. In order to train prediction models on a given medical data set, similarities have to be calculated for every pair of patients—which results …

01.09.2018

On-demand big data integration

A hybrid ETL approach for reproducible scientific research

Scientific research requires access, analysis, and sharing of data that is distributed across various heterogeneous data sources at the scale of the Internet. An eager extract, transform, and load (ETL) process constructs an integrated data …

31.08.2018

A memory-optimal many-to-many semi-stream join

Semi-stream join algorithms join a fast stream input with a disk-based master data relation. A common class of these algorithms is derived from hash joins: they use the stream as build input for a main hash table, and also include a cache for …

28.08.2018

Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm

Correlation clustering detects complex and intricate relationships in high-dimensional data by identifying groups of data points, each characterized by differents correlation among a (sub)set of features. Current correlation clustering methods …

24.08.2018

Abstract cost models for distributed data-intensive computations

We consider data analytics workloads on distributed architectures, in particular clusters of commodity machines. To find a job partitioning that minimizes running time, a cost model, which we more accurately refer to as makespan model, is needed.

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Über diese Zeitschrift

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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