Journal Information
Distributed and Parallel Databases
http://www.springer.com/computer/database+management+%26+information+retrieval/journal/10619
Impact Factor:
0.800
Publisher:
ELSEVIER
ISSN:
0926-8782
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3717
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Call For Papers
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
Last updated by Dou Sun in 2017-02-12
Special Issues
Special Issue on Large-Scale Data Curation and Metadata Management
Submission Date: 2017-03-01

Data curation and annotation are essential mechanisms for capturing a wide variety of metadata related to data. This metadata may carry different semantics ranging from tracking the data's lineage and provenance, quality information, exchanging knowledge and discussion messages among scientists, attaching related articles or documents, linking to relevant statistics about the data, and highlighting erroneous or conflicting values. This metadata may be represented in many different formats including free-text values, articles or binary files, images, structured information such as provenance, or semi-structured content such as email messages. The creation and maintenance of annotated databases and metadata repositories require a great deal of effort (and cost) from many scientists and domain experts. Yet, the gain from the maintained annotations is still very limited, because of the lack of comprehensive solutions that automate large-scale metadata management tasks such as storage of annotations, extracting information from large sets of annotations, and their propagation through operations. Thus, the virtue of the hidden knowledge in this metadata is still uncharted. The growing volume, profound complexity, increasing heterogeneity, and hidden semantics of emerging metadata repositories create many unprecedented challenges. Unfortunately, existing techniques are not well-prepared to deal with the aforementioned characteristics, and they fall short in providing advanced processing, semantics extraction, and deep analytics over the such metadata. In addition, the rapid evolution of the big data infrastructure, e.g., MapReduce, Spark, and NoSQL databases, creates numerous challenges and research opportunities for curation, annotation, and metadata management including distributed metadata processing, metadata migration across different infrastructures, provenance tracking through complex workflows over possibly several platforms, proactive curation techniques, and many others. This special issue seeks original and high-quality research articles, advancing the state of the art of research in large-scale data curation and metadata management. We encourage submissions in the following areas: - Metadata summarization, mining, and deep analytics - Distributed data curation and metadata management - Big data curation and annotation in emerging infrastructures, e.g., MapReduce, Spark, and NoSQL engines - Scalable metadata management and migration across different infrastructures - Metadata validation and verification - Integration of metadata into the data's processing and exploration cycle - New annotation management and metadata models - Proactive and automated data curation and annotation - Metadata cleaning and quality control strategies - Metadata-driven query processing and optimizations - Storage and indexing techniques for big metadata
Last updated by Dou Sun in 2017-02-12
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