MetaFa: Metadata management framework for data sharing in data-intensive applications

Minoru Ikebe, Atsuo Inomata, Kazutoshi Fujikawa, Hideki Sunahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The data-intensive applications are naturally executed on the Internet and generate a huge amount of data. The generated data would be stored distributed storages. In such environments, a user cannot easily find the target data by only filenames. The metadata is very useful to represent the characteristics and semantics of data. If users can specify the metadata, they will access the target data intuitively. We have been developing a distributed data management system called "MetaFa". MetaFa can collect the metadata semi-automatically. In this paper, we discuss the implementation issues of MetaFa in order to collect metadata automatically.

Original languageEnglish
Title of host publicationDistributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, Ambient Assisted Living - 10th Int. Work-Conf. Artificial Neural Networks, IWANN 2009 Workshops, Proceedings
Pages655-658
Number of pages4
EditionPART 2
DOIs
Publication statusPublished - 2009 Dec 1
Event10th International Work-Conference on Artificial Neural Networks, IWANN 2009 - Salamanca, Spain
Duration: 2009 Jun 102009 Jun 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5518 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International Work-Conference on Artificial Neural Networks, IWANN 2009
CountrySpain
CitySalamanca
Period09/6/1009/6/12

Keywords

  • Data management
  • Data-intensive applications
  • DataGrid
  • Metadata

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ikebe, M., Inomata, A., Fujikawa, K., & Sunahara, H. (2009). MetaFa: Metadata management framework for data sharing in data-intensive applications. In Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, Ambient Assisted Living - 10th Int. Work-Conf. Artificial Neural Networks, IWANN 2009 Workshops, Proceedings (PART 2 ed., pp. 655-658). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5518 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-02481-8_97