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BALTCOM Datawarehouse: online data mining using MS Analysis services (poster)
Jansen, T.; Degel, H. (2002). BALTCOM Datawarehouse: online data mining using MS Analysis services (poster), in: Brown, M. et al. (Ed.) (2002). The Colour of Ocean Data: International Symposium on oceanographic data and information management, with special attention to biological data. Brussels, Belgium, 25-27 November 2002: book of abstracts. VLIZ Special Publication, 11: pp. 68
In: Brown, M. et al. (Ed.) (2002). The Colour of Ocean Data: International Symposium on oceanographic data and information management, with special attention to biological data. Brussels, Belgium, 25-27 November 2002: book of abstracts. VLIZ Special Publication, 11. Flanders Marine Institute (VLIZ): Oostende. XI, 93 pp., more
In: VLIZ Special Publication. Vlaams Instituut voor de Zee (VLIZ): Oostende. ISSN 1377-0950, more

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Keyword
    Marine

Authors  Top 
  • Jansen, T.
  • Degel, H.

Abstract
    BALTCOM version 2.0 is an internet based datawarehouse (URL: http://www.BaltCom.org) where the countries represented in IBSFC (International Baltic Sea Fishery Commission) upload, download, validate and analyse discard data.

    The development of the international datawarehouse was an EC funded project.

    In the process of assessing stocks of commercially fished species in the Baltic Sea [mainly: Cod (Gadus morhua), Herring (Clupea harengus), Sprat (Sprattus sprattus) and Flounder (Platichthys flesus)] data needs to be aggregated and calculated to match the input formats required to run assessment software used in the Baltic Fisheries Assessment Working Group.

    The need for a fast performing web based data mining application to analyse these formats on different aggregation levels was addressed by implementing a solution using Microsoft Analysis Server and Microsoft Excel Pivot Table Services.

    The functionality of the solution is presented through use cases. Architecture and calculations are documented.

    The selection of technology is discussed.

    Recommendations are given on the basis of lessons learned during development and implementation.

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