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SEWEBAR - a Framework for Creating and Dissemination of Analytical Reports from Data Mining

SEWEBAR - a Framework for Creating and Dissemination of Analytical Reports from Data Mining. Jan Rauch, Milan Šimůnek University of Economics, Prague, Czech Republic. SEWEBAR - a Framework for C reating and Dissemination of Analytical Reports from Data Mining. Starting points

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SEWEBAR - a Framework for Creating and Dissemination of Analytical Reports from Data Mining

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  1. SEWEBAR - a Framework for Creating and Dissemination of Analytical Reports from Data Mining Jan Rauch, Milan ŠimůnekUniversity of Economics, Prague, Czech Republic

  2. SEWEBAR - a Framework for Creating and Dissemination of Analytical Reports from Data Mining • Starting points • Principles (as seen now) • Simple examples • First steps SEWEBAR

  3. SEWEBAR – Starting points (1) • Several similar mining problems a la STULONG: ADAMEK, TINITUS HEPATITIS, SOCIOLOGY, …: • Cca. 100 - 300 attributes • thousands of objects (usually patients) • domain expert (non informatics) available • some (this time relatively simple) background knowledge available • Reasonable result form is a well structured analytical report that must be • created • stored • retrieved • disseminated • used to answer more complex analytical questions SEWEBAR

  4. SEWEBAR – Starting points (2) • Some results concerning partial related projects • Report assistant (it works) • AR2NL (successful experiment) • EverMiner (considerations) • SEWEBAR (considerations) • observational calculi • Grants: LISp, Czech Science Foundation (GAČR), Kontakt, CBI, ?? • Students can contribute (4IZ460, 4IZ210, ? ) • Dealing with knowledge and semantics „is in“ (see e.g. „10 Challenging problems in Data Mining Research“ - http://www.cs.uvm.edu/~icdm/) SEWEBAR

  5. SEWEBAR – inspiration by Semantic Web (SEmanticWEBandAnalyticalReports) SEWEBAR

  6. SEWEBAR – Principles (1) • There is a structured set of (types of) patterns of local analytical questions • What strong relations (*, *, …) are valid in given data? • What strong known relations are not valid in given data? • What exceptions from … are valid in given data? • …. • There are various items of background knowledge in easy understandable form • Bier consumption BMI • Mother hypertension + Hypertension • , - , …. • Application of the pattern of analytical question to a given item of background knowledge and to a given data matrix leads to a concrete analytical question. SEWEBAR

  7. SEWEBAR – Principles (2) • To each local analytical question there is type of local analytical report answering the question • The concrete local analytical question can be answered by the GUHA procedures implemented in the LISP-Miner system • The corresponding analytical report can be automatically created • There is a similar structured set of patterns of global analytical questions (concerning several similar data matrices) that can be automatically answered on the basis of the local analytical reports SEWEBAR

  8. SEWEBAR – Principles From local analytical question to analytical report SEWEBAR

  9. SEWEBAR – simple examples • Pattern of analytical question – mutual influence of attributes • Pattern of analytical question – groups of attributes • Answering „analytical question – groups of attributes“ by 4ft-Miner • Analytical report SEWEBAR

  10. SEWEBAR - a Framework for Creating and Dissemination of Analytical Reports from Data Mining • Starting points • Principles (as understood now) • Simple examples • First steps SEWEBAR

  11. SEWEBAR – Principles for first steps • To implement soon first version (simplified if necessary) of support for the whole process dealing with local and global analytical reports. The whole process covers: • Formulation of reasonable local analytical questions using background knowledge • Creation of analytical reports answering particular analytical questions • Formulating and answering reasonable global analytical questions • Use the first version to • Gradually improve and enhance particular parts • Develop corresponding theory using observational calculi SEWEBAR

  12. Control panel – tool for first steps SEWEBAR

  13. SEWEBAR – First steps (1) Background knowledge and local analytical questions: • We start with ADAMEK and STULONG data sets • Background knowledge – we use current version of Knowledge Base • To define first version of the set of LAQ - Local Analytical Questions • To implement LAQPA - Local Analytical Question Patterns Administrator • To implement LAQA - Local Analytical Questions Administrator SEWEBAR

  14. SEWEBAR – First steps (3) Local analytical reports: • Enhancement of 4ft-Miner by filtering out of uninteresting rules • EverMiner modules • To define skelets of analytical reports • Generator of analytical reports SEWEBAR

  15. SEWEBAR – First steps (4) Global analytical reports - implemented using ?Topic Maps Content management system? • To define rules for indexing analytical reports by Topic Maps • To implement tool for automated indexing analytical reports for Topic Maps • To define first version of a set of global analytical questions • To implement tool for automated answering global analytical reports • ??IGA grant?? SEWEBAR

  16. Thank you for your attention SEWEBAR

  17. Thank you for your attention SEWEBAR

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