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Expert system

Utilizing data mining techniques to enhance TRACES system by predicting and improving effectiveness of controls on consignments. The project focuses on creating predictive models for different aspects of TRACES certification process. Assessing and analyzing consignments' data will help identify and prevent non-compliant cases efficiently.

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Expert system

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  1. Expert system Data Mining for TRACES

  2. Expert system - Plan • Purpose of the project • Certificates and controls • Models • Effectiveness • TRACES integration and planning

  3. Expert system - Purpose • Trade, regulated by a strict legislation defined by EC (animal welfare, transport safety, consumers’ health, disease control…). • TRACES expert system • Detect or prevent the consignments that do not follow the EC rules. • Be able to increase the control in a more risky period or decrease the control in a less risky period. • Regulate the workload balancing (number of controls per day) of BIP. • 3 kinds of controls • EC legislation • Personal suspicion of the BIP authority • Random! The aim of the project is to improve the random control. => Use of Data Mining.

  4. Expert system – Data Mining • Data Mining, definition: • process of analyzing data and identifying existing patterns in information. • Diverse techniques exist: • Statistics • Database • pattern recognition • artificial intelligence etc. • For TRACES, we use a technology which • allows for fast build of predictive models, and • Provides for the integration with TRACES

  5. Expert system – Certificates and Controls Certificates 3 kinds of certificates: • CVEDP (Common Veterinary Entry document for Animal Products), animals products export from a third country to the EC and EE area. • CVEDA (Common Veterinary Entry document for Animal), animals export from a third country to the EC and EE area. • INTRA (Community Trade of Animals and certain Animal Products), trade among Europe. All CVEDA are controlled! Predictive models => CVEDP and INTRA Controls 4 kinds of controls: • Documentary check, it is done systematically. • Identity check, it is done systematically. • Physical check, it is not systematic and depends on Commission Decision 94/360/EC, by random or suspicion. The predictive model will replace the random control. • Laboratory check, less frequent Use of predictive models. Expert system => Physical check and laboratory check

  6. Expert system – Predictive models • The starting point is a single model for the European countries • 4 European models: cvedp physical, cvedp laboratory, intra physical, intra laboratory. • Consignments in Portugal and in Lithuania may not share the same patterns => Need a model per country. • 132 country models: 4 models for each of the 33 european countries. • In large countries, heterogeneous distribution of patterns, consignments in Marseille may not share the same patterns with those in Dunkerque or Paris. => Need a model per BIP. • 1428 BIP models: 4 models for each of the 357 BIP.

  7. Expert system – Predictive models Which model to use? • 3 predictive models are called • European model, all TRACES certificates of the same type (Cvedp or Intra). • Country model, all TRACES certificates of the same type (Cvedp or Intra) and the same BIP country. • BIP model, all TRACES certificates of the same type (Cvedp or Intra) and the same BIP. • Predicted value • The predicted value ‘non satisfactory’ is sent as a response if there is at least one predicted value ‘non satisfactory’. • Otherwise, the value ‘satisfactory’ is sent.

  8. Expert system – Effectiveness Predictive model on all EU CVEDP consignments,30% consignments checked => 85% of problematic consignments detected! Analysis of EU CVEDP consignments for 2007, 2008 and 2009

  9. Expert system – Effectiveness Predictive model on France CVEDP consignments,30% consignments checked => 87% of problematic consignments detected! Analysis of France CVEDP consignments for 2007, 2008 and 2009

  10. Expert system – Effectiveness Predictive model on Austria consignments,30% consignments checked=> 99% of problematic consignments detected! Analysis of Austria consignments for 2007, 2008 and 2009

  11. Expert system – Effectiveness Analysis of TRACES consignments, 2007, 2008 and 2009 Effectiveness of the predictive models in case of 5% of problematic consignements. • A random control will have to check all the consignments to get the problematic 5%. • A perfect model will detect all the problematic consignments, so will check only 5% of consignments. For countries with very different heterogenous consignments as France or United Kingdom we will also need an analysis at the BIP level.

  12. Expert system – TRACES Integration TRACES web UI mock-up Banner shown if above threshold Data mining result is always shown

  13. Expert system – Next steps • Deployment:Q3 – Q4 2010 • Evaluation: • Collect and evaluate results: • Was the advise followed? • What was the result? • Adapt model as needed (requires critical mass of data) • Possible future adaptations: • Additional certificate models • Possibility to define specific model (cncode, species-class, species-family…)

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