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Loan Approval Metadata Storage System for Efficient Database Management

This application uses data mining techniques to analyze approved but not booked loans to determine key characteristics. It aims to store metadata in an innovative frame-based system efficiently. The logical flow involves analyzing database attributes, instances, and pivots for optimal data storage. The goal is to develop a generic, space-efficient solution with a structured approach to manage loan metadata effectively.

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Loan Approval Metadata Storage System for Efficient Database Management

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Presentation Transcript

  1. Introduction Business Problem APPLICATIONS APPROVED BOOKED

  2. Technique: Data Mining

  3. Objectives • Determine prominent characteristics of loans and/or applicant(s) where loan is approved but not booked. • Devise innovative and exciting ways to store metadata using a frame-based system. • Develop an efficient solution as measured by database (storage) space requirements. • Develop a solution that is generic.

  4. Logical Flow: Pt. 1 Database(s) Attribute Name Instance Count Pivot Count S K A P By Attr.

  5. Logical Flow: Pt. 2 Attribute Name Instance Count Pivot Count Value/Range Pivot Count Value/Range Pivot Count Value/Range Pivot Count Gini Coefficient

  6. Nomenclature Variable vs. Attribute Quantitative Categorical Ordinal Nominal

  7. Structures: Pivot Relation Key Pivot Key 1 Y / N Key 2 Y / N Key 3 Y / N

  8. Structures: Mined Data Mining Relation Mining Variable Partition Element Partition Element Mining Variable Partition Element Partition Element . . .

  9. Structures: Narl Nodes

  10. Addendum RFC

  11. Addendum RFJC

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