Use of graph-oriented database in the detection of fraud in quotas for parliamentary activity
summary
Uses OrientDB with a Java ETL and a Ruby on Rails front end to link CEAP and TSE open datasets and detect relationship patterns. Loads take about 16h/5h ETL loads for the SAP (CEAP) and donations files, builds nodes for deputies, companies and transactions, and runs graph MATCH pattern queries to find when a company donates to a 2014 campaign and later supplies CEAP transactions. Finds the pattern for seven deputies in Minas Gerais (44 CEAP transactions, 6 TSE donations) and prototypes a collaborative verification web UI.
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chapters (12)
- 0:00Start
- 0:57Motivation
- 6:53Oriented Graph
- 10:04Examples of no Sequel Dbms
- 10:56Graph Oriented Dbms
- 11:46The Usage of Graph Oriented Databases
- 15:57Object Orientation
- 20:45Data Model
- 22:27Fraud Patterns
- 24:44Workflow
- 25:55Query Language
- 28:50Fraud Pattern
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