Consumer Fraud Prevention Example

NOTE - this example requires DSE Enterprise with Analytics enabled

Scenario

This is an example of how to use DSE Analytics to detect fraudulent claims in a real time environment.
This example deals with a credit card processing application using Spark.MLlib to learn about fraudulent claims. The application reads from a sample data set and then detect actual fraudulent claims from a separate data set containing simulated data.

Schema Setup

1. Start DSE in Analytics mode
2. Run the included cql script resources/cql/create_schema.cql using cqlsh with the following command cqlsh -f '/resources/cql/create_schema.cql'

Configuring the processor

1. Open the file src/main/resources/properties.txt
2. set trainingDir= (located in src/main/resources/training)
3. set testDir= (located in src/main/resources/test)
4. set batchDuration and numFeatures to appropriate values. Defaults are respectively 1 and 3

TODO: above section needs more fleshing out

Building and running the jar file - WORK IN PROGRESS: CURRENTLY BUILDS BUT DOES NOT RUN CORRECTLY

To build the jar file run:

mvn clean package 

To start the processor run

dse spark-submit <path to jar file> <path to properties.txt file>