Development of a Synthetic Data Set Generator for Building and Testing Information Discovery Systems

Data mining research has yielded many significant and useful results such as discovering consumer-spending habits, detecting credit card fraud, and identifying anomalous social behavior. Information discovery and analysis systems (IDAS) extract information from multiple sources of data and use data mining methodologies to identify potential significant events and relationships. This research designed and developed a tool called IDAS data and scenario generator (IDSG) to facilitate the creation, testing and training of IDAS. IDSG focuses on building a synthetic data generation engine powerful and flexible enough to generate synthetic data based on complex semantic graphs