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...

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Bibliographic Details
Main Authors: Lin, P.J., Samadi, B., Cipolone, A., Jeske, D.R., Cox, S., Rendon, C., Holt, D., Rui Xiao
Format: Conference Proceeding
Language:English
Subjects:
Buildings
Client-Server
Credit cards
Data Generation
Data mining
Engines
Information analysis
Java
Medical diagnostic imaging
Performance analysis
Power generation
Semantic Gra[j
System testing
Online Access:Request full text
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Summary: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
DOI:10.1109/ITNG.2006.51