document: Generation of synthetic data sets for evaluating the accuracy of knowledge discovery systems Information Discovery and Analysis Systems ( IDAS ) are designed to correlate multiple sources of data and use data mining techniques to identify potential significant events . Application domains for IDAS are numerous and include the emerging area of homeland security . Developing test cases for an IDAS requires background data sets into which hypothetical future scenarios can be overlaid . The IDAS can then be measured in terms of false positive and false negative error rates . Obtaining the test data sets can be an obstacle due to both privacy issues and also the time and cost associated with collecting a diverse set of data sources . In this paper , we give an overview of the design and architecture of an IDAS Data Set Generator ( IDSG ) that enables a fast and comprehensive test of an IDAS . The IDSG generates data using statistical and rule-based algorithms and also semantic graphs that represent interdependencies between attributes . A credit card transaction application is used to illustrate the approach .
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