NDLI: Creating realistic, scenario-based synthetic data for test and evaluation of information analytics software

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Creating realistic, scenario-based synthetic data for test and evaluation of information analytics software

Content Provider	ACM Digital Library
Author	Whiting, Mark A. Haack, Jereme Varley, Carrie
Abstract	We describe the Threat Stream Generator, a method and a toolset for creating realistic, synthetic test data for information analytics applications. Finding or creating useful test data sets is difficult for a team focused on creating solutions to information analysis problems. First, real data that might be considered good for testing analytic applications may not be available or may be classified. In the latter case, tool builders will not have the clearances needed to use, or even see, the data. Second, analysts' time is scarce and obtaining the needed characteristics of real data from them to create a test data set is difficult. Finally, generating good test data is challenging. Commercial data generators are focused on large database testing, not information analytics tool testing. Our distinctive contribution is that we embed known ground truth in a test data set, so that tool developers and others will be able to determine the effectiveness of their software and how they are progressing in their support for information analysts. Our automated methods also significantly decrease data set development time. We review our approach to scenario development, threat insertion strategies, data set development, and data set evaluation. We also discuss our recent successes in using our data in open analytic competitions.
Starting Page	1
Ending Page	9
Page Count	9
File Format	PDF
ISBN	9781605580166
DOI	10.1145/1377966.1377977
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-04-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Evaluation Information visualization Data generator Visual analytics
Content Type	Text
Resource Type	Article

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