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Title: Synthetic Transaction Test Generator for Websites


Python script for querying a list of web sites and their details from Splunk and dynamically creating tests for monitoring uptime. The data generated from this script is then sent back to Splunk for creating reports and alerts.

Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
OSTI Identifier:
Report Number(s):
Synthetic Transaction Test Generator for Websites; 005474IBMPC00
Battelle IPID 30822-E
DOE Contract Number:
Resource Type:
Software Revision:
Software Package Number:
Software CPU:
Open Source:
Available under MIT license.
Source Code Available:
Other Software Info:
Available as open source. The existing free app available from the Splunk community requires a lot of work to maintain. This approach allows for a one time setup with very little maintenance.
Related Software:
This is a helper script for gathering data to send to Splunk for Enterprise.
Country of Publication:
United States

Citation Formats

. Synthetic Transaction Test Generator for Websites. Computer software. Vers. 00. USDOE. 9 Oct. 2017. Web.
. (2017, October 9). Synthetic Transaction Test Generator for Websites (Version 00) [Computer software].
. Synthetic Transaction Test Generator for Websites. Computer software. Version 00. October 9, 2017.
title = {Synthetic Transaction Test Generator for Websites, Version 00},
author = {},
abstractNote = {Python script for querying a list of web sites and their details from Splunk and dynamically creating tests for monitoring uptime. The data generated from this script is then sent back to Splunk for creating reports and alerts.},
url = {},
doi = {},
year = 2017,
month = ,
note =

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  • The Threat Stream Generator (TSG) project at Pacific Northwest National Laboratory has been developing synthetic datasets to test and evaluate visual analytics tools for the past four years. Our activities have ranged from supporting the evaluation of major U.S. Government analytical frameworks to creating four datasets for the IEEE Visual Analytics Science and Technology (VAST) contest over the past two years. We have developed a reasonable method and supporting toolset for creating believable synthetic data sets for different uses. A key differentiator for our datasets is that they contain data concerning one or more invented threats, based on a scenario.more » Embedding a known threat into the data provides ground truth for analytic tools to work against in evaluating their performance, as well as new opportunities for evaluation researchers to explore techniques given ground truth exists. We describe the process of creating the scenarios and threats and the process of transforming them into data elements, and then we describe how this data is embedded in other data to form a TSG dataset.« less
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