WS / Wall Street MQP
The project ‘Predictive Analysis for Network Data Storm’ involves the analysis of big data in Splunk, which indexes machine-generated big data and allows efficient querying and visualization, to develop a set of thresholds to predict a network meltdown, or commonly known as a data storm. The WPI team analyzed multiple datasets to spot patterns and determine the major differences between the normal state and the storm state of the network. A set of rules and thresholds were fully developed for the Fixed Income Transversal Tools team in BNP Paribas, who implemented the model in their internal real-time monitoring tool ‘SCADA’ to predict and prevent network data storms.
Worcester Polytechnic Institute
Major Qualifying Project
All authors have granted to WPI a nonexclusive royalty-free license to distribute copies of the work, subject to other agreements. Copyright is held by the author or authors, with all rights reserved, unless otherwise noted.