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.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Ledesma, Mauricio
• Hoque, Muyeedul

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-012314-153515

Advisor

• Gerstenfeld, Arthur

Year

• 2014

Center

• Wall Street-FinTech Project Center (Boston or New York) - MQP

Sponsor

• BNP Paribas

Date created

• 2014-01-23

Location

• Boston

Resource type

• Major Qualifying Project

Major

• Industrial Engineering

Rights statement

• In Copyright