Student Work
Predictive Analysis for Network Data Storm
PublicDownloadable Content
open in viewerThe 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
- Publisher
- Identifier
- E-project-012314-153515
- Advisor
- Year
- 2014
- Center
- Sponsor
- Date created
- 2014-01-23
- Location
- Boston
- Resource type
- Major
- Rights statement
Relations
- In Collection:
Items
Items
Thumbnail | Title | Visibility | Embargo Release Date | Actions |
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Hoque_and_Ledesma_-_Predictive_Analysis_for_Network_Data_Storm.pdf | Public | Download |
Permanent link to this page: https://digital.wpi.edu/show/7w62f968p