U.S. Department of Energy

Pacific Northwest National Laboratory

A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data

Publish Date: 
Sunday, June 21, 2015
Business intelligence problems are particularly challenging due to the use of large volume and high velocity data in attempts to model and explain complex underlying phenomena. Incremental machine learning based approaches for summarizing trends and identifying anomalous behavior are often desirable in such conditions to assist domain experts in characterizing their data. The overall goal of this research is to develop a machine learning algorithm that enables predictive analysis on streaming data, detects changes and anomalies in the data, and can evolve based on the dynamic behavior of the data. Commercial shipping transaction data for the U.S. is used to develop and test a Naïve Bayes model that classifies several companies into lines of businesses and demonstrates an ability to predict when the behavior of these companies changes by venturing into other lines of businesses.
Bramer LM, S Chatterjee, AE Holmes, SF Bradley, SM Robinson, and BJM Webb-Robertson. 2015. "A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data." In The 2015 World Congress in Computer Science, Computer Engineering, and Applied Computing. PNNL-SA-110014, Pacific Northwest National Laboratory, Richland, WA.
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