skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1334900
Report Number(s):
PNNL-SA-110014
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: The 11th International Conference on Data Mining (DMIN 2015), July 27-30, 2015, Las Vegas, Nevada, 162-167
Country of Publication:
United States
Language:
English

Citation Formats

Bramer, Lisa M., Chatterjee, Samrat, Holmes, Aimee E., Robinson, Sean M., Bradley, Steven F., and Webb-Robertson, Bobbie-Jo M. A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. United States: N. p., 2015. Web.
Bramer, Lisa M., Chatterjee, Samrat, Holmes, Aimee E., Robinson, Sean M., Bradley, Steven F., & Webb-Robertson, Bobbie-Jo M. A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data. United States.
Bramer, Lisa M., Chatterjee, Samrat, Holmes, Aimee E., Robinson, Sean M., Bradley, Steven F., and Webb-Robertson, Bobbie-Jo M. Wed . "A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data". United States.
@article{osti_1334900,
title = {A Machine Learning Approach for Business Intelligence Analysis using Commercial Shipping Transaction Data},
author = {Bramer, Lisa M. and Chatterjee, Samrat and Holmes, Aimee E. and Robinson, Sean M. and Bradley, Steven F. and Webb-Robertson, Bobbie-Jo M.},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {9}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: