Big Data Analytics and Statistics


Description:
The session welcomes contributors that highlight the application of statistics in Big Data Analytics in the process industries. The topics could potentially cover (but are not limited to) the planning of data collection, data pre-processing or management, data summarization to extract useful information, making inferences or predictions, etc. and should demonstrate how the methodology deals with the challenges in the big data setting (the 4Vs) with the goal of improve or optimize the process design or operations. The goal here is to highlight the importance of Statistics in Big Data Analytics applications.

Sponsor:
Topical A: 3rd Big Data Analytics


Chair:
Swee-Teng Chin
Senior Engineer
The Dow Chemical Company
2301 N. Brazosport Blvd., B-1225
Freeport, TX
77541
Email: SChin@dow.com

Co-Chair:
James Cross III
Manager of Consulting Services
The MathWorks, Inc.
Consulting Services Group
3 Apple Hill Drive
Natick, MA
01760
Phone Number: 617-605-5818
Fax Number: United States
Email: james.cross@mathworks.com


Papers:
An Alternative Control Chart to Monitor Low Level Count Data
David E. Fitchett, Dow Chemical Company; Ruben 't Lam, Dow Chemical Company
Use of Genetic Algorithm for Selecting Number of Non-Zero Loadings in Sparse Principal Component Analysis
Shriram Gajjar, University of California, Davis; Murat Kulahci, Technical University of Denmark; Ahmet Palazoglu, University of California, Davis
Challenges and Benefits of Aligning and Reconciling Process Data for Seasonal Processing Industries
Nick Depree, University of Auckland; Arrian Prince-Pike, University of Auckland; Irina Boiarkina, University of Auckland; Richard Croy, Fonterra Co-operative Group Limited; David I. Wilson, AUT University; Brent R. Young, University of Auckland
On Mapping the Operating Window of Processes
Fabio D'Ottaviano, Dow Chemicals
Big Data Approach to Fault Detection and Diagnosis in Batch Processes Using Nonlinear SVM-Based Feature Selection
Melis Onel, Texas A&M Energy Institute, Texas A&M University; Chris A. Kieslich, Texas A&M Energy Institute, Texas A&M University; Yannis A. Guzman, Princeton University; Christodoulos A. Floudas, Texas A&M University; Efstratios N. Pistikopoulos, Texas A&M Energy Institute, Texas A&M University
Utilizing Big Data for Development of Active Learning Dynamic Soft Sensor
Lester L. T. Chan, Chung-Yuan Christian University; Qing-Yang Wu, Chung-Yuan Christian University; Junghui Chen, Chung-Yuan Christian University
See more of this Group/Topical: Topical A: 3rd Big Data Analytics