[Iefac.list] IIE Transactions Special Issue on Big Data Analytics

[Ronald Askin]

IIE Transactions
Call for Papers
Special issue on “Big Data Analytics in Manufacturing and Services”

Guest Editors: Soundar Kumara, Paul Griffin and LiYing Cui
Information technology has provided unprecedented reach and range resulting in the rapid evolution of computation and communication. The Internet of Things connects devices, computers and people seamlessly with ubiquitous coverage. This has led to an explosive growth in the volume of data resulting in “Big Data” becoming a topic of scientific and commercial importance. The big data era is impacting almost everything around us, including retail, healthcare, security, manufacturing, energy, logistics and other services. A recent McKinsey report (2011) argues that big data will be the next frontier of innovation, competition and productivity.
Big data is characterized by high volume, high variety and high velocity (Eaton et al., 2011). Though traditional statistical techniques remain important, the increased volume, velocity and variety necessitate new methods that integrate statistics with simulation, computer science, and optimization. On the other hand Manufacturing research is focusing on how to make products faster using global resources leading to the concept of “information intensive manufacturing,” in which analyzing large volumes of data and building inference schemes are becoming critical. The field of big data analytics presents challenges to both academia and industry. Several key issues related to Big Data (Balakrishnan et al., 1999) and new techniques (Cui et al., 2010) need to be explored. The central theme of this special issue is Big data analytics with emphasis on manufacturing and services. The purpose is to explore the state-of-the-art research and applications in big data in the focus domains, by bringing together researchers and practitioners to document the significant advances, explore emerging challenges, present new models, and provide vision for future research and development.

• Subject Coverage

In this special issue, we are looking for both theoretical and application based research papers in the service and manufacturing domains, which are of particular interest to the industrial engineering discipline. Topics to be covered include, but are not limited to the following:
Data Capture and Storage: models and technologies addressing storage, cleaning and aggregating multi modal and real-time streaming data.
Mining Big Data: pattern extraction from static and streaming data. Mining multimodal data, with emphasis on theory and application development of data mining and machine learning.
Decision Making: Optimization and other analytic models to aid decision making in the context of Big Data

Visualizing Big Data: Real-time visualization techniques for big data. Integrating decision-making and visualization



v Submission Instructions

Manuscripts prepared according to the IIE Transactions publication guidelines

must be submitted through http://mc.manuscriptcentral.com/iietransactions. Please select “Special Issue” under Manuscript Category of your submission.



v Important Dates



 October 1, 2013: Intent to submit (optional)

 March 1, 2014: Paper submission deadline.

 June 1, 2014: Completion of the first round review.

 August 1, 2014: Completion of the second round review.

 October 1, 2014: Final manuscripts due.

 December, 2014: Tentative publication date.



v Guest Editors



You may contact any of the special issue guest editors whose coordinates are given below:

Soundar Kumara, Paul Griffin

310 Leonhard Building

Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802

skumara at psu.edu; pmg14 at engr.psu.edu

LiYing Cui

Starbucks Coffee Company

2401 Utah Ave, S-SC6, Seattle, WA 98134

Lcui at starbucks.com

v References



Balakrishnan, A., S. Kumara, and S. Sundaresan. 1999. Manufacturing in the Digital Age: Exploiting IT for Product Realization. Information Systems Frontiers . 1(1):25-50.

Eaton, C., D. Deroos, T. Deutsch, G. Lapis, and P. Zikopoulos, Understanding Big Data – Analytics for Enterprise Class Hadoop and Streaming Data. 2011. McGraw Hill Publishers.

McKinsey Global Institute. Big Data: The next frontier for innovation, competition and productivity. 2011.
Cui L., S. Kumara, R. Albert, Internet based Service Networks, Chapter 8, Vol. 2, Handbook of Optimization in Complex Networks, Springer, 2011.



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