2015 Informs Annual Meeting

WD32

INFORMS Philadelphia – 2015

WD30 30-Room 407, Marriott Information Systems III Contributed Session Chair: Miaomiao Lu, Huazhong University of Science and Technology, Room403, South Student Hostel, the Huazh, Wuhan, China, 1207170339@qq.com 1 - Online Social Networks: The Social Influence of Sentiment Content on Digital Product Diffusion Tung Cu, Louisiana State University, 2200 Business Education Complex, Nicholson Extention, Baton Rouge, LA, 70803, United States of America, tcu1@lsu.edu, Helmut Schneider, James Van Scott The study explores the role of user-generated content (UGC) during the diffusion process of digital artifacts. Data collection is conducted on 260 new digital products and more than 105 thousand social network nodes. The overall finding shows that Volume of Post and UGC Sentiment have a dynamic impact on diffusion of digital products. But, the relationships among them depend on certain situations. The study sheds light on the crowding power and the long-tail effect in online social networks. 2 - How Much to Open, How Fast to Fix? Effects of Making the Software Open Source Rakesh Mallipeddi, PhD Student, Texas A&M University, 320 Wehner, 4217 TAMU, College Station, TX, 77843-4217, United States of America, rmallipeddi@mays.tamu.edu, Subodha Kumar, Ram Gopal, Emre Demirezen We develop empirical and analytical models to examine the effects of making software open on the overall quality of software systems and behavior of software vendor. We derive and develop optimal strategies for software vendor to allocate resources for maintenance of existing software while developing new software. 3 - Towards a Theoretical Framework of IT-enabled Operations Strategy Yeming Gong, Associate Professor, EMLYON Business School, 12 Rue Dunoir, lyon, France, Gong@em-lyon.com, Hongyi Mao, Ryad Titah, Oliver Yao By an integrated analysis of quantitative data from more than 100 organizations in Europe, Asia and North America and qualitative data from 56 cases, this paper presents a theoretical framework of IT-enabled operations strategy with the objective of investigating “How does information technology leverage resources and processes for operational agility?” 4 - Timing, Diffusion,and Substitution of Generations of Technological Innovations Miaomiao Lu, Huazhong University of Science and Technology, Room403, South Student Hostel, The Huazh, Wuhan, China, 1207170339@qq.com Diffusion processes across generations and over time have become increasingly complex and multifaceted in recent years. We discuss efforts to model simultaneously the substitution of successive generation of a durable technological innovation,and the diffusion of the technology.Empirical and normative implications of the proposed model are explored for four generations on Microsoft Windows operating system:win Vista;win xp;win 7;win 8.

We derive estimating equations for the optimal allocation strategy that do not require a model the system dynamics and that scale to very large problems. 2 - A Novel Sequence Kernel Graph Transform for Clustering and Visualization Chitta Ranjan, Georgia Institute of Technology, 755 Ferst Drive NW, Atlanta, GA, United States of America, nk.chitta.ranjan@gatech.edu, Samaneh Ebrahimi, Kamran Paynabar We propose a novel sequence kernel graph (SKG) transform for non-parametric feature extraction on sequence data. The proposed method is accurate, faster than existing methods and parallelizable. The SKG transform can be used for finding similarity between sequences, and hence, alignment-free clustering. It can be extended to perform bi-clustering, and graph visualization of sequences; with application on various behavioral data (clickstream, purchase pattern), protein and gene sequences, etc. 3 - A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems Jing Li, Arizona State University, Tempe, AZ, United States of America, jinglz@asu.edu, Na Zou Transfer learning, as a statistical modeling approach, refers to methods that integrate knowledge of old domains and data of a new domain, in order to develop a model for the new domain that is better than using the data of the new domain alone. We propose a transfer learning method for predictive modeling and apply it to degenerate biological systems. Theoretical results and findings from real-data analysis will also be presented. 4 - Bayesian Learning Without Recall: A Naive Social Learning Model Mohammad Amin Rahimian, Graduate Research Fellow, University of Pennsylvania, Levine 4F, University of Pennsylvania, 3330 Walnut Street, Philadelphia, PA, 19104, United States of America, mohar@seas.upenn.edu, Ali Jadbabaie We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents are making their decisions. This model avoids the complexities of fully rational inference and also provides a behavioral foundation for non-Bayesian updating. We present the implications of the choice of signal and action structures for such agents leading to familiar update forms. WD32 32-Room 409, Marriott Data Mining and Optimization Contributed Session Chair: Fatma Yerlikaya Ozkurt, Middle East Technical University, Institute of Applied Mathematics, Ankara, Turkey, fatmayerlikaya@gmail.com 1 - Data Classification via Cluster Covering Zhengyu Ma, Korea University, Room 551,Engineering Building, Seoul, 136-713, Korea, Republic of, mazhengyu@hotmail.com, Kwangsoo Kim, Hong Ryoo For data classification, a homogeneous cluster containing only one type of data can easily be identified via neighborhood measure. Using clusters for patterns in LAD, one can obtain a set of homogeneous clusters and next optimize their interplay to discover a decision theory. This new framework of supervised data analytics inherits the major advantage of LAD while it avoids redundant data binarization and also the difficult stages of support feature selection and pattern generation in LAD. 2 - Wastewater Sewerage Treatment Plant Aeration Process Optimization: A Data-driven Approach Being water quality oriented, large-scale industries such as wastewater treatment plants tend to overlook potential savings in energy consumption. Wastewater treatment process includes energy intensive equipment such as pumps and blowers to move and treat wastewater. Presently, a data-driven approach has been applied for aeration process modeling and optimization of one large scale wastewater in Midwest. A great deal of saving in energy can be made while keeping the water quality within limit. 3 - Optimal Experimental System Design Alireza Mohseni, Oregon State University, 204 Rogers, Corvallis, OR, 97331, United States of America, mohseni.s.alireza@gmail.com, David Kim This research examines how the design of a factorial experiment can be modeled as a cost and time constrained discrete optimization problem. Initial results with respect to creating a statistical model for an experiment will be presented. Anoop Verma, Research Associate, Wayne State University, 4815 4th St, Detroit, MI, 48201, United States of America, anoop.verma@wayne.edu, Kai Yang, Ali Asadi

WD31 31-Room 408, Marriott Data Mining in Medical and Sociological

Decision Making Sponsor: Data Mining Sponsored Session

Chair: Chitta Ranjan, Georgia Institute of Technology, Ferst Drive NW, Atlanta, GA, United States of America, nk.chitta.ranjan@gatech.edu Co-Chair: Kamran Paynabar, School of Industrial and Systems Engineering, 755 Ferst Drive, NW, Atlanta, GA, 30332, United States of America, kpaynabar3@gatech.edu 1 - Online, Semi-Parametric Estimation of Treatment Allocations for the Control of Emerging Epidemics Eric Laber, 211 Devonhall Lane, Cary, NC, 27518, United States of America, eblaber@ncsu.edu A key component in controlling the spread of an epidemic across a network of individuals is deciding where, when, and to whom to apply an intervention. An allocation strategy formalizes this process as a sequence of functions that map up- to-date information on the epidemic to a subset of nodes targeted for treatment.

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