2015 Informs Annual Meeting

WE30

INFORMS Philadelphia – 2015

2 - Use the Whole Buffalo: Binary Classification which Encounters Diverse Data Types David Elkind, National Security Consultant & Statistical Modeler, Novetta Solutions, 1320 N Veitch St, Arlington, VA, 22201, United States of America, delkind@novetta.com Contemporary data collection gathers many different data formats, yet conventional analysis views data from a flat perspective in which an observation is a single unit type. This perspective leaves some data types unexploited when they cannot be made to fit a single paradigm. We propose to use model stacking to make predictions informed by all available data types. We accomplish this by using several SVMs, each of which has a kernel function appropriate to the subset of features it is learning. 3 - Big Data and Causality Xuan Wang, Graduate Student, Louisiana State University - ISDS Department, 2200 Business Education Complex, Nicholson Extension, Baton Rouge, LA, 70803, United States of America, xwang35@lsu.edu, Helmut Schneider In the past decade, Big Data Analytics has mainly focused on data mining to make better predictions. This research explores analytical techniques to discover causal relationships and focuses on challenges of interpreting correlational relationships in big data and discusses methods that help to distinguish between correlational and potential causal effects. Chair: Shalini Wunnava, Assistant Professor, SUNY Potsdam, 44 Pierrepont Avenue, 209 Dunn Hall, Potsdam, NY, 13676, United States of America, wunnavss@potsdam.edu 1 - Crowdsourcing and Project Management Michael Chuang, SUNY, 1 Hawk Dr, New Paltz, NY, United States of America, mikeychuang@gmail.com Crowdsourcing has been increasingly employed in today’s projects, and has become a phenomenon. Issues of crowdsourcing also demonstrate in various facets, such as innovation and technology. However, there lacks a comprehensive understanding of crowdsourcing’s potentials for projects. To bridge the gap, this research aims at conducting a systematic review of literature, in hopes of better understanding crowdsourcing for project management. 2 - The Effect of Project Artifacts, Ambidexterity, and Social Network on Open Source Project Success Ram Kumar, Professor, UNC-Charlotte, 203B Friday Building, 9201 University City Boulevard, Charlotte, NC, 28223, United States of America, rlkumar@uncc.edu, Orcun Temizkan Open Source Software (OSS) development is a rapidly emerging, yet poorly understood type of software development with extremely high project failure rates. We present a model of OSS project success by integrating multiple theories from social networking, innovation, and organizational theory. Our results illustrate that ambidexterity along with artifact development success and social network characteristics influences project success and highlight the bridging role of ambidextrous developers 3 - Leveraging On-demand Markets to Manage a Hybrid Workforce for IT Service Delivery Su Dong, Assistant Professor, Fayetteville State University, School of Business and Economics, 1200 Murchison Road, Fayetteville, NC, 28311, United States of America, sdong@uncfsu.edu, Monica Johar, Ram Kumar Organizations increasingly have access to temporary workers through markets for on-demand workers. We present a model of a hybrid workforce in which organizations effectively assign tasks to a mix of full-time and on-demand workers. Full-time workers have to perform assigned tasks. Temporary (on- demand) workers bid for tasks that are beneficial to them. Issues relating to pricing, task allocation and knowledge management are explored. 4 - Designing Quality Control Tools for Enhanced Cyber-security in Manufacturing Ahmed Elhabashy, Graduate Student, Virginia Tech, 114 Durham Hall, 1145 Perry Street, Blacksburg, VA, 24061, United States of America, habashy@vt.edu, William Woodall, Lee Wells, Jaime Camelio Manufacturing relies heavily upon the use of Quality Control tools to detect quality losses and to ensure high quality parts production. However, current tools are not designed to detect the effects of cyber-attacks, as they are based on assumptions (sustained shifts, rational sub-grouping, etc.) that may no longer be valid under the presence of an attack. The goal of this research is to design/adapt current Quality Control tools by adopting principles from the Information Technology domain. WE30 30-Room 407, Marriott Information Systems IV Contributed Session

5 - Biometrics: Adoption and Attitudes Shalini Wunnava, Assistant Professor, SUNY Potsdam, 44 Pierrepont Avenue, 209 Dunn Hall, Potsdam, NY, 13676, United States of America, wunnavss@potsdam.edu Biometrics has become ubiquitous on present day digital devices; although the usage is not very widespread currently, it is expected to see exponential growth in the near future. What attitudes are fueling the adoption of biometrics? This research question will be examined using the lenses of protection motivation theory and technology acceptance model. WE31 31-Room 408, Marriott Statistical Roles in Stochastic Decision-Making Chair: Victoria Chen, The University of Texas at Arlington, Dept. of Ind., Manuf., & Sys. Engr., Campus Box 19017, Arlington, TX, 76019, United States of America, vchen@uta.edu 1 - A Data-driven Optimization of Price, Promotion, Display, and Feature at Product Category Level Durai Sundaramoorthi, Lecturer In Management, Washington University in St. Louis, One Brookings Drive, Olin Business School, St. Louis, MO, 63131, United States of America, sundaramoorthi@wustl.edu, Seethu Seetharaman Data Mining can be broadly classified into two groups: supervised learning and unsupervised learning. Supervised learning is the subject of interest in this research as we deal with predicting the units of category-level products sold in stores of a grocery chain. The goal of this research is two-fold. First, prediction models are developed to predict demand. Second, optimum vector X is determined to maximize the profit made by the chain. 2 - High-dimensional Adaptive Dynamic Programming with Mixed Integer Linear Programming Zirun Zhang, University of Texas, Arlington, 500 West First Street, Arlington, TX, 76019, United States of America, zhang.zirun@gmail.com, Victoria Chen, Jay Rosenberger This study addresses the optimization of a real world, complex, dynamic system. The objective is to control the environmental impact of aircraft deicing activities at the Dallas-Fort Worth International Airport. To overcome the complexities such as nonlinear transitions, non-convex objective function, and high-dimensional decision space, an ADP method is introduced using treed regression and MILP. The proposed ADP approach is also compared with a reinforcement learning approach. 3 - Efficient Simulation-based Sampling for Approximate Dynamic Programming Danilo Macció, National Research Council of Italy (CNR-ISSIA), Via De Marini 6, Genova, 16153, Italy, ddmach@ge.issia.cnr.it, Victoria Chen, Cristiano Cervellera We propose a method to generate efficient state sample points for the solution of continuous-state finite-horizon approximate dynamic programming problems. The method is based on the notion on F-discrepancy, which measures how closely a set of points follows a given distribution. The proposed algorithm can be used as an alternative to uniform random sampling when it is difficult to define a priori the state boundaries. Simulation results confirm in practice the effectiveness of the method. 4 - Continuous-state Adaptive Dynamic Programming Prashant Tarun, Associate Professor, Missouri Western State University, Craig School of Business, 4525 Downs Drive, St. Joseph, MO, 64507, United States of America, ptarun@missouriwestern.edu, Victoria Chen, Huiyuan Fan We present a sequential state space exploration (SSSE) approach to adaptively adjust the state space ranges for the experimental design while also sampling useful data for the statistical model. The SSSE approach is coupled with an adaptive value function approximation (AVFA) algorithm that gradually grows the complexity of the statistical model as more data are observed. Sponsor: Data Mining Sponsored Session

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