Ying Liu, Sudha Ram, and Robert Lusch Eller College of Management, University of Arizona {yingliu, ram, rlusch}@eller.arizona.edu Abstract -Market segmentation is an important issue in today’s intensely competitive environment. While many methods have been proposed for market segmentation, they can be classified into two categories: descriptive and predictive. Descriptive methods are optimized for segment identifiability while predictive methods are optimized for segment responsiveness. Most existing segmentation methods cannot effectively address both identifiability and responsiveness goals of market segmentation due to their focus on only one aspect of the multiobjective problem. This paper proposes a new market segmentation method that unifies descriptive and predictive methods by simultaneously optimizing multiple objectives. The unified market segmentation method overcomes the limitations of existing segmentation methods and generates Pareto optimal solution sets. It also suggests the optimal number-of-segments and the best solution based on the characteristics of the Pareto front. As a result, the method presents a unified view of possible segmentation solutions and automatically selects the best solution(s) by balancing the tradeoffs. We demonstrate the benefits of our method by empirically evaluating it using data from a cell phone service provider.
The global adoption of RFID technology presents a number of challenges to IT architecture design. This paper identifies important requirements for RFID data provisioning at an abstract architecture level. A non-invasive architecture style is proposed to satisfy these requirements. Our proposed architecture style has the advantages of low entry barriers and high flexibility. The architecture style is used as a basis for evaluating three existing architectures for RFID data provisioning. Various architecture mismatches that could hinder the pace of RFID adoption are identified and discussed.
The popularity of distributed computing platforms (e.g., Hadoop) is largely due to their ability to address scalability issues that arise from data storage and processing limitations of standard computing systems. However, the decision to dedicate organizational resources and capital for such systems needs a careful consideration of several factors including evaluation of cloud-based distributed computing options. We propose a framework of metrics which we used to conduct an in-depth performance and cost benefit analysis of two standard Hadoop infrastructural choices, i.e., a Platform as a Service ( PaaS ) on-demand cloud setup and a local organizational setup. We evaluated the framework by means of an exploratory data analysis use-case for a large-scale graph processing research problem. Our analysis considered highly granular aspects of distributed computing performance and studied how utilization rates and infrastructure amortization times affect break-even times. We identified that virtual memory management adversely affects the performance of a cloud cluster during the reduce phase with the magnitude of degradation dependent on the type of MapReduce operation. Our study is intended not only as an evaluation of infrastructural choices but also a development of a metric framework that can serve as a baseline for researchers examining distributed infrastructures.
Recent development of wearable sensor technologies have made it possible to capture concurrent data streams for ambient environment and instantaneous physiological stress response at a fine granularity. Characterizing the delay in physiological stress response time to each environment stimulus is as important as capturing the magnitude of the effect. In this paper, we discuss and evaluate a new regularization-based statistical method to determine the ideal lagged effect of five environmental factors-carbon dioxide, temperature, relative humidity, atmospheric pressure and noise levels on instantaneous stress response. Using this method, we infer that the first four environment variables have a cumulative lagged effect, of approximately 60 minutes, on stress response whereas noise level has an instantaneous effect on stress response. The proposed transformations to inputs result in models with better fit and predictive performance. This study not only informs the field of environment-wellbeing research about the cumulative lagged effects of the specified environmental factors, but also proposes a new method for determining optimal feature transformation in similar smart health studies.
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