43794304
Minitrack Introduction
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IT-enabled transparent electronic markets: the case of the air travel industry
The electronic market hypothesis predicts that by reducing coordination costs, information technology (IT) will shift industrial organization from hierarchical to market-based forms of economic activity. While academic researchers and practitioners have witnessed these shifts with the advent of the Internet, there is little understanding about the process and the underlying forces that drive them. In this paper, we provide an in-depth analysis of the air travel industry, which has recently experienced significant IT-driven transformations. We conclude that, together with IT, pro-competitive laws and the information-intensive nature of air travel products have triggered competition for consumers with transparent market mechanisms, which is leading to the emergence of more transparent electronic markets in the air travel industry.
30721210
Economic analysis of consumer purchase intentions in electronic and traditional retail channels: competitive and strategic implications
We develop a multiattribute economic model to analyze consumer behavior for traditional and electronic channels based on consumers' risk profiles and risk-neutral or risk-averse. We then conduct comparative analyses of consumer purchase decisions to derive strategies for different channels. We study the attitudinal and economic factors that lead to specific purchase decisions under different combinations of price, delivery time, and product customization. We develop theoretical propositions concerning consumer channel switching behavior, incentives to consumers, and pricing strategies for different channels. We also use simulations to gain insights for cases where analytical model becomes intractable.
57631
Insights and analyses of online auctions
166893960
Priority pricing of integrated services networks
7914809
Predicting Bidders' Willingness to Pay in Online Multiunit Ascending Auctions: Analytical and Empirical Insights
We develop a real-time estimation approach to predict bidders' maximum willingness to pay in a multiunit ascending uniform-price and discriminatory-price (Yankee) online auction. Our two-stage approach begins with a bidder classification step, which is followed by an analytical prediction model. The classification model identifies bidders as either adopting a myopic best-response (MBR) bidding strategy or a non-MBR strategy. We then use a generalized bid-inversion function to estimate the willingness to pay for MBR bidders. We empirically validate our two-stage approach using data from two popular online auction sites. Our joint classification-and-prediction approach outperforms two other naive prediction strategies that draw random valuations between a bidder's current bid and the known market upper bound. Our prediction results indicate that, on average, our estimates are within 2% of bidders' revealed willingness to pay for Yankee and uniform-price multiunit auctions. We discuss how our results can facilitate mechanism-design changes such as dynamic-bid increments and dynamic buy-it-now prices.
8269092
Color and texture fusion: application to aerial image segmentation and GIS updating
Abstract This paper describes a new algorithm for combining color and texture information for the segmentation of color images. The algorithm uses maximum likelihood classification combined with a certainty based fusion criterion. The algorithm was validated using mosaics of real color textures. It was also tested on real outdoor color scenes and aerial images. This algorithm is part of a more complex system, which is currently being designed to assist an operator in updating an old map of an area using aerial images.
14870566
An information fusion framework for robust shape tracking
Existing methods for incorporating subspace model constraints in shape tracking use only partial information from the measurements and model distribution. We propose a unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model. The resulting nonorthogonal subspace projection and fusion are natural extensions of the traditional model constraint using orthogonal projection. We present two motion measurement algorithms and introduce alternative solutions for measurement uncertainty estimation. We build shape models offline from training data and exploit information from the ground truth initialization online through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the motion estimation errors are heteroscedastic in nature, each heart has a distinct shape, and the relative motions of epicardial and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the existing shape-space-constrained tracking algorithm. Due to the complete treatment of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases.
17779534
Power Consumption Analysis of Maximum A Posterior Classifier Using LU Decomposition and Jacobi Iterations
In tracking applications of wireless sensor networks (WSNs), the classification of an object or event of interest is envisaged to be one of the most computationally intensive tasks that recur frequently over the lifetime of the network. It is imperative that the implementations of such tasks be power efficient and computationally feasible for resource-constrained WSNs. Existing implementations of the best known classifiers such as maximum Aposterior (MAP) classifier are computationally infeasible for WSN environments. The focus of this paper is to investigate computational techniques to realize power efficient distributed implementation of the MAP classifier. In the MAP classifier, one of the most computationally challenging steps is the computation of the inverse of the covariance matrices. In this paper, we study computationally efficient methods for realizing the inverse of a matrix. We present a detailed discussion of one-sided Jacobi Iterations and LU Decomposition for approximating and computing the inverse of the covariance matrices. For LU Decomposition-based solutions, we also apply folding techniques to ensure equal power dissipation among the sensor network nodes. Our contribution lies in the power consumption analysis of executing the MAP classifier using LU decomposition and one-sided Jacobi Iterations to substitute the inverse of the covariance matrices. We show that MAP with one-sided Jacobi Iterations greatly simplifies classification process and makes it more feasible and efficient choice for sensor network applications.
14792710
Enabling analysts in managed services for CRM analytics
Data analytics tools and frameworks abound, yet rapid deployment of analytics solutions that deliver actionable insights from business data remains a challenge. The primary reason is that on-field practitioners are required to be both technically proficient and knowledgeable about the business. The recent abundance of unstructured business data has thrown up new opportunities for analytics, but has also multiplied the deployment challenge, since interpretation of concepts derived from textual sources require a deep understanding of the business. In such a scenario, a managed service for analytics comes up as the best alternative. A managed analytics service is centered around a business analyst who acts as a liaison between the business and the technology. This calls for new tools that assist the analyst to be efficient in the tasks that she needs to execute. Also, the analytics needs to be repeatable, in that the delivered insights should not depend heavily on the expertise of specific analysts. These factors lead us to identify new areas that open up for KDD research in terms of 'time-to-insight' and repeatability for these analysts. We present our analytics framework in the form of a managed service offering for CRM analytics. We describe different analyst-centric tools using a case study from real-life engagements and demonstrate their effectiveness.
18770378
A new model for the recovery of cylindrical structures from medical image data
We introduce a novel analytic model formulation for recovering cylindrical structures (e.g., blood vessels) from segmented 3-D medical image data. Unlike all previous formulations, our model is capable of describing a cylinder with an arbitrary spine (a space curve based on cubic B-splines) and arbitrary cross section which is guaranteed to be orthogonal to the spine. Given this expressiveness, we are able to provide a second order continuous approximation to the centerline of nearly any tubular object. This information may be used for such tasks as a reformatting of the original image data in order to visually detect stenoses or aneurysms. In addition, the cross-section parameter values of our model may aid in automatically isolating these regions. We maintain a relatively simple cross-section function to make this detection straightforward (note that any cross-section function is possible). To describe fine detail in the data, we employ local finite element deformations from the model surface. Thus we are able to recover gross geometric approximations as well as quantify characteristics of the object such as its surface area. We apply our model to the recovery of both a healthy and diseased aorta from segmented CT acquisitions.