33156757
Technology roles and paths of influence in an ecosystem model of technology evolution
We propose a new conceptual model for understanding technology evolution that highlights dynamic and highly interdependent relationships among multiple technologies. We argue that, instead of considering technologies in isolation, technology evolution is best viewed as a dynamic system or ecosystem that includes a variety of interrelated technologies. By considering the interdependent nature of technology evolution, we identify three roles that technologies play within a technology ecosystem. These roles are components, products and applications, and support and infrastructure. Technologies within an ecosystem interact through these roles and impact each others’ evolution. We also classify types of interactions between technology roles, which we term paths of influence. We demonstrate the use of our proposed model through examples of wireless networking (Wi-Fi) technologies and a business mini-case on the digital music industry.
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.
166893960
Priority pricing of integrated services networks
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.
17233820
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.
57631
Insights and analyses of online auctions
5988621
Motion estimation using ordinal measures
We present a method for motion estimation using ordinal measures. Ordinal measures are based on relative ordering of intensity values in an image region called rank permutation. While popular measures like the sum-of-squared-difference (SSD) and normalized correlation (NCC) rely on linearity between corresponding intensity values, ordinal measures only require them to be monotonically related so that rank permutations between corresponding regions are presented. This property turns out to be useful for motion estimation in tagged magnetic resonance images. We study the imaging equation involved in two methods of tagging and observe temporal monotonicity in intensity under certain conditions though the tags themselves fade. We compare our method to SSD and NCC in a rotating ring phantom image sequence. We present an experiment on a real heart image sequence which suggests the suitability of our method.
107769190
Hack I.T. - Security Through Penetration Testing
Hack I.T. introduces penetration testing and its vital role in an overall network security plan. You will learn about the roles and responsibilities of a penetration testing professional, the motivation and strategies of the underground hack community, and potential system vulnerabilities, along with corresponding avenues of attack. Most importantly, the book provides a framework for performing penetration testing and offers step-by-step descriptions of each stage in the process. The latest information on the necessary hardware for performing penetrating testing, as well as an extensive reference on the available security tools, is included.
5989555
Enhanced Business Intelligence using EROCS
The EROCS technology automatically links unstructured data with relevant structured data from an external relational database. We demonstrate how EROCS can be used for enhancing business intelligence by allowing OLAP tools to analyze structured and unstructured data in a consolidated manner. Our demonstration showcases the use of EROCS in exploiting latent information in customer emails, which helps in building a complete view of the customer. This results in new insights about the business which are not possible with the existing state of the art.
17746198
Large—scale parallel numerical integration
We present and analyze strategies which can be used for the parallel computation of large numbers of integrals which may be of different levels of difficulty. Parallelization on the integral level, which is generally used for large numbers of integrals, is combined with parallelization on the subregion level, which enables handling local integration difficulties within individual problems. This results in a new, hierarchical algorithm which incorporates load balancing on the integral level and on the subregion level. We report test results of the software and show that the hierarchical approach leads to a scalable integration algorithm.
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.