18767986
Applying Hypothetical Queries to E-Commerce Systems to Support Reservation and Personal Preferences
Online travel continues to be one of the most successful e-commerce categories on the Internet. As increasing number of people book online, existing e-ticket systems can not satisfy customers' growing demand. Customers complain about most of the current e-ticket systems for the lack of "reservation" functionality. They also complain that these systems are not "intelligent" enough as a traditional travel agent who can deal with more complicated personal preferences. In this paper, we propose a transaction model for e-commerce system to support reservation functionality while balancing the benefits and risks of both customers and airlines. We introduce the theory of hypothetical query to avoid unnecessary transactions and rollbacks on underlying database. Hypothetical queries can be generated automatically and transformed into relational algebra queries which can be optimized with conventional techniques. We extend the lazy approach for evaluating hypothetical queries with integrity constraints to deal with customers' personal preferences. We also provide the "watch" and "remind" mechanism to help customers to purchase their most desirable tickets. Although we have chosen ticket booking system as our target application, the framework presented in this paper is also applicable to other e-commerce systems.
11913920
Content-Based Trust Mechanism for E-commerce Systems
Previous trust mechanisms are mainly focused on reputational models based on explicit trust ratings. However, the large amount of system-provided and user-generated content published on Web is often ignored. These information is very important for providing more accurate, fine-grained and efficient trust management. In this paper, we propose a content-based trust mechanism and introduce trust reasoning functionalities which can infer a user's interests and expertise. We present a new method to calculate user similarity based on their feedbacks on reviews to reduce data sparseness of User Similarity matrix. We pinpoint several characteristics of trust which have been overlooked previously: trust is field-dependant, similarity-sensitive, personalized and etc. we differ various trust levels between pairs of users to support more accurate and efficient trust calculation. Although we choose Epinions as our targeted application, the mechanism proposed in this paper are also applicable to other e-commerce systems.
16665207
RCCtrust: A Combined Trust Model for Electronic Community
Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models: first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCCtrust – a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and community context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users' interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.
14869838
A Social Network-Based Trust Model for the Semantic Web
Trust is very essential to secure and high quality interactions on the Semantic Web. In this paper, we introduce a trust model, which incorporates pairwise trust ratings and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust values. We interpret trust in two dimensions to better deal with it. We apply the theory in social network field to calculate trust and simulate the trust relationships between humans. We also take advantage of formulas in probability and statistics to analyze the similarities between agents. The algorithm of the trust model aims to be simple, efficient and flexible.
18873066
Extending Online Travel Agency with Adaptive Reservations
Current online ticket booking systems either do not allow customers to reserve a ticket with a locked price, or grant a fixed reservation timespan, typically 24 hours. The former often leads to false availability: when a customer decides to purchase a ticket after a few queries, she finds that either the ticket is no longer available or the price has hiked up. The latter, on the other hand, may result in unnecessary holdback: a customer cannot purchase a ticket because someone else is holding it, who then cancels the reservation after an excessively long period of time. False availability and holdback routinely lead to loss of revenues, credibility and above all, customers. To rectify these problems, this paper introduces a transaction model for e-ticket systems to support a reservation functionality: customers can reserve tickets with a locked price, for a timespan that is determined by the demands on the tickets, rather than being fixed for all kinds of the tickets. We propose a method for implementing the model, based on hypothetical queries and triggers. We also show how to adjust the reservation timespan w.r.t. demands. We experimentally verify that our model and methods effectively reduce both false availability and holdback rates. These yield a practical approach to improving not only e-ticket systems but also other e-commerce systems.
1164569
Impact of time-selective fading on the performance of quasi-orthogonal space-time-coded OFDM systems
In this paper, we study the impact of time-selective fading on quasi-orthogonal space-time (ST) coded orthogonal frequency-division multiplexing (OFDM) systems over frequency-selective Rayleigh fading channels. OFDM is robust against frequency-selective fading, but it is more vulnerable to time-selective fading than single-carrier systems. In ST-OFDM, channel time variations cause not only intercarrier interference among different subcarriers in one OFDM symbol, but also intertransmit-antenna interference. We quantify the impact of time-selective fading on the performance of quasi-orthogonal ST-OFDM systems by deriving, via an analytical approach, the expressions of carrier-to-interference and signal-to-interference-plus-noise ratios. We observe that system error performance is insensitive to changes in vehicle speeds and the channel power-delay profile, but very sensitive to changes in the number of subcarriers. We also evaluate the performance of five different detection schemes in the presence of time-selective fading. We show that although there exist differences in their relative performances, all these detection schemes suffer from an irreducible error floor.
4002740
Selectivity Estimation by Batch-Query based Histogram and Parametric Method
Histograms are used extensively for selectivity estimation and approximate query processing. Workload-aware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlaying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users' interest. However, it takes long time to 'warm-up' (i.e., a large number of queries need to be processed before the histogram can provide a satisfactory coverage and accuracy). Thus, it is less effective to adapt with workload pattern changes. In this paper, we propose a novel online query scheduling algorithm which can significantly reduce the warm-up time for dynamic histograms. A parametric method is proposed to remedy the problem of inaccurate query selectivity estimation for the areas with poor histogram coverage. Experimental results demonstrate a significant effectiveness and accuracy improvement of our approach.
759478
A New Load Balancing Scheme for Distributed Multi-Agent Simulations
Disclosed is a heat sink clip assembly for releaseably attaching a heat sink to a DIP or similar electronic device package. The clip assembly includes a non-conductive attachment clip which extends between the leads on the underside of the DIP and means for latching the attachment clip to a heat sink disposed adjacent the opposite side of the electronic device package with the heat sink in intimate thermal contact with the electronic device package.
7761771
Access control in peer-to-peer collaborative systems
As an emerging model of communication and computation, peer-to-peer networking represents a fully distributed, cooperative network design, and has recently gained significant acceptance. Peer groups share the properties of peer-to-peer overlay network, including full decentralization, symmetric abilities, and dynamism, which make security problems more complicated. In this paper, we propose a fine-grained and attribute-based access control framework for peer-to-peer systems. This design employs a novel policy model, which extends role-based trust management language RT to satisfy security requirements of peer groups. Intend for a pure decentralized model without centralized server, our framework presents distributed delegation authorization mechanism, which could avoid single point of failure. We also introduce our implementation experience.
5444016
Arbitrary warped document image restoration based on segmentation and Thin-Plate Splines
Warping is a common appearance in camera captured document images. It is the primary factor that makes such kind of document images hard to be recognized. Therefore it is necessary to restore warped document image before recognition. In this paper, a novel restore method is presented. The method takes a rough line segmentation and character segmentation firstly in order to estimate the warping direction. Then several pairs of key points mapping between the original image and the restored image are determined and thin-plate splines (TPS), which is an interpolation algorithm, is introduced to restore the image. Such process can effectively describe the warping direction of the document and successfully restore the image. Some experimental results show the effect of the image restoration and compare the recognition rate before and after the restoration based on a same OCR application.