Web Auctions in Europe

This paper argues that a better understanding of the business model of web auctions can be reached if we adopt a broader view and provide empirical research from different sites. In this paper the business model of web auctions is refined into four dimensions. These are auction model, motives, exchange processes, and stakeholders. One of the objects of this research is to redefine the blurry concept of the business model by analyzing one business model, the web auction model. We show in this research the complexity and diversity of factors contributing to the success of the web auction model. By generalizing the results to the level of business model we also show how complex and diverse business models can be. Motivated by the lack of empirically grounded justification for the mixed business results of web auctions, this paper adopts a qualitative approach that includes telephone interviews with web auctions developed in different European countries.exchange processes;stakeholders;Web auctions

Fault diversity among off-the-shelf SQL database servers

Fault tolerance is often the only viable way of obtaining the required system dependability from systems built out of "off-the-shelf" (OTS) products. We have studied a sample of bug reports from four off-the-shelf SQL servers so as to estimate the possible advantages of software fault tolerance - in the form of modular redundancy with diversity - in complex off-the-shelf software. We checked whether these bugs would cause coincident failures in more than one of the servers. We found that very few bugs affected two of the four servers, and none caused failures in more than two. We also found that only four of these bugs would cause identical, undetectable failures in two servers. Therefore, a fault-tolerant server, built with diverse off-the-shelf servers, seems to have a good chance of delivering improvements in availability and failure rates compared with the individual off-the-shelf servers or their replicated, nondiverse configurations

Towards a normalized trustworthiness approach to enhance security in on-line assessment

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper proposes an approach to enhance information security in on-line assessment based on a normalized trustworthiness model. Among collaborative e-Learning drawbacks which are not completely solved, we have investigated information security requirements in on-line assessment (e-assessment). To the best of our knowledge, security requirements cannot be reached with technology alone, therefore, new models such as trustworthiness approaches can complete technological solutions and support e-assessment requirements for e-Learning. Although trustworthiness models can be defined and included as a service in e-assessment security frameworks, there are multiple factors related to trustworthiness which cannot be managed without normalization. Among these factors we discuss trustworthiness multiple sources, different data source formats, measure techniques and other trustworthiness factors such as rules, evolution or context. Hence, in this paper, we justify why trustworthiness normalization is needed and a normalized trustworthiness model is proposed by reviewing existing normalization procedures for trustworthy values applied to e-assessments. Eventually, we examine the potential of our normalized trustworthiness model in a real online collaborative learning course.Peer ReviewedPostprint (author's final draft

Intrusion detection and prevention of web service attacks for software as a service:Fuzzy association rules vs fuzzy associative patterns

Cloud computing inherits all the systems, networks as well asWeb Services’ security vulnerabilities, in particular for software as a service (SaaS), where business applications or services are provided over the Cloud as Web Service (WS). Hence, WS-based applications must be protected against loss of integrity, confidentiality and availability when they are deployed over to the Cloud environment. Many existing IDP systems address only attacks mostly occurring at PaaS and IaaS. In this paper, we present our fuzzy association rule-based (FAR) and fuzzy associative pattern-based (FAP) intrusion detection and prevention (IDP) systems in defending against WS attacks at the SaaS level. Our experimental results have validated the capabilities of these two IDP systems in terms of detection of known attacks and prediction of newvariant attacks with accuracy close to 100%. For each transaction transacted over the Cloud platform, detection, prevention or prediction is carried out in less than five seconds. For load and volume testing on the SaaS where the system is under stress (at a work load of 5000 concurrent users submitting normal, suspicious and malicious transactions over a time interval of 300 seconds), the FAR IDP system provides close to 95% service availability to normal transactions. Future work involves determining more quality attributes besides service availability, such as latency, throughput and accountability for a more trustworthy SaaS

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports