Zaki Malik

RATEWeb: Reputation Assessment for Trust Establishment among Web services

We introduce RATEWeb, a framework for establishing trust in service-oriented environments. RATEWeb supports a cooperative model in which Web services share their experiences of the service providers with their peers through feedback ratings. The different ratings are aggregated to derive a service provider’s reputation. This in turn is used to evaluate trust. The overall goal of RATEWeb is to facilitate trust-based selection and composition of Web services. We propose a set of decentralized techniques that aim at accurately aggregating the submitted ratings for reputation assessment. We conduct experiments to assess the fairness and accuracy of the proposed techniques.

Geoinformatics: Transforming data to knowledge for geosciences

An integrative view of Earth as a system, based on multidisciplinary data, has become one of the most compelling reasons for research and education in the geosciences. It is now necessary to establish a modern infrastructure that can support the transformation of data to knowledge. Such an information infrastructure for geosciences is contained within the emerging science of geoinformatics, which seeks to promote the utilization and integration of complex, multidisciplinary data in seeking solutions to geoscience-based societal challenges.

Evaluating rater credibility for reputation assessment of web services

This paper investigates the problem of establishing trust in service-oriented environments. We focus on providing an infrastructure for evaluating the credibility of raters in a reputation-based framework that would enable trust-based Web services interactions. The techniques we develop would aid a service consumer in assigning an appropriate weight to the testimonies of different raters regarding a prospective service provider. The experimental analysis show that the proposed techniques successfully dilute the effects of malicious ratings.

Rater Credibility Assessment in Web Services Interactions

We investigate the problem of establishing trust in service-oriented environments. The focus is on providing an infrastructure for evaluating the credibility of raters in a reputation-based framework that would enable trust-based Web services interactions. The techniques we develop aid a service consumer in assigning an appropriate weight to the testimonies of different raters regarding a prospective service provider. The experimental analysis show that the proposed techniques successfully dilute the effects of malicious ratings, thereby facilitating accurate and fair assessment of the providers’ reputations.

Web Services Reputation Assessment Using a Hidden Markov Model

We present an approach for reputation assessment in service-oriented environments. We define key metrics to aggregate the feedbacks of different raters, for assessing a service providers reputation. In situations where rater feedbacks are not readily available, we use a Hidden Markov Models (HMM) to predict the reputation of a service provider. HMMs have proven to be suitable in numerous research areas for modelling dynamic systems. We propose to emulate the success of such systems for evaluating service reputations to enable trust-based interactions with and amongst Web services. The experiment details included in this paper show the applicability of the proposed HMM-based reputation assessment model.

Efficient change management in long-term composed services

We present an efficient framework for managing changes in Long-term Composed Services (LCSs). Because we assume LCSs outsource their functionality from autonomous third-party Web service providers, there is a need to select the best Web service replacements when changes are induced at the LCS level. We propose a framework where managing changes in LCSs is modeled as a dual service query optimization process. In the first phase, reputation is used as a trust mechanism to weed out those Web services that do not exhibit acceptable reputation. In the second phase, the non-functional requirements represented in and by the Quality of Web Service (QoWS) are used to further narrow down the set of reputable Web services to those that also best adhere to the QoWS requirements.

A service computing manifesto: the next 10 years

Mapping out the challenges and strategies for the widespread adoption of service computing.

A Reputation-Based Approach to Preserving Privacy in Web Services

The Web is an environment where users, Web services, and software agents exchange sensitive personal information. This calls for enforceable strategies to preserve people’s privacy. In most solutions, users define their respective privacy requirements and must themselves make the decision about information disclosure. Personal judgments are usually made based on the sensitivity of the information and the reputation of the party to which the information is to be disclosed. The emerging semantic Web is expected to make the challenge more acute in the sense that it would provide a whole infrastructure for the automation of semantics in the Web. On the privacy front, this means that privacy invasion would net more quality and sensitive personal information. In this paper, we propose a reputation-based approach to automate privacy enforcement in a semantic Web environment. We propose a reputation management system that monitors Web services and collects, evaluates, updates, and disseminates information related to their reputation for the purpose of privacy protection.

CAPRA: a comprehensive approach to product ranking using customer reviews

Online shopping generates billions of dollars in revenues, including both the physical goods and online services. Product images and associated descriptions are the two main sources of information used by the shoppers to gain knowledge about a product. However, these two pieces of information may not always present the true picture of the product. Images could be deceiving, and descriptions could be overwhelming or cryptic. Moreover, the relative rank of these products among the peers may lead to inconsistencies. Hence, a useful and widely used piece of information is “user reviews”. A number of vendors like Amazon have created whole ecosystems around user reviews, thereby boosting their revenues. However, extracting the relevant and useful information out of the plethora of reviews is not straight forward, and is a very tedious job. In this paper we propose a product ranking system that facilitates the online shopping experience by analyzing the reviews for sentiments, evaluating their usefulness, extracting and weighing different product features and aspects, ranking it among similar comparable products, and finally creating a unified rank for each product. Experiment results show the usefulness of our proposed approach in providing an effective and reliable online shopping experience in comparison with similar approaches.

A Scalable Middleware for Web Databases

The emergence of Web databases has introduced new challenges related to their organization, access, integration, and interoperability. New approaches and techniques are needed to provide across-the-board transparency for accessing and manipulating Web databases irrespective of their data models, platforms, locations, or systems. In meeting these needs, it is necessary to build a middleware infrastructure to support flexible tools for information space organization, communication facilities, information discovery, content description, and assembly of data from heterogeneous sources. In this paper, we describe a scalable middleware for efficient data and application access that we have built using the available technologies. The resulting system is called WebFINDIT. It is a scalable and uniform infrastructure for locating and accessing heterogeneous and autonomous databases and applications.