M. Omair Shafiq

On the Social Aspects of Personalized Ranking for Web Services

Ranking is an important step once automated discovery in Web Services is enabled, it allows for automated selection of the best matched service, out of the discovered ones. However, automated selection of the best matched service is not as simple as it may look like. Different service consumers may have different preferences to select the service providers, which may even depend upon their past interactions. Various approaches have been proposed that allow ranking of services based on different functional and non-functional aspects. However, we believe that the selection of services based on the analysis of the past interactions of service consumers or their social-network could be another effective way to rank the services for the benefit of service consumers. In this paper, we present a community-aware personalized approach for recommending and ranking Web Services for a service consumer. It is based on analysis of historical interactions among service consumers and service providers. We perform analysis and mining on the log information of service consumers and service providers, model their past interactions as social network, apply standard social-network analysis techniques, and use this information in ranking Web Services.

Investigating Semantic Web Service Execution Environments: A Comparison between WSMX and OWL-S Tools

The application of semantics in Web services as semantic Web services for dynamic discovery, composition, invocation and monitoring has been very helpful in enabling Enterprise Application Integration and E-Commerce. There are many initiatives that aim to realize the semantic Web services to enable effective exploitation of semantic annotations, and two major of them are Web service modeling ontology (WSMO) and ontology Web language for services (OWL-S). Several tools have been developed to realize both the conceptual models i.e. Web services execution environment (WSMX) is the reference implementation for WSMO, on the other side OWL-S reference implementation exists in the form of loose collection of individual tools like OWL-S Editor, OWL-S matchmaker, OWL-S virtual machine, OWL-S IDE, WSDL20WL-S converter and OWL-S2UDDI converter etc. In this paper, we have conducted a comparison of both the reference implementations to identify similarities and differences between them and to evaluate their potential to become widely accepted implementation recommendations.

Scalable fault tolerant Agent Grooming Environment: SAGE

Scalable fault tolerant Agent Grooming Environment (SAGE) is first open source initiative in South-Asia. It is a multi-agent system which has been developed according to FIPA (Foundation for Intelligent Physical Agents) 2002 specifications. SAGE has been designed with a distributed and decentralized architecture to achieve fault tolerance and scalability as its key features. Due to these characteristics, SAGE is not only regarded as 2nd generation Multi Agent System but also provides a competitive edge over other platforms.

On personalizing Web search using social network analysis

Most of the existing Web search solutions are built for satisfying broad set of users regardless whether naive or professionals. Further, with the emergence of high speed internet applications and advanced Web 2.0 based Rich Internet Applications (i.e. blogs, wikis, etc.), it has become much easier for users to publish data over the Web. This brings a challenge for Web search solutions to let individual users find the right information as per their preferences. Different users of the Web may have different preferences. Search results for the same query from different users may differ in priority for individual users. In this paper, we describe our approach of enabling personalized Web search for users based on their preferences. It is a challenge in itself to have the preferences of the users known to and considered by search engines. We have designed and developed our unique approach of finding the preferences of users from the relevant parts of their social networks and communities. We believe that the information related to the queries posed by users may have strong correlation with relevant information in their social networks. In order to find out the personal interests and the social-contexts, we find out (1) activities of users in their social-networks, and (2) relevant information from users social networks, based on our proposed trust and relevance matrices. We have developed a mechanism that extracts information from a users social network and uses it to re-rank the results from a search engine. We have also discussed the implementation and evaluation of our proposed solution by emphasizing how it improves the Web search.

Log based business process engineering using fuzzy web service discovery

Business process engineering and mining is a technique that allows discovery, analysis and modeling of possible Business Processes based on information gathered from enterprise information systems. Most of currently available business process engineering and mining techniques either focus on machine learning techniques to mine, discover and model any possible Business Processes from raw data, or use semantically-enabled process models and service descriptions to construct and represent complex Business Processes. However, in real-life scenario, all the required services are not always available and hence exact matching of the services in order to construct Business Process is not possible. In this paper, we present our approach of using fuzzy Web Service discovery to construct and represent Business Processes. It helps in relaxing the matching criteria of Web Services, and allows service consumers to specify business requirements in a more fuzzy way, and hence increases the possibility of finding required Web Services that could construct Business Processes. The paper presents the proposed solution then reports and discusses the evaluation.

Triple space computing: adding semantics to space-based computing

Triple Space Computing (TSC) is a very simple and powerful paradigm that inherits the communication model from Tuple Space Computing and projects it in the context of the Semantic Web In this paper, we propose Triple Space Computing as a new communication and coordination framework for Semantic Web and Semantic Web Services We identify the value added by TSC and propose the overall architecture of TSC and the interactions among different components.

Light-weight semantics and Bayesian Classification: A hybrid technique for dynamic Web Service discovery

Web Service discovery and ranking has been one of the key issues in Service Oriented Systems. Enormous efforts and research has been done towards semantic modeling of Web Services and a couple of semantic matchmaking and reasoning mechanisms have been developed to allow service consumers search for the required service providers dynamically. These approaches seem to be promising in theory, provided that exhaustive semantic descriptions of the services are available. However, in practice, this is not the case, as current Web Service standards provide quite limited information about services. Therefore, the process of discovery as well as ranking cannot always rely only on the extensive semantic descriptions to be available all the time. However, description of services using light-weight semantics (i.e., non-functional properties) is rather easier to have, and this could be used by classification and machine learning techniques to help in the classification of Web Services at real-time. In this paper, we present a hybrid approach towards enabling dynamic Web service discovery which is based on Bayesian Classification mechanism that classifies different available Web services, representing service providers, based on light-weight semantic descriptions.

Community Aware Personalized Web Search

Searching for the right information over the Web is not straight-forward. In the era of high speed internet, high capacity networks, and interactive Web applications, it has become even easier for the users to publish data online. A huge amount of data is published over the internet; every data is in the form of web pages, news, blogs and other material, etc. Similarly, for search engines like Google and Yahoo, it becomes rather hard to find out the right information, i.e., as per user’s preferences; search results for same query differ in priority for different users. In this paper, we proposed a way to prioritize search results of search engines like Google, based on the personal interests and context of users. In order to find out personal interest and context, we follow a unique approach of (1) finding out activities of a user of his/her social-network, (2) finding out what information does the social networks (i.e., friends and community) provide to the user. Based on this information, we have developed a methodology that takes into account the information about social networks and prioritize search results from Web search engine.

Reducing search space for Web Service ranking using semantic logs and Semantic FP-Tree based association rule mining

Ranking and Adaptation (used interchangeably) is often carried out using functional and non-functional information of Web Services. Such approaches are dependent on heavy and rich semantic descriptions as well as unstructured and scattered information about any past interactions between clients and Web Services. Existing approaches are either found to be focusing on semantic modeling and representation only, or using data mining and machine learning based approaches on unstructured and raw data to perform discovery and ranking. We propose a novel approach to allow semantically empowered representation of logs during Web Service execution and then use such logs to perform ranking and adaptation of discovered Web Services. We have found that combining both approaches together into a hybrid approach would enable formal representation of Web Services data which would boost data mining as well as machine learning based solutions to process such data. We have built Semantic FP-Trees based technique to perform association rule learning on functional and non-functional characteristics of Web Services. The process of automated execution of Web Services is improved in two steps, i.e., (1) we provide semantically formalized logs that maintain well-structured and formalized information about past interactions of Services Consumers and Web Services, (2) we perform an extended association rule mining on semantically formalized logs to find out any possible correlations that can used to pre-filter Web Services and reduce search space during the process of automated ranking and adaptation of Web Services. We have conducted comprehensive evaluation to demonstrate the efficiency, effectiveness and usability of our proposed approach.

Frequent Pattern Mining Using Semantic FP-Growth for Effective Web Service Ranking

Automated Ranking is crucial in the process of automated Web Services execution. Often adaptation and ranking (used interchangeably) of discovered Web services is carried out using functional and non-functional information of Web Services. Existing approaches are either found to be only focusing on semantic modeling and representation only, or using data mining and machine learning based approaches on unstructured and raw data to perform discovery and ranking. We propose an approach to allow semantically formalized representation of logs during Web Service execution and then use such logs to perform ranking and adaptation of discovered Web Services. We have built Semantic FP-Tree based technique to perform association rule learning on functional and non-functional characteristics of Web Services. The process of automated execution of Web Services is improved in two steps, i.e., (1) we provide semantically formalized logs that maintain well-structured and formalized information about past interactions of Services Consumers and Web Services, (2) we perform an extended association rule mining on semantically formalized logs to find out any possible correlation in functional and non-functional characteristics of Web Services during past execution which is then used in automated ranking and adaptation of Web Services.