PhD Shahab Mokarizadeh

– I am  from Iran. I received my bachelor's degree in Software Engineering from Ferdowsi University of Mashad in Iran while I received my Degree of Master of Science in Software Engineering of Distributed Systems from School of Information and Communication Technology at KTH in 2007.

– My PhD topic is "Web service Ontology Learning, Analysis and Usage for Service Selection and Composition". Despite of such lengthy title, my thesis work is simply about developing Web service analysis methods in large scale. As the utilization and development of Web services grows rapidly, the problem of analysing of existent Web services naturally arises. Among other things, I was and I still am passionate about the analysis method and not to mention the distributed nature of web services.

– A Web service is often characterized as a self-described, self-contained, reusable, and portable software component that can be exposed, discovered, and invoked on the Web . It allows uniform access 

via Web standards for software components residing on different platforms and developed in different languages. The obvious benefit of this computing paradigm is that companies and organizations can develop distributed software systems by assembling basic services that can be discovered dynamically on the Web. Consequently, large number of Web services are available on the Web. The majority of currently available Web services lack any kind of semantics or textual documentation to facilitate their utilization and analysis. This deficiency makes the Web service analysis quite challenging. The problem becomes even more challenging when the number of targeted Web services is quite large and they are collected form heterogeneous domains and this calls for developing (semi-) automated ontology learning methods. At the same time, similarly to many other domains the Web service community is affected by the emergence of Web 2.0 and the increasing popularity of social networks. The user-generated contents such as tags and ratings in social networks are a rich source of information that can be exercised to perform more efficient service selection. Similarly, any method that targets the employment of user profiles in a social network needs to address a plausible solution for privacy issues. The main contribution of this dissertation is the development of techniques and frameworks supporting the exploitation, and analysis of large quantity of Web services from heterogeneous domains that lack mostly any textual documentation.

– In my thesis, I show that the networks resulting from automatically annotated Web services are complex networks, as they exhibit scale-free and small-world properties and exhibit a negative correlation degree of nodes. This allows us to track the performance of leveraged semantic annotation schemes in certain network properties (particularly regarding shortest length paths, clustering coefficients and power-law exponents of node degrees). The main distinguishing feature of the suggested metrics is that they are scalable and computationally inexpensive compared to traditional metrics. With regard to the proposed rivacy-aware profile exploitation method, the evaluation results demonstrate that it is feasible to gain reasonable accuracy for a collaborative filtering based recommendation, while satisfying users’ privacy concerns in a social network.

– The major part of my thesis is grounded in complex network theories and privacy aware exploitation of personal data. Both of these topics are gaining more and more attention from both academia and industry. From network theory perspective, the proposed Web service analysis method can be enriched by augmenting the new metrics and can be applied to similar paradigms, such as Web APIs. While from privacy side the situation is different. In my thesis work, the proposed privacy model’s design is based on the hypothesis that dependency between trust and privacy follows an exponential function. I am looking forward to see to what extend the upcoming privacy models will confirm the proposed model.

– I have already joined a start-up company focused on developing city traffic models and navigation solutions.