The Web turns out to be a collection of services that interoperate through the Internet. As the number of services increase, it is getting more and more diffucult for users to find, filter and integrate these services depending on their requirements. Automatic techniques are being developed to fulfill these tasks. The first step toward automatic composition is the discovery of services needed. UDDI which is one of the accepted web standards, provides a registry of web services. However representation capabilities of UDDI are insufficient to search for services on the basis of what they provide. Semantic web initiatives like OWL and OWL-S are promising for locating exact services based on their capabilities. In this thesis, a new semantic service discovery mechanism is implemented based on OWL-S service profiles. The service profiles of an advertisement and a request are matched based on OWL ontologies describing them. In contrast to previous work on the subject, the ontologies of the advertisement and the request are not assumed to be same. In case they are different, schema matching algorithms are applied. Schema matching algorithms find the mappings between the given schema models. A hybrid combination of semantic, syntactic and structural schema matching algorithms are applied to match ontologies

A comprehensive failure detection model is proposed to detect service failures in asynchronous distributed systems. The proposed model takes advantage of P2P technology to provide required functionality. When compared to similar studies in failure detection, the presented failure detection model is more autonomous in resolving service dependencies, embodies more flexibility in providing different failure detection functions (like unreliable failure detectors, membership services) and offers more security. A failure detection library is developed using JXTA P2P framework to show realization of such a model.

One of the challenging issues for mobile ad hoc network (MANET) applications is data replication. Unreliable wireless communication, mobility of network participators and limited resource capacities of mobile devices make conventional replication techniques useless for MANETs. Frequent network divisions and unexpected disconnections should be handled. In this thesis work, a novel mobility and power aware, data interest based data replication strategy is presented. Main objective is to improve data accessibility among a mission critical mobility group. A clustering approach depending on mobility and data interest patterns similarities is introduced. The investigated replica allocation methodology takes care of data access frequency and data correlation values together with mobile nodes&rsquo / remaining energy and memory capacities. Performance of the proposed approach is analyzed in terms of data accessibility / cache hit ratio and traffic metrics. Improvements are observed by data interest based clustering in addition to mobility awareness over sole mobility aware clustering. Advantages of power aware replica allocation are demonstrated by experimental simulations.

Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. Support vector machines and partial least squares method are used for predicting binding affinity of compounds, and results are compared.

In Web service composition, service discovery and combining suitable services through determination of interoperability among different services are important operations. Utilizing semantics improves the quality and facilitates automation of these operations. There are several previous approaches for semantic service discovery and service matching. In this work, we exploit and extend these semantic approaches in order to make Web service composition process more facilitated, less error prone and more automated. This work includes a service discovery and service interoperability checking technique which extends the previous semantic matching approaches. In addition to this, as a guidance system for the user, a new semantic domain model is proposed that captures semantic relations between concepts in various ontologies.

We tackle the problem of automated code generation for a High Level Architecture (HLA)- compliant federate application, given a model of the federation architecture including the federate&rsquo / s behavior model. The behavior model is based on Live Sequence Charts (LSCs), adopted as the behavioral specification formalism in the Federation Architecture Metamodel (FAMM). The FAMM is constructed conforming to metaGME, the meta-metamodel offered by Generic Modeling Environment (GME). FAMM serves as a formal language for describing federation architectures. We present a code generator that generates Java/AspectJ code directly from a federation architecture model. An objective is to help verify a federation architecture by testing it early in the development lifecycle. Another objective is to help developers construct complete federate applications. Our approach to achieve these objectives is aspect-oriented in that the code generated from the LSC in conjunction with the Federation Object Model (FOM) serves as the base code on which the computation logic is weaved as an aspect.

The adaptiveness provides uniquely identifying and monitoring the learner&rsquo / s learning activities according to his/her respective profile. The adaptive intelligent learning management systems (AILMS) help a wide range of students to achieve their learning goals effectively by delivering knowledge in an adaptive or individualized style through online learning settings. This study presents a multi-agent system, called MODA, developed to provide adaptiveness in learning management systems (LMS). A conceptual framework for adaptive learning systems is proposed for this purpose. The framework is based on the idea that adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of learners and the content type of learning material are used to match the learner to the most suitable content. The thesis covers the pedagogical framework applied in MODA, the technical and multi-agent architectures of MODA, the TCP-IP based protocol providing communication between MODA and LMS, and a sample application of the system to an open source learning management system, OLAT. The study also discusses the possibilities of future interests.

This study proposes a metamodel, named Federation Architecture Metamodel (FAMM), for describing the architecture of a High Level Architecture (HLA) compliant federation. The metamodel provides a domain specific language and a formal representation for the federation adopting Domain Specific Metamodeling approach to HLA-compliant federations. The metamodel supports the definitions of transformations both as source and as target. Specifically, it supports federate base code generation from a described federate behavior, and it supports transformations from a simulation conceptual model. A salient feature of FAMM is the behavioral description of federates based on live sequence charts (LSCs). It is formulated in metaGME, the meta-metamodel for the Generic Modeling Environment (GME). This thesis discusses specifically the following points: the approach to building the metamodel, metamodel extension from Message Sequence Chart (MSC) to LSC, support for model-based code generation, and action model and domain-specific data model integration. Lastly, this thesis presents, through a series of modeling case studies, the Federation Architecture Modeling Environment (FAME), which is a domain-specific model-building environment provided by GME once FAMM is invoked as the base paradigm.

The increase in the use of digital video/image has showed the need for modeling and querying the semantic content in them. Using manual annotation techniques for defining the semantic content is both costly in time and have limitations on querying capabilities. So, the need for content based information retrieval in multimedia domain is to extract the semantic content in an automatic way. The semantic content is usually defined with the objects in images/videos. In this thesis, a Genetic Algorithm based object extraction and classification mechanism is proposed for extracting the content of the videos and images. The object extraction is defined as a classification problem and a Genetic Algorithm based classifier is proposed for classification. Candidate objects are extracted from videos/images by using Normalized-cut segmentation and sent to the classifier for classification. Objects are defined with the Best Representative and Discriminative Feature (BRDF) model, where features are MPEG-7 descriptors. The decisions of the classifier are calculated by using these features and BRDF model. The classifier improves itself in time, with the genetic operations of GA. In addition to these, the system supports fuzziness by making multiple categorization and giving fuzzy decisions on the objects. Externally from the base model, a statistical feature importance determination method is proposed to generate BRDF model of the categories automatically. In the thesis, a platform independent application for the proposed system is also implemented.

There has been no commonsense on how to identify problem domain concerns in architectural modeling of software systems. Even, there is no commonly accepted method for modeling the Non-Functional Requirements (NFRs) effectively associated with the architectural aspects in the solution domain. This thesis introduces the use of a Machine Learning (ML) method based on Support Vector Machines to relate NFRs to classified &quot / architectural concerns&quot / in an automated way. This method uses Natural Language Processing techniques to fragment the plain NFR texts under the supervision of domain experts. The contribution of this approach lies in continuously applying ML techniques against previously discovered &ldquo / NFR - architectural concerns&rdquo / associations to improve the intelligence of repositories for requirements engineering. The study illustrates a charted roadmap and demonstrates the automated requirements engineering toolset for this roadmap. It also validates the approach and effectiveness of the toolset on the snapshot of a real-life project.