Apply Page rank-like approach to web service workflow field. At the same time, no one try to device an engine that validates a composite web service workflow by means of FSM, which has formal theoretical base. In this work, we presented an algorithm to build up the composition FSM, which is a criterion to know a composition is feasible or not. We introduce nested cycle hierarchy to address the problem that PageRank weight assignment would favor cycle structure.

Nowadays, web services have been widely utilized on the Internet. The communication of organizations becomes much easier; thanks to the advances of computer and communication technologies and the inexpensive cost, and the integration of applications within and across business organizations is a trend. In general, there are two approaches in composing web services inside or outside an organization: orchestration and choreography. Previous work in the web service selection is usually based on orchestration model and focuses on the interest of a single party. However, in many application scenarios, business goals are achieved by pair-wise interactions among a set of WSs, and there is no single entity that is in charge of selecting web service for each task. Each web service can autonomously perform web service selection. By autonomy, we maintain that each WS is aware of only its partner web services. In such a choreographic environment, we study the kind of information that each web service should provide to its partner web services and how each web service should perform web service selection so as to maximize the chance of successfully accomplish a business goal. The proposed approach is evaluated by simulating 10,000 execution sequences of the target WS and assumed a fixed operation reliability for each delegation. The experimental results show that our proposed method is close to centralized method and better than other two selection methods, namely random and view-based-propagation-free.

Approved for public release, distribution is unlimited / The purpose of this study is to investigate U.S. Naval Academy student predictors of aviation selection for graduates between 1995 and 2002. The main hypothesis is that the background characteristics that predict aviation selectees will differ from the characteristics that predict non-aviation selectees. Although prior research suggests that several characteristics (academic, cognitive, athletic, and personality traits) play an important role in predicting success in aviation, other research suggests that many of those characteristics have not been included in the service selection process at the Naval Academy. Two empirical models were estimated to investigate this hypothesis. The models were used to determine whether the significance of predictive factors differ between all aviation selectees and non-aviation selectees, and likewise between pilot aviation selectees and non-pilot aviation selectees. The results show that of all of the variables in both models PFAR (an ASTB score) was the most important factor in predicting aviation selection. Both PFAR and academic grade point average at USNA had a large impact on aviation selection and separately on pilot selection. These results were representative of both aviation and pilot selection. It is also important to note that some variables were strong negative predictors in the models, although prior research suggested they would be positive predictors of aviation success. Apparently, the factors that predict success in aviation flight training are not the same that predict selection of the aviation community. / Lieutenant, United States Navy

Current software engineering topics such as clone detection and service selection need to improve the capability of detection process and selection process. The clone detection is the process of finding duplicated code through the system for several purposes such as removal of repeated portions as maintenance part of legacy system. Service selection is the process of finding the appropriate web service which meets the consumer’s request. Both problems can be converted into a matching problem. Matching process forms an essential part of software engineering activities. In this research, a well-known mathematical algorithm Stable Marriage Problem (SMP) and its variations are investigated to fulfil the purposes of matching processes in software engineering area. We aim to provide a competitive matching algorithm that can help to detect cloned software accurately and ensure high scalability, precision and recall. We also aim to apply matching algorithm on incoming request and service profile to deal with the web service as a clever independent object so that we can allow the services to accept or decline requests (equal opportunity) rather than the current state of service selection (search-based), in which service lacks of interacting as an independent candidate. In order to meet the above aims, the traditional SMP algorithm has been extended to achieve the cardinality of many-to-many. This adaptation is achieved by defining the selective strategy which is the main engine of the new adaptations. Two adaptations, Dual-Proposed and Dual-Multi-Allocation, have been proposed to both service selection and clone detection process. The proposed approach (SMP-based) shows very competitive results compare to existing software clone approaches, especially in identifying type 3 (copy with further modifications such update, add and delete statements) of cloned software. It performs the detection process with a relatively high precision and recall compare to the CloneDR tool and shows good scalability on a middle sized program. For service selection, the proposed approach has several advantages such as service protection and service quality. The services gain equal opportunity against the incoming requests. Therefore, the intelligent service interaction is achieved, and both stability and satisfaction of the candidates are ensured. This dissertation contributes to several contributions firstly, the new extended SMP algorithm by introducing selective strategy to accommodate many-to-many matching problems, to improve overall features. Secondly, a new SMP-based clone detection approach to detect cloned software accurately and ensures high precision and recall. Ultimately, a new SMPbased service selection approach allows equal opportunity between services and requests. This led to improve service protection and service quality. Case studies are carried out for experiments with the proposed approach, which show that the new adaptations can be applied effectively to clone detection and service selection processes with several features (e.g. accuracy). It can be concluded that the match based approach is feasible and promising in software engineering domain.

Cloud computing, mobile computing, Service-Oriented Computing (SOC), and Software as a Service (SaaS) indicate that the Internet emerges to an anonymous service market where service functionality can be dynamically and ubiquitously consumed. Among functionally similar services, service consumers are interested in the consumption of the services which perform best towards their optimization preferences. The experienced performance of a service at consumer side is expressed in its non-functional properties (NFPs). Selecting the best-fit service is an individual challenge as the preferences of consumers vary. Furthermore, service markets such as the Internet are characterized by perpetual change and complexity. The complex collaboration of system environments and networks as well as expected and unexpected incidents may result in various performance experiences of a specific service at consumer side. The consideration of certain call side aspects that may distinguish such differences in the experience of NFPs is reflected in various call contexts. Service optimization based on a collaborative knowledge base of previous experiences of other, similar consumers with similar preferences is a desirable foundation. The research work described in this dissertation aims at an individually optimized selection of services considering the individual call contexts that have an impact on the performance, or NFPs in general, of a service as well as the various consumer preferences. The presented approach exploits shared measurement information about the NFP behavior of a service gained from former service calls of previous consumptions. Gaining selection/recommendation knowledge from shared experience benefits existing as well as new consumers of a service before its (initial) consumption. Our approach solely focuses on the optimization and collaborative information exchange among service consumers. It does not require the contribution of service providers or other non-consuming entities. As a result, the contribution among the participating entities also contributes to their own overall optimization benefit. With the initial focus on a single-tier optimization, we additionally provide a conceptual solution to a multi-tier optimization approach for which our recommendation framework is prepared in general. For a consumer-sided optimization, we conducted a literature study of conference papers of the last decade in order to find out what NFPs are relevant for the selection and consumption of services. The ranked results of this study represent what a broad scientific community determined to be relevant NFPs for service selection. We analyzed two general approaches for the employment of machine learning methods within our recommendation framework as part of the preparation of the actual recommendation knowledge. Addressing a future service market that has not fully developed yet and due to the fact that it seems to be impossible to be aware of the actual NFP data of different Web services at identical call contexts, a real-world validation is a challenge. In order to conduct an evaluation and also validation that can be considered to be close approximations to reality with the flexibility to challenge the machine learning approaches and methods as well as the overall recommendation approach, we used generated NFP data whose characteristics are influenced by measurement data gained from real-world Web services. For the general approach with the better evaluation results and benefits ratio, we furthermore analyzed, implemented, and validated machine learning methods that can be employed for service recommendation. Within the validation, we could achieve up to 95% of the overall achievable performance (utility) gain with a machine learning method that is focused on drift detection, which in turn, tackles the change characteristic of the Internet being an anonymous service market.

Open distributed environments such as the World Wide Web facilitate information sharing but provide limited support to the protection of sensitive information and resources. Web services have become a part of components for quickly building a business process that satisfies the business goal of an organization, and access control is imperative to prevent the illegal access of sensitive information. In recent years, several researches have investigated the Web services-based workflow access control problem, and selection approaches for choosing the performer for each task so as to satisfy all access control constraints have been proposed. Based on the role-based access control model, we focus on two types of access control: separation of duties and binding of duties. Both role-level and participant-level of SoDs and of BoDs that need to be dynamically enforced are considered in this thesis. While dealing with complex and flexible business logics, we use rule engine to reasons with the business facts to get the result based on business rules. The proposed approach is evaluated by a workflow scenario and is shown to be flexible to develop new process with dynamic access control constraints at the cost of higher execution time.

As the emergence of SOA concept, web services has became a key technology to achieve the seamless system interoperability and collaborations with enterprises partners. Since many available web services provide overlapping or identical functionality, when it comes to composing a composite web service, a choice needs to be made for selecting an appropriate component web service. Dynamic web service selection refers to determining a subset of component web services to be invoked so as to orchestrate a composite web service. Previous work in web service selection usually assumes the invocations of web service operations to be independent of on another. But this assumption however does not hold in practice as both the composite and component web services often impose some orderings on the invocation of their operations to represent its business logic. Such orderings constrain the selection of component web services to orchestrate the composite web service. We therefore propose to use finite state machine (FSM) to model the invocation order of web service operations. We define a measure, called aggregated reliability, to measure the probability that a given state in the composite web service will lead to successful execution in the context where each component web service may fail with some probability. We show that the computation of aggregated reliability is equivalent to eigenvector computation. We also propose two strategies to select component web services that are likely to successfully complete the execution of a given sequence of operations. For our approach to work in a practical environment, the dominating composition language BPEL for specifying the operation invocation orders will be transformed into an abstract FSM. We also proposed a prototype for realizing our dynamic WS selection. Our experiments on a generated set of web service operation sequences show that our proposed strategies perform better than two baseline selection strategies.

Web services have emerged as a de facto standard for encapsulating services within or across organization boundaries. Various proposals have been made to compose Web services into workflow so as to meet the goal previously unaccomplished by a single entity. This thesis intends to investigate the Web services-based workflow access control problem. It starts by analyzing the various access control constraints proposed in the literatures and presenting three primitive constructs that are capable of specify these constraints. It then proposes a Web service selection approach that dynamically chooses a performer for each task in the workflow, not only to satisfy all access control constraints currently but also to increase the chance of completing the entire process in the future. The proposed approach is evaluated using synthetic data and is shown to result in the execution that is less likely to violate any specified access control constraints.

Web services have become the de facto standards as components for quickly building a business process that satisfies the business goal of an organization. Nowadays, Web services have found its way into describing the functions of automatic tasks as well as manual tasks. An important part in the specification of a business process, especially for manual tasks, is the access control. This thesis considers both types of tasks involved in a Web services-based process with its corresponding access control problem and proposes a selection approach for choosing the performer for each task so as to satisfy all access control constraints. Based on the role-based access control model, we focus on two types of access control: separation of duties (SoD) and binding of duties (BoD). Both role-level and participant-level of SoDs and of BoDs that need to be dynamically enforced and these constraints are considered in this thesis. The proposed performer selection approach is evaluated by a workflow scenario and is shown to have the highest chance of satisfying all predefined access control constraints when compared to other methods.

Nowadays, a multitude of functionally equal web services are available. By thisbroad offer, the need of a service recommendation based on non-functional characteristics(e.g. price, response time, availability) is increasing. The static ServiceLevel Agreements (SLAs) of service providers cannot suffice this need. SLAs arenot reliable enough, due to the fact that they do not cover the dynamic performanceand quality changes of services during their lifetime. This bachelor’s thesis waswritten within a research project of the Linnaeus University in Sweden and the KarlsruheUniversity of Applied Science in Germany. The goal of this research projectis to eliminate the issues as described above. For this reason, a framework for anoptimized service selection was developed. Instead of using the static SLAs, measurementsof each service call are taken. On the basis of the measurements and therequirements of the consumer, the framework then provides an automated best-fitservice selection. The purpose of this thesis is to involve the geographic location of each serviceconsumer in the automated service selection. Therefore, a mobile app was developedto get a sufficient amount of real world test data. This app measures service calls andadditionally records the geographic location of the user. Based on the geographiclocation, the collected measurement data then were grouped into regions. Thereby,it could be shown that the geographic location of the user can be used to improve theoptimal service selection. / Service-Oriented Computing