Web Services technologies are established to deliver elasticity, inter-connection of dissimilar applications and have become popular. When user demand is not fulfilled, a composition is implemented to compose the existing services. It comforts to link over World Wide Web. Web-Service-Composition (WSC) is one of the flexible ways of integration of application and resource sharing. It aims to establish several services that handle the user necessities. It allows developers to create applications by focusing on the service oriented part. When lots of web services are available with similar characteristics and user is unable to decide which services are suitable, then we need to perform the composition of services. Performing WSC depends upon QoS parameter. Composing is a difficult problem and should guarantee quality parameters. Different approaches can be used and these must deal with different issues like performance. In our proposed work, we present the composed services with different performance parameters. In order to resolve the composed services, combining of different approaches were used.

Analysis of human activity, e.g., by tracking and analyzing motion information or vital signs became lots of attention in medical as well as athletic appliances during the last years. Nonetheless, comprehensive and labeled datasets containing human motion information are only sparsely accessible to the public. Especially qualitatively labeled datasets are rare, although they are of great value for the development of concepts concerning qualitative motion assessment, e.g., to avoid injuries during athletic workouts or to optimize a training’s success. Therefore, we provide an open and qualitative as well as quantitative labeled dataset containing acceleration and rotation data of 8 different body weight exercises, conducted by 26 study participants. It encompasses more than 11,000 exercise repetitions of which we extracted 8,576 into individual segments. We believe, that due to its structure and labeling our work is suitable to serve for development, benchmarking, and validation of new concepts for human activity recognition and qualitative motion assessment (Publication notes: The dataset will be published at http://github.com/andrebert/body-weight-exercises together with this paper’s presentation on the MobiHealth conference 2017, taking place in Vienna, 14–16 November.).

Service-Oriented Computing enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Selecting a near-optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, particularly in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this, current approaches do not differentiate between the QoS of services themselves and the network. Therefore, the computed latency differs from the actual latency, resulting in suboptimal QoS. Thus, we propose a network-aware approach that handles the QoS of services and the QoS of the network independently. First, we build a network model in order to estimate the network latency between arbitrary services and potential users. Our selection algorithm then leverages this model to find compositions with a low latency for a given execution policy. We employ a self-adaptive genetic algorithm which balances the optimization of latency and other QoS as needed and improves the convergence speed. In our evaluation, we show that our approach works under realistic network conditions, efficiently computing compositions with much lower latency and otherwise equivalent QoS compared to current approaches.

In earlier work, we introduced flexible inference and decision-theoretic metareasoning to address the intractability of normative inference. Here, rather than pursuing the task of computing beliefs and actions with decision models composed of distinctions about uncertain events, we examine methods for inferring beliefs about mathematical truth before an automated theorem prover completes a proof. We employ a Bayesian analysis to update belief in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.

We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show how we can cluster distinctions into groups of distinctions at progressively higher levels of abstraction, and describe rules for decision making with the abstractions. The techniques introduce a utility-based perspective on the nature of concepts, and provide a means of simplifying decision models used in automated reasoning systems. We demonstrate the techniques by describing the capabilities and output of TUBA, a program for utility-based abstraction.

Service-Oriented Computing (SOC) enables the composition of loosely coupled service agents provided with varying Quality of Service (QoS) levels, effectively forming a multiagent system (MAS). Selecting a (near-)optimal set of services for a composition in terms of QoS is crucial when many functionally equivalent services are available. As the number of distributed services, especially in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this and opposed to most MAS approaches, current service approaches depend on a centralized architecture which cannot adapt to the network. Thus, we propose a scalable distributed architecture composed of a flexible number of distributed control nodes. Our architecture requires no changes to existing services and adapts from a centralized to a completely distributed realization by adding control nodes as needed. Also, we propose an extended QoS aggregation algorithm that allows to accurately estimate network QoS. Finally, we evaluate the benefits and optimality of our architecture in a distributed environment.

Optimizing the Quality-of-Service (QoS) levels of a service workflow is essential for the user satisfaction in Service-oriented Computing. For that purpose, QoS computation models are applied to reflect the actual QoS experienced by the user during service execution. Current QoS models ignore the possible dependencies of QoS attributes, such as the dependency on the time of the execution or on the input data supplied to the service. Apart from that, composition approaches consider only single workflows during service selection, narrowing the number of possible compositions. Thus, we introduce a novel QoS model that covers QoS dependencies and discuss how this model can be used to consider multiple workflows at the same time. Moreover, we adopt a multi-objective optimization approach to offer solutions varying in QoS such as finishing time and price, allowing the user to make fine-grained decisions.

Service composition algorithms are used for realizing loosely coupled interactions in Service-Oriented Computing. Starting from an abstract workflow, concrete services are matched, based on their QoS, with the preferences and constraints of users. Current approaches usually only consider static QoS values and find a single solution consisting of one concrete service for each workflow task. In a business-to-business (B2B) environment, though, there are additional requirements for service compositions: 1) a high number of invocations, and 2) a high reliability. Thus, we introduce a probabilistic approach on the basis of a new QoS model to solve the composition problem for such long-term B2B service compositions. For each task and for every point in time, we determine the most appropriate services and backup services for a specific user. Thus, the selection depends on the actual response time and reliability, or recent invocation failures or timeouts. For that purpose, we propose an adaptive genetic algorithm that employs our QoS model and determines backup services dynamically based on the required reliability. Our evaluations show that our approach significantly increases the utility of long-term compositions compared with standard approaches in the envisioned B2B environments.

Service-Oriented Architecture enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Given a composition, finding the set of services that optimizes some QoS attributes under given QoS constraints has been shown to be NP-hard. Therefore, heuristic algorithms are widely used, finding acceptable solutions in polynomial time. Still the time complexity of such algorithms can be prohibitive for real-time use, especially if the algorithms are required to run until they find near-optimal solutions. Thus, we propose a heuristic approach based on Hill-Climbing that makes effective use of an initial bias computed with Linear Programming, and works on a reduced search space. In our evaluation, we show that our approach finds near-optimal solutions and achieves a low time complexity.

Service-Oriented Architecture enables the composition of loosely coupled services provided with varying Quality of Service (QoS) levels. Given a composition, finding the set of services that optimizes some QoS attributes under given QoS constraints has been shown to be NP-hard. Until now the problem has been considered only for a single execution, choosing a single service for each workflow element. This contrasts with reality where services often are executed hundreds and thousands of times. Therefore, we modify the problem to consider repeated executions of services in the long-term. We also allow to choose multiple services for the same workflow element according to a probabilistic selection policy. We model this modified problem with Linear Programming, allowing us to solve it optimally in polynomial time. We discuss and evaluate the different applications of our approach, show in which cases it yields the biggest utility gains, and compare it to the original problem.

Service selection is a central challenge in the context of a Service Oriented Architecture. Once functionally sufficient services have been selected, a further selection based on non-functional properties (NFPs) becomes essential in meeting the user's requirements and preferences. However, current descriptions of NFPs and approaches to NFP-aware selection lack the ability to handle the variability of NFPs, that stems from the complex nature of real-world business scenarios. Therefore, we propose a probabilistic approach to service selection as follows: First, to address the inherent variability in the actual values of NFPs at runtime, we treat them as probability distributions. Then, on top of that, we tackle the variability needed in describing NFPs, by providing conditional contracts. Finally, from usage patterns, we compute user-specific expectations for such NFPs. Further, we depict a typical scenario, which serves both as a motivation for our approach, and as a basis for its evaluation.

PROBLEM TO BE SOLVED: To provide a function of reasoning by taking into consideration information on a user's experience and behavior when it is not clear whether a user needs assistance or not and of assisting the user based thereon. SOLUTION: An intelligent user interface system monitors user interaction with a software application and applies probabilistic reasoning to sense that the user may need assistance in using a particular feature or to accomplish a specific task. The intelligent user interface also accepts a free-text query from the user asking for help and combines the inference analysis of user actions and program state with an inference analysis of the free-text query. The inference system accesses a rich, updatable user profile system to continually check for competencies and changes assistance that is given based on user competence. COPYRIGHT: (C)2009,JPO&INPIT