Web service composition enables seamless and dynamic integration of Web services. The behavior of participant Web services determines the overall performance of a composition. Therefore, it is important to choose high quality services for service composition. Existing Web service selection and discovery approaches rely on non-functional aspects (also known as quality of service or QoS), e.g., response time and availability. Though these parameters are crucial for selecting Web services, they may not reflect the user's perspective of quality. In this paper, we explore the feasibility of incorporating perceived quality from user's perspective for service selection and composition. We name such quality attributes as quality of experience (QoE). First, we propose a solution that automatically mines and identifies QoE attributes from the Web. Second, we study the application of such dynamically extracted QoE attributes for service selection. For the evaluation purpose, we collected more than 34,000 reviews from 58 different services in six domains. Our findings show that it is possible to automatically identify QoE attributes with an average precision and recall of 92 and 80 percent respectively. Our study shows that there is a strong positive correlation between QoS and QoE. Hence QoE can be used during service selection especially when QoS data are not available. Furthermore, we found 70 percent of service discovery queries indeed contain QoE attributes showing the importance of QoE attributes during the service discovery phase.

End-users conduct various on-line activities. Quite often, they re-visit websites and use services to perform re-occurring activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such re-occurring tasks. It can negatively impact user experience when a user needs to type the re-occurring information repetitively into such web services. In this paper, we propose an approach to prevent end-users from performing such repetitive tasks. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach pre-fills values to the input parameters for an end-user using his or her previous inputs. To increase the chance of identifying a proper value for an input parameter performed by one end-user, our approach also leverages the inputs from other end-users. We identify and link similar end-users to enable the propagation of user inputs among end-users. We have designed and developed a prototype. We also conduct an empirical study to evaluate our approach using the real world services. The empirical results show that our approach using an end-user's previous inputs can reduce on average 41 percent of repetitive typing for the execution of composed services. Furthermore, the previous inputs from the similar end-users can improve our approach in reducing the repetitive typing for an end-user.

Web service composition enables seamless and dynamic integration of web services. The behavior of participant web services determines the overall performance of a composition. Therefore, it is important to choose the high quality participants for service composition. The state of the art in service discovery and selection rely on non-functional aspects also known as quality of service (QoS) e.g., response time and availability. Though these parameters are crucial for selecting web services, they do not reflect the end-user's perspective on quality. In this paper, we explore the feasibility of adopting the perceived quality from end-user's perspective for service selection and composition. We name such quality parameters as quality of experience (QoE). First, we propose a solution that automatically mines and identifies QoE parameters from the web. Second, we study the application of such dynamically extracted QoE attributes for service selection. For the evaluation purpose, we collected more than 24,000 reviews from 22 different services from four domains. Our result shows the automated approach identifies QoE attributes with an average precision and recall 90% and 79% respectively. Our study shows that there is a strong positive correlation between QoS and QoE. Hence QoE can be used during service selection especially when QoS data are not available.

End-users conduct various on-line activities. Quite often they re-visit websites and use services to perform repeated activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such repeated tasks. Typing redundant information repetitively into such services negatively impacts the user experience. In this study, we propose an approach to prevent end-users from such unnecessary interruption. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach also pre-fills values to the input parameters which could not be filled by the values from other input or output parameters. We propose a meta-data model for storing user inputs and an Input Parameter Context Model for identifying similar input or output parameters. We have implemented our approach and evaluated it on the real world services through an empirical study. Our overall approach can reduce on average 37% of input parameters through the execution of composed services.

Web service composition enables seamless and dynamic integration of applications on the web. Generally a user has to find services, select proper services and form a flow to create a service composition. The performance of the composed application is determined by the performance of the involved Web services. Current work in web service selection and discovery are based on non-functional, quality of service (QoS) aspects (such as, response time and availability). However, QoS information does not reflect an end user's perspective on the quality of services. An end user's perspective of a service is a credible source of information, covers diverse platforms and geographical locations. In this position paper, we provide an approach to extract a user's perception of the quality of services from user reviews on services and use such information to compose services. We provide a mechanism to select a particular service from a pool of services and recommend the best service execution path in a composite service.

Nowadays, people use on-line services to conduct various tasks such as on-line shopping and holiday trip planning using web applications. Generally users are required to enter information into web forms to interact with the web applications. However they often have to type in the same information to different web applications repetitively. It could be a tedious job for a user to fill in a large amount of web forms with the same information. To save users from typing redundant information, it is critical to propagate and pre-fill the user's previous inputs across different web applications. However, existing software and approaches cannot meet this urgent need. In this position paper, we propose an intelligent framework to propagate user's inputs across different web applications. Our framework collects user's inputs and analyzes the patterns of user's usage. Furthermore it detects the changes of user's contexts by extracting user's contextual information from various sources such as a user's calender. Our framework clusters the user interface (UI) components to form semantic groups of similar UI components based on our proposed clustering approach. Knowing the similarity relation between UI components, the framework can pre-fill the web forms with user's previous inputs. We conduct a preliminary study on effectiveness of our proposed clustering approach. We achieved a precision of 80% and a recall of 87%.

ABSTRACT A service‐oriented application is composed of multiple Web services to fulfill complex functionality that cannot be provided by individual Web service. The combination of services is not random. In many cases, a set of services are repetitively used together in various applications. We treat such a set of services as a service composition pattern. The quality of the patterns is desirable because of the extensive uses and testing in the large number of applications. Therefore, the service composition patterns record the best practices in designing and developing reliable service‐oriented applications. The execution log tracks the execution of services in a service‐oriented application. To document the service composition patterns, we propose an approach that automatically identifies service composition patterns from various applications using execution logs. We locate a set of associated services using Apriori algorithm and recover the control flows among the services by analyzing the order of service invocation events in the execution logs. We also identify structurally and functionally similar patterns to represent such patterns in a higher level of abstraction regardless of the actual services. A case study shows that our approach can effectively detect service composition patterns. Copyright © 2012 John Wiley & Sons, Ltd.

Process knowledge, such as tasks involved in a process and the control flow and data flow among tasks, is critical for designing business processes. Such process knowledge enables service composition which integrates different services to implement business processes. In the current state of practice, business processes are primarily designed by experienced business analysts who have extensive process knowledge. It is challenging for novice business analysts and non-professional end-users to identify a complete set of services to orchestrate a well-defined business process due to the lack of process knowledge. In this paper, we propose an approach to extract process knowledge from existing commercial applications on the Web. Our approach uses a Web search engine to find websites containing process knowledge on the Internet. By analyzing the content and the structure of relevant websites, we extract the process knowledge from various websites and merge the process knowledge to generate an integrated ontology with rich process knowledge. We conduct a case study to compare our approach with a tool that extracts ontologies from textual sources. The result of the case study shows that our approach can extract process knowledge from online applications with higher precision and recall comparing to the ontology learning tool.