Bipin Upadhyaya

Migration of SOAP-based services to RESTful services

Web services are designed to provide rich functionality for organizations and support interoperable interactions over a network. Web services are mainly realized in two ways: 1) SOAP-based services and 2) RESTful services. For the service providers, RESTful services can improve system flexibility, scalability, and performance as compared to the SOAP-based Web services. It is equally attractive to end users as it is consume less resources (i.e., battery, processor speed, and memory). Additionally, REST-based services do not include complex standards and heterogeneous operations; and hence are easier to consume and compose as compared to SOAP-based Web services. We provide an approach to migrate SOAP-based services to RESTful services. We identify resources from a SOAP-based Web service by analyzing its service description and mapping the contained operations to resources and HTTP methods. To demonstrate the effectiveness of our approach, we conduct a case study on a set of publicly available SOAP-based Web services. The results of our case study show that our approach can achieve high accuracy of identifying RESTful services from the interfaces of SOAP-based services. Our approach can improve the performance for invoking Web services after SOAP-based services are migrated to RESTful services.

An approach for mining service composition patterns from execution logs

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

Extracting RESTful services from Web applications

The Web contains large amount of information and services primarily intended for human users. A Web application offers high user experience and responsiveness. A user performs different task, such as reserving flight tickets from a Web application. A task is a set of activities required for a user to achieve a goal. Similar tasks are often used in different websites. Therefore, facilitating their reuse would improve development productivity and ease maintenance of Web applications. However, designing a reusable Web application component is often neglected by Web developers due to the pressure for the time-to-market. To circumvent this limitation, we propose an approach to interactively identify tasks from Web applications and represent these tasks as services.

Quality of Experience: User's Perception about Web Services

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 users perspective of quality. In this paper, we explore the feasibility of incorporating perceived quality from users 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.

An Automatic Approach for Extracting Process Knowledge from the Web

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.

Automatically Composing Services by Mining Process Knowledge from the Web

Current approaches in Service-Oriented Architecture SOA are challenging for users to get involved in the service composition due to the in-depth knowledge required for SOA standards and techniques. To shield users from the complexity of SOA standards, we automatically generate composed services for end-users using process knowledge available in the Web. Our approach uses natural language processing techniques to extract tasks. Our approach automatically identifies services required to accomplish the tasks. We represent the extracted tasks in a task model to find the services and then generate a user interface UI for a user to perform the tasks. Our case study shows that our approach can extract the tasks from how-to instructions Web pages with high precision i.e., 90%. The generated task model helps to discover services and compose the found services to perform a task. Our case study shows that our approach can reach more than 90% accuracy in service composition by identifying accurate data flow relation between services.

Quality of Experience: What End-Users Say about Web Services?

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-users perspective on quality. In this paper, we explore the feasibility of adopting the perceived quality from end-users 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.

A concept analysis approach for guiding users in service discovery

Web services are widely used as basic constructs to build complex distributed applications with fast speed and low cost. However, existing service discovery techniques provide users with poor results which require substantial human intervention to filter the services to locate the desired ones. In particular, users often have no prior knowledge of the functional description of the available services on the Web. The queries formulated by the users may not match well with the service descriptions of existing services. As a consequence, a users query can result in a large number of returned services. In this paper, we propose an approach that derives the semantic concepts conveyed in the service descriptions and clusters the services based on the concepts. As a result, each concept is associated with a set of relevant services. To understand the semantic meanings of a users query, we identify concepts behind the query and recommend related concepts associated with services. Our approach also guides users to formulate their queries. We conducted a case study and found that the average precision and recall of our approach for service discovery are respectively, 83% and 100%. We also performed a user study which shows that for 85% of time, a user reformulates their queries using the suggestion provided by our approach to improve the precision of the retrieved services.

Automatic Propagation of User Inputs in Service Composition for End-Users

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.

An Empirical Study on Categorizing User Input Parameters for User Inputs Reuse

End-users often have to enter the same information to various services (e.g., websites and mobile applications) repetitively. To save end-users from typing redundant information, it becomes more convenient for an end-user if the previous inputs of the end-user can be pre-filled to applications based on end-user’s contexts. The existing pre-filling approaches have poor accuracy of pre-filling information, and only provide limited support of reusing user inputs within one application and propagating the inputs across different applications. The existing approaches do not distinguish parameters, however different user input parameters can have very varied natures. Some parameters should be pre-filled and some should not. In this paper, we propose an ontology model to express the common parameters and the relations among them and an approach using the ontology model to address the shortcomings of the existing pre-filling techniques. The basis of our approach is to categorize the input parameters based on their characteristics. We propose categories for user inputs parameters to explore the types of parameters suitable for pre-filling. Our empirical study shows that the proposed categories successfully cover all the parameters in a representative corpus. The proposed approach achieves an average precision of 75% and an average recall of 45% on the category identification for parameters. Compared with a baseline approach, our approach can improve the existing pre-filling approach, i.e., 19% improvement on precision on average.