Users visit on-line services and compose them to accomplish on-line tasks, such as shopping on-line. Quite often, users enter the same information into various on-line services to finish on-line tasks. However, repetitively typing the same information into web forms is a tedious job for users. In this paper, we propose a context-aware ranking framework to rank values for input parameters. We propose 6 categories of ranking features constructed from various types of information, such as user contexts and patterns of user inputs. Our framework adopts learning-to-rank (LtR) algorithms that consist of a set of machine learned models to automatically construct ranking models by integrating the ranking features. When a user enters a value to an input parameter, an interaction between the user input and the input parameter is established. Our framework learns information relevant to such interactions and ranks user inputs in different contexts. Through empirical studies on the real-world on-line services, we obtain the following main results: (1) Among the 8 state-of-the-art learning-to-rank models, RankBoost can outperform other LtR models on ranking user inputs for input parameters; (2) Our framework using IRSVM that performs the worst among the LtR models outperforms the two baseline conventional ranking models and Google Chrome Autofilling, an industrial tool, on ranking user inputs to input parameters; and (3) We observe that the textual information of user inputs and input parameters is the most influential factor on ranking user inputs. Among the various types of contextual data, user locations and time matter the most to the ranking of user inputs.

With the rapid growth of the web services technologies, users often leverage various web services to perform their daily activities, such as on-line shopping. Due to the massive amount of web services available, a user faces numerous choices to meet their personal preferences when selecting the desired services from the web services with the similar functionality. Therefore, it becomes tedious and cumbersome tasks for users to discover and compose services. To reduce user's cognitive burden, it is critical to support automated service composition and make efficient recommendation of personalized services to achieve user's overall goals. However, existing approaches only offer users with limited options designed for the interest of a group of users without considering individual users' interests. To allow users to compose personalized services without much manual specification, we propose a machine learning approach that applies a learning-to-rank algorithm, RankBoost, to automatically learn user preferences and the prioritization of the preferences from users' historical data. Moreover, our approach uses the multi-objective reinforcement learning (MORL) algorithm to make trade-offs among user preferences and recommends a collection of services to achieve the highest objective. We conduct an empirical study to evaluate our approach by collecting the historical data from 12 subjects. The results demonstrate that our approach outperforms the two well-established baseline approaches by 100%-200% in terms of precision on recommending services.

End-users repetitively perform various on-line tasks and invoke multiple web services for their re-occurring activities, such as planning a trip. Usually, end-users have to complete different tasks in order to achieve a goal, and look through large volumes of services to find the best ones that satisfy their constraints, such as a budget limit. Current approaches on service composition require programming skills and domain knowledge to accomplish goals. Moreover, existing approaches lack an automatic way to analyze end-users' goals and extract relevant tasks for achieving goals. In this paper, we provide a lightweight service composition framework for end-users with limited technical background. Our framework analyzes endusers' goals expressed in natural languages to mine tasks (e.g., plan a trip) and non-functional constraints (e.g., budget <; 500). Our framework extracts task models from textual descriptions of tasks (e.g., eHow, a How-to instruction website) to guide the selection of services and recommend web services that can finish tasks and satisfy constraints. We have designed and developed a prototype as a proof of concept. We conduct case studies to evaluate the effectiveness of our framework. Our framework can identify tasks with a precision of 93% and a recall of 77%, and extract non-functional constraints with a precision of 89% and a recall of 76%. A user study shows that our framework is helpful for end-users to compose services.

Users visit web services and compose them to accomplish on-line tasks. Normally, users enter the same information into various web services to finish such tasks. However, repetitively typing the same information into services is unnecessary and decreases the service composition efficiency. In this paper, we propose a context-aware ranking approach to recommend previous user inputs into input parameters and save users from repetitive typing. We develop five different ranking features constructed from various types of information, such as user contexts. We adopt a learning-to-rank approach, a machine learning technology automatically constructing the ranking model, and integrate our ranking features into a state-of-the-art learning-to-rank framework. Our approach learns the information of interactions between input parameters and user inputs to reuse user inputs under different contexts. Through an empirical study on 960 real services, our approach outperforms two baseline approaches on ranking values to input parameters of composed services. Moreover, we observe that textual information affects the ranking most and the contextual information of location matters the most to ranking among various types of contextual data.

This paper coins the term "Tasking" and defines a "Tasking Conceptual Model" as a software engineering approach. In this approach, instead of producing Apps as a runtime artifact whole for its users, software developers produce intermediary artifacts, as accessible controls parts for average users. Using these controls, users are enabled to construct their own tasks autonomously by using resources of their own choice from across the cloud. I propose using web tasking over the cloud as a tasking approach to provide (1) average users consistent and universal tasking experience across the cloud despite of the resource diversity, and also, to provide (2) developers prescriptive, standard-ready software engineering steps to produce and contribute web-tasking resources. This proposed approach also provides (3) built-in interoperability. Users can use web-tasking resources produced by independent parties and expect that they can interoperate seamlessly together. I also propose a new cloud layer for users on top of the existing cloud layers for developers to create Apps. I coin this new cloud layer for end users "Task as a Service" (TaaS).

It is common for users to explicitly or implicitly compose on-line services to accomplish daily tasks, such as shopping for a pair of shoes on-line. However, unnecessary and repetitive data typing into the services would negatively impact the user experience and decrease the efficiency of service composition. Recent studies have proposed several approaches to help users fill in values to services automatically. However, existing approaches suffer the following two drawbacks: 1) poor accuracy of filling values to services, 2) not designed for service composition. In this position paper, we first present the recent approaches improving the process of filling values to services. We then present our recent achievements and results of using our proposed approaches to help users fill values to services. Lastly, we discuss the various opportunities and challenges in the research field of filling values to 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 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.

REpresentational State Transfer (REST) today represents and transfers the data-states of distrib-uted resources. Through hypermedia-based inter-actions among these representations and transfers, the web's original goal of information retrieval is accomplished. However, despite of the fact that the web today has evolved beyond information retrieval into task executions, the original web interaction model built for information browsing has not been enhanced accordingly. This paper proposes a hypermedia-based RESTful model for task expression, delegation and execution through the representations and transfers of action-states of distributed resources, termed as REpresentational Action State Transfer (REAST). The con-tributions of this paper are (1) a representation of hypermedia-based task expressions that enables the building of user-controlled interactive apps, (2) a technique for Resource Oriented Web Auto-mation (ROWA) using a dedicated media type designed for machine processing as well as ma-chine-initiated and machine-executed task expressions within the RESTful HATEOAS constraints, (3) REST-based Resource Oriented Intelligent Agents (ROIA) to act on users' behalf across the web without domain-specificity, and (4) an in-teroperability model for tasks execution involving diversified resources types (e.g., enterprise re-sources and internet of things) working seamlessly together within the RESTful architecture.

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.

The web was originated as a global information space. Over time, it has been evolved into a global service platform upon which users perform transactional tasks. While web browsing is good for information retrieval and search, web browsing might not be the most optimal user model for web tasks. Tasks have defined sequences of sub-tasks advancing towards specific goals that require user input. Web tasks involve operations on resources that produce side effects. Web browsing typically involves read-only operations on resources. This paper introduces the concept of web tasking, applying the RESTful web application model of "Hypermedia as the Engine of Application State" (HATEOAS) on web tasks. It also introduces the notion of web task automations; pre-set conditions for executions at future points in time. It also discusses machine-operate goal-directed application state transitions as a part of task automation. The ubiquitous nature of interactions and monitoring of web tasking is also covered.

End-users are confined by the social networking platforms that they invest effort in to set up their social contacts and relationships. The centralized, monopolized, and silo approach of today's social networking providers discourage interoperability across platforms. Enterprises struggle to find a common social networking approach to deal with the diversity of social networking platforms of choice of their users. Though each social networking platform provides their own APIs for externalizing users' social data, they are not designed to interoperate with other platforms. In this paper, we propose Social Ontology and Semantic Actions (SOSA) as a de-centralized, inter-operable social networking model and runtime services that enterprises can use as social networking middleware components for interoperability of social networking platforms of their users. SOSA provides platform agnostic construct for contacts; relationship axiom association with contacts, with inherent inferences to compute memberships given a relationship axiom. SOSA also enables binding with enterprise services as semantic actions of a given relationship axiom. Social relationship can also be used incontext with enterprise services and distributed web tasking.

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%.

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.

Given the large amount of existing services and the diversified needs nowadays, it is time-consuming for end-users to find appropriate services. To help end-users obtain their desired services, context-aware systems provide a promising way to automatically search and recommend services using a user's context. However, existing context-aware techniques have limited support for dynamic adaption to newly added context types (e.g., location, time and activity). Due to the diversity of user's environment, the available context types may change over time. It is challenging to anticipate a complete set of context types while we design a context aware system. In this paper, we propose a context modeling approach which can dynamically handle various context types and values. More specifically, we use ontologies to enhance the meaning of a user's context values and automatically identify the relations among different context values. Based on the relations among context values, we capture the potential services which the user might need. A case study is conducted to evaluate the effectiveness of our approach. The results show that our approach can use contexts to find users' needs and recommend their desired services with high precision and recall.