Adoption Of Web Services Research Articles

Service similarity measurement integrating Bi-LSTM contextual representation and attention mechanism for web service discovery

With the rapid growth and wide adoption of web services, service discovery is becoming commonly used to locate the optimal services that can meet the requirements of users. This paper proposes a bidirectional long short-term memory (Bi-LSTM) service discovery method that integrates contextual representation and attention mechanisms for web service discovery. To conduct the study, FastText word embedding with the N-gram feature is primarily used as the input of the Bi-LSTM model to obtain the context feature vector of the service function description. Moreover, the attention mechanism is employed to calculate the similarity of the service function description to gain the semantic feature vector of the correlation degree between services. Then, the multilayer perceptron network is used to mine the mapping relationship between semantic feature vectors and matching levels among services. Finally, for a given query, target services are retrieved by ranking candidate services according to the prediction cores through the matching method. The proposed method is examined against multiple evaluation metrics, including accuracy and error, and compared with the state-of-the-art searching approaches. The experimental results show that the proposed method is more effective and attains better relevant web services, which helps the request service find more accurate results. Furthermore, this study provides new insights for researchers into web service discovery.

Web service discovery approach: application in e-healing domain

The main objective of today's companies is to cope with rapid changes in the environment. For this, they must ensure the integration and interoperability of their applications. To manage and automate the life cycle of these applications, companies are adopting web services technology. The current problem is that the content of these web services cannot be processed automatically. Only humans can interpret its contents. The semantic web is a new vision of the web that promises to overcome this difficulty. The goal of this technology is to automate the retrieval, assembly, and selection of web services. In this post, we are interested in semantic detection of web services. The main problem is automatically discovering web services on request from clients. Against this background, we first describe the principle of the proposed detection mechanism and then present the designed matchmaking algorithm. Finally, we implement our proposed method. To verify our work, we run tests against various user requests and web service panels. As part of a case study, we consider an online hospital problem. This problem is a typical web service discovery scenario to which the concepts of our method are applied.

Semantically Rich Framework to Automate Cyber Insurance Services

With the rapid enhancements in technology and the adoption of web services, there has been a significant increase in cyber threats faced by organizations in cyberspace. It has become essential to get financial cover to mitigate the expenses due to a security incident. Organizations want to purchase adequate cyber insurance to safeguard against the third-party services they use. However, cyber insurance policies describe their coverages and exclusions using legal jargon that can be difficult to comprehend. Parsing these policy documents and extracting the rules embedded in them is currently a very manual time-consuming process. We have developed a novel framework that automatically extracts the coverage and exclusion key terms and rules embedded in a cyber policy. We have built our framework using Information Retrieval and Artificial Intelligence techniques, specifically Semantic Web and Modal Logic. We have also developed a web interface where users can find the best matching cyber insurance policy based on particular coverage criteria. To validate our approach, we used industry standards proposed by the Federal Trade Commission document (FTC) and have applied it against publicly available policies of seven insurance providers. Our system will allow cyber insurance seekers to explore various policy documents and compare the paradigms mentioned in those documents while selecting the best relevant policy documents.

Web Service Composition Security

The continuous adoption of service-oriented architecture (SOA) and web services across vertical industries, geographies, and organization sizes generates new major security concerns both for individuals and organizations. Many of the features that make web services attractive, including greater accessibility of data, dynamic application-to-application connections, and relative autonomy are at odds with traditional security models and controls. Current web service composition technologies still present serious risks to individual and enterprise security and privacy. In the presence of multiple providers that coordinate to provide a composite web service, these issues are amplified. Web services security, privacy, and forensics investigations are therefore becoming a serious concern, especially with the tremendous increase in cybernetic crimes. The aim of this work is to draw a comprehensive overview about security aspects related to web services composition by considering different branches related to security, privacy, and forensics investigations. For each security aspect, the problem statement and related works are discussed. Then, a proposed approach to deal with the aspect under consideration is presented.

A Reinforcement Learning Method for Constraint-Satisfied Services Composition

With increasing adoption and presence of Web services, service composition becomes an effective way to construct software applications. Composite services need to satisfy both the functional and the non-functional requirements. Traditional methods usually assume that the quality of service (QoS) and the behaviors of services are deterministic, and they execute the composite service after all the component services are selected. It is difficult to guarantee the satisfaction of user constraints and the successful execution of the composite service. This paper models the constraint-satisfied service composition (CSSC) problem as a Markov decision process (MDP), namely CSSC-MDP, and designs a Q-learning algorithm to solve the model. CSSC-MDP takes the uncertainty of QoS and service behavior into account, and selects a component service after the execution of previous services. Thus, CSSC-MDP can select the globally optimal service based on the constraints which need the following services to satisfy. In the case of selected service failure, CSSC-MDP can timely provide the optimal alternative service. Simulation experiments show that the proposed method can successfully solve the CSSC problem of different sizes. Comparing with three representative methods, CSSC-MDP has obvious advantages, especially in terms of the success rate of service composition.

Blogs: are they headed downwards in social networking An empirical analysis

Many businesses today utilise blogs to expand opportunities to communicate, collaborate, and network. However, some believe it may be on a downward path as users today may prefer micro-blogging with Twitter and Facebook. Since the adoption of web services is typically bottom-up, it is important to understand perceptions toward workplace blogs, especially among the younger generation. A study of over four hundred users finds statistically significant evidence for reduced user perceptions of blogging over the years. The study also identifies four business areas that users find important for blogs, including employee networking and online sales. This research is one of the few recent works that show a relationship between importance and performance, previously assumed to be independent. For blogs, the relationship is found to be continuously increasing. The importance-performance analysis framework can now be used as a tool for decision-making by businesses as well providers of the blogging technology.

Blogs: are they headed downwards in social networking An empirical analysis

Many businesses today utilise blogs to expand opportunities to communicate, collaborate, and network. However, some believe it may be on a downward path as users today may prefer micro-blogging with Twitter and Facebook. Since the adoption of web services is typically bottom-up, it is important to understand perceptions toward workplace blogs, especially among the younger generation. A study of over four hundred users finds statistically significant evidence for reduced user perceptions of blogging over the years. The study also identifies four business areas that users find important for blogs, including employee networking and online sales. This research is one of the few recent works that show a relationship between importance and performance, previously assumed to be independent. For blogs, the relationship is found to be continuously increasing. The importance-performance analysis framework can now be used as a tool for decision-making by businesses as well providers of the blogging technology.

An efficient image processing method based on web services for mobile devices

The traditional image processing system which is based on a centralized computing model has a disadvantage of limiting resources. This can cause troubles in the smooth execution of system in mobile devices. We propose a new system which unlike traditional system uses a distributed model. It can be achieved by adopting web service based image processing system. Our system has the advantages of being component oriented and has low coupling and high encapsulation. Thus our system can solve the main problem of traditional image processing system and avoid the resource bottleneck situation.

Online document editing and collaboration web services: factors influencing adoption

Today, online document editing and collaboration (ODEC) web services, such as Google Drive and Zoho, are commonly used to achieve workplace collaboration. They provide unique business advantages at the tactical as well as strategic levels, including harnessing group collaboration, collective intelligence, and improved business processes. The adoption rate has also quickly increased in the last few years. As web services are believed to be a bottom-up phenomenon, this paper empirically examines the perceptions of the new generation. An exploratory factor analysis reveals four factors that drive adoption of document-level collaboration web services: business improvement, day-to-day collaboration, business innovation, and personal use. Business benefits such as collaboration, cost reduction, and collective intelligence find strong recognition. This research is important for academicians as there are few studies that examine what motivates the modern workforce to adopt online document editing and collaboration. A better understanding of the end user perceptions can help organisations shape their initiatives in this regard and improve successful adoption. Technology providers can focus their attention on improving performance on aspects that are important for the business users.

The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services

This work aims to advance Web Service retrieval, also known as Matching, in two directions. First, it introduces a matching algorithm for SAWSDL, which adapts and extends known concepts with novel strategies. Effective logic-based and syntactic strategies are introduced and combined in a novel hybrid strategy, targeting an envisioned well-defined, real-world scenario for matching. The algorithm is evaluated in a universal environment for matching algorithms, SME2, in an objective, reproducible manner. Evaluation ranks Tomaco high amongst state of the art, especially for early recall levels (first in macro-averaging precision, up to 0.7 recall). Secondly, this work introduces the Tomaco web application, which aims to promote wide-spread adoption of Semantic Web Services while targeting the lack of user-friendly applications in this field, by integrating a variety of configurable matching algorithms proposed in this paper. It, finally, allows discovery of both existing and user-contributed service collections and ontologies, serving also as a service registry.

Diversifying web service recommendation results via exploring service usage history

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With the increasing adoption and presence of web services, design of novel approaches for effective web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing web service recommendation approaches mainly focus on predicting missing QoS values of web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant web services may exist in a recommendation list. In this paper, we propose a novel web service recommendation approach incorporating a user's potential QoS preferences and diversity feature of user interests on web services. User's interests and QoS preferences on web services are first mined by exploring the web service usage history. Then we compute scores of web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a web service graph based on the functional similarity between web services. Finally, we present an innovative diversity-aware web service ranking algorithm to rank the web service candidates based on their scores, and diversity degrees derived from the web service graph. Extensive experiments are conducted based on a real world web service dataset, indicating that our proposed web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.

An Effective Web Service Ranking Method via Exploring User Behavior

Service-oriented computing and Web services are becoming more and more popular, enabling organizations to use the Web as a market for selling their own Web services and consuming existing Web services from others. Nevertheless, with the increasing adoption and presence of Web services, it becomes more difficult to find the most appropriate Web service that satisfies both users’ functional and nonfunctional requirements. In this paper, we propose an effective Web service ranking approach based on collaborative filtering (CF) by exploring the user behavior, in which the invocation and query history are used to infer the potential user behavior. CF-based user similarity is calculated through similar invocations and similar queries (including functional query and QoS query) between users. Three aspects of Web services—functional relevance, CF based score, and QoS utility, are all considered for the final Web service ranking. To avoid the impact of different units, range, and distribution of variables, three ranks are calculated for the three factors respectively. The final Web service ranking is obtained by using a rank aggregation method based on rank positions. We also propose effective evaluation metrics to evaluate our approach. Large-scale experiments are conducted based on a real world Web service dataset. Experimental results show that the proposed approach outperforms the existing approach on the rank performance.

Unified Collaborative and Content-Based Web Service Recommendation

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users’ interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collaborative filtering</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> content-based recommendation</i> . Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.

Harnessing the Environmental Data Flood: A Comparative Analysis of Hydrologic, Oceanographic, and Meteorological Informatics Platforms

Abstract Researchers studying large-scale questions in hydrology, oceanography, and meteorology can work with existing data through myriad platforms that provide access to remote datasets and render said information in various graphical outputs for interpretation and analysis. A survey of 30 publicly available hydrometeorological data platforms reviews the current state of the art in water-data discovery and visualization. Such platforms best meet the needs of a diverse user community by providing valuable data content, facilitating data exchange, and supporting visual analysis. To provide datasets of value to wider audiences, data providers can emphasize building datasets that are not only voluminous but also proportionally content rich in geographic, temporal, and measurement breadth, through integration of complementary datasets and coordinated data collection programs. In support of efficient data interchange, including software-driven integration of complementary content, data providers should increase adoption of web services to share data, along with machine-readable formats. In addition, this work surveys best practices in visualization and advocates for graphical user interface features that provide the flexibility required to integrate heterogeneous datasets by both novice and expert end users. Features that stand out as particularly useful include comprehensive content filters for customizable data queries, multiparameter plotting, statistical analysis tools, and predictive visualization. Effective combination of comprehensive—as well as voluminous—content, widely implemented standards for efficient data interchange, and appropriate visualization flexibility in the next generation of tools provided as software services will empower a wide user community, spanning laypersons, students, and researchers.

Technological, Organizational, and Environmental Antecedents to Web Services Adoption

The proliferation of web services within the last two years enables organizations to assimilate software and services from different companies and locations into an integrated service capable of streamlining important processes. Widespread adoption of web services has not yet occurred across all industries. To better understand the key determinants of web services adoption at the firm level, a conceptual model of factors impacting web services adoption was developed. The conceptual model was grounded in the technology-organization-environment (TOE) framework (Tomatzky and Fleischer, 1990) to support the formulation of eleven propositions that may affect adoption and continued utilization of web services. Specifically, factors for each of the contexts within the TOE framework were formulated and supported including: (1) technological factors (security concerns; reliability; deployability); (2) organizational factors (firm size; firm scope; technological knowledge; perceived benefits); and, (3) environmental factors (competitive pressure; regulatory influence; dependent partner readiness; trust in the web service provider). A summary of the relationships between the key constructs in the model and recommendations for future research are provided.

Selecting Dynamic Skyline Services for QoS-based Service Composition

With the growing adoption of web services on the Internet, service selection becomes an important issue of service-oriented computing (SOC). Appropriate services selection algorithm is the fundamental guarantee to compose complex services from single- function components effectively. The quality of selected component services is crucial for the performance of the service composition. Therefore, it has become a hot issue to select the best services from a set of services with similar functionality. Recently, skyline has been introduced to solve the problem by selecting skyline services as the best candidate services. In this paper, we focus on selecting skyline services in dynamic environment, where new services may appear, original services may invalidate and QoS of services may change. We propose a skyline service model as well as a novel skyline algorithm to maintain dynamic skyline services. An extensive performance study is propoesed to verify the effectiveness and effi ciency of our approach.

The Impact of Standards in Web Services Security

The Internet has provided an avenue for businesses to adopt web services. Web services promises scalability, agility, cost reduction, profitability and availability thereby meeting instant gratification needs of web users'. It refers to modular Internet-based business functions that perform specific business tasks to facilitate business interactions within and beyond the organization. While addressing web services benefits there have been discussions on the importance of its security. The security of web services has deeply influenced its development and is also one of the main reasons why web services have not been adopted widely. Therefore, this study aims to examine the impact of web services standards and how it facilitates web services security. We analyze security models in web based technologies from previous studies such as; in cloud computing and security as a service technologies to develop a framework of web services standards and its impact on web services security. We then discuss the theoretical and practical implications of web services security and directions for future research.

Automatic dynamic generation of likely invariants for WS-BPEL compositions

The wide adoption of Web Services has led to the development of languages to compose them, like the WS-BPEL standard. In order to check whether the composition works as expected, one common approach is to analyze it and infer functional properties describing its behavior. Traditional approaches for inferring properties in WS-BPEL have been static: compositions are transformed into specialized analysis models based on some formalization. However, this formalization could be inexact due to theoretical limitations or differing interpretations of the standard by implementers. Dynamic invariant generation solves these problems by extracting the properties from actual executions and has been successfully used in popular languages, but not to WS-BPEL yet. In this work, we apply dynamic invariant generation to WS-BPEL, providing innovative solutions for several features that require special consideration, like highly multidimensional values in variables, an advanced type system or unstructured code. We have implemented these solutions in Takuan and evaluated its performance with several compositions of varying complexity. We present the results obtained and a comparative analysis of the efficiency and effectiveness of our solutions. Results show that the solutions are successful in reducing the cost of applying dynamic invariant generation and the number of uninteresting invariants generated.

The QoS Prediction of Web Service with Location Information Ensemble

With the increasing presence and adoption of web services, accurate QoS prediction methods are becoming increasingly important. Although some QoS prediction techniques have been proposed and analyzed recently, the performance is not satisfactory, since they didn't take the relation between QoS values and users' physical locations into consideration. In order to improve the precision of web service QoS prediction, we propose a probabilistic matrix factor model that fuses the users' own properties and their physical neighbors' performance together, so as to make a comprehensive use of both the users' QoS records and their location relation. The experimental results show that our method performs better than the state-of-the-art approaches.

A distributed aggregation and fast fractal clustering approach for SOAP traffic

The adoption of Web Services has increased tremendously by many network organizations. Although Web services as XML-based network applications provide significant advantages over other communication technologies in terms of interoperability and dynamic scalability, they suffer from congestion and bottlenecks as a result of the large demand and large XML messages. Aggregation of SOAP messages is one of the potential solutions to reduce network traffic by aggregating large numbers of messages (responses), minimizing their volume over the Internet. However, existing aggregation models only support this function at one node (Web server) and therefore they do not allow aggregation of SOAP messages at different servers as one collaborative technique. In this paper, a new distributed aggregation model is proposed to support aggregation of messages from several nodes that share the path of SOAP responses over the Internet. Furthermore, a fast fractal similarity-based clustering technique is proposed speeding up the computation of similar messages that can be aggregated together in order to achieve a higher reduction. The proposed models show significant results, with distributed aggregation outperforming regular aggregation, resulting in a 100% higher compression ratio. The fast fractal clustering has reduced the required processing time by 85% when compared to the classical fractal clustering technique.