Microsoft has promoted ASP.NET’s new web services more than almost any other part of the.NET Framework. But despite their efforts, confusion is still widespread about what a web service is and, more importantly, what it’s meant to accomplish. This chapter introduces web services and explains their role in Microsoft’s vision of the programmable web. Along the way, you’ll learn about the open standards plumbing that allows web services to work and removes some of the confusion surrounding technical terms like WSDL (Web Service Description Language), SOAP, and UDDI (universal description, discovery, and integration).

Web Services Architecture

事情(IoT ) 的因特网针对启用物理世界和电子空间的互联和集成。它代表未来联网的趋势，并且带信息产业革命的第三个波浪。在这篇文章，我们首先介绍某背景和 IoT 的相关技术并且讨论 IoT 的概念和目的。然后，我们在场挑战和关键科学问题在 IoT 包含了开发。而且，我们介绍中国(973 程序) 的国家基本研究程序支持的当前的研究工程。最后，我们构画出未来研究方向。

Internet of Things： Objectives and Scientific Challenges

Web services are becoming the prominent paradigm for distributed computing and electronic business. This has raised the opportunity for service providers and application developers to develop value-added services by combining existing web services. However, current web service composition solutions do not address software engineering principles for raising the level of abstraction in web-services by providing facilities for packaging, re-using, specializing and customizing service compositions.In this paper we propose the concept of service component that packages together complex services and presents their interfaces and operations in a consistent and uniform manner in the form of an abstract class definition, Service components are internally synthesized out of reused, specialized, or extended complex web services and just like normal web services are published and can thus be invoked by any service-based application. In addition, we present an integrated framework and prototype system that manage the entire life-cycle of service components ranging from abstract service component definition, scheduling, and construction to execution.

Service components for managing the life-cycle of service compositions

Personalization of Web Services: Opportunities and Challenges

Web service discovery based on goal-oriented query expansion

With a growing number of web services, discovering services that can match with a user's query becomes a challenging task. It's very tedious for a service consumer to select the appropriate one according to her/his needs. In this paper, we propose a non-logic-based matchmaking approach that uses the Correlated Topic Model (CTM) to extract topic from semantic service descriptions and model the correlation between the extracted topics. Based on the topic correlation, service descriptions can be grouped into hierarchical clusters. In our approach, we use the Formal Concept Analysis (FCA) formalism to organize the constructed hierarchical clusters into concept lattices according to their topics. Thus, service discovery may be achieved more easily using the concept lattice. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. In our experiment, we compared the accuracy of the our hierarchical clustering algorithm with that of a classical hierarchical agglomerative clustering. The comparisons of [email protected] and Normalised Discounted Cumulative Gain (NDCGn) values for our approach, Apache lucene and SAWSDL-MX2 Matchmaker indicate that the method based on CTM presented in this paper outperform all the others matchmakers in terms of ranking of the most relevant services.

Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery

In Information Retrieval the Probabilistic Topic Models were originally developed and utilized for topic extraction and document modeling. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. These extracted latent factors are then used to group the services into clusters. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering web services using latent factors. In our experiment, we compared the accuracy of the three probabilistic clustering algorithms (PLSA, LDA and CTM) with that of a classical clustering algorithm. We evaluated also our service discovery approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn). The results show that both approaches based on CTM and LDA perform better than other search methods.

Probabilistic Topic Models for Web Services Clustering and Discovery

With the increasing number of published Web services
providing similar functionalities, it’s very tedious for a
service consumer to make decision to select the appropriate one
according to her/his needs. In this paper, we explore several
probabilistic topic models: Probabilistic Latent Semantic Analysis
(PLSA), Latent Dirichlet Allocation (LDA) and Correlated
Topic Model (CTM) to extract latent factors from web service
descriptions. In our approach, topic models are used as efficient
dimension reduction techniques, which are able to capture semantic
relationships between word-topic and topic-service interpreted
in terms of probability distributions. To address the limitation of
keywords-based queries, we represent web service description as
a vector space and we introduce a new approach for discovering
and ranking web services using latent factors. In our experiment,
we evaluated our Service Discovery and Ranking approach
by calculating the precision (P@n) and normalized discounted
cumulative gain (NDCGn).

/pdf/correlated-topic-model-for-web-services-ranking-5bxhthtir0.pdf

Correlated Topic Model for Web Services Ranking

Due to the increase of published Web Services (WSs), finding the suitable WS that satisfies the user goals among discovered WSs still needs deep investigations Certainly, QoS requirements represent a more appropriate and decisive factor to distinguish similar WSs A lot of research efforts in this direction have been made but are still limited due to the complexity and diversity of QoS constraints The novelty of our approach lies in its simplicity since it is based on WS Popularity Score (WSPS) This score is computed using an algorithm based on both user's requirements and quality measures of each discovered WSs such as pertinence, age, frequency, etc The paper reports a validation of the proposed algorithm, its implementation and evaluation trough Information Extraction (IE), in order to illustrate, and assess the convenience of our approach

Ranking Web Services using Web Service Popularity Score

This paper shows that the problem of web services representation is crucial and analyzes the various factors that influence on it. It presents the traditional representation of web services considering traditional textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services descriptions are dirty and need significant cleaning to keep only useful information. To deal with this problem, we introduce rules based text tagging method, which allows filtering web service description to keep only significant information. A new representation based on such filtered data is then introduced. Many web services have empty descriptions. Also, we consider web services representations based on the WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called symbolic reputation, which is computed from relationships between web services. The impact of the use of these representations on web service discovery and recommendation is studied and discussed in the experimentation using real world web services.

Zahi Jarir

Papers

Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery

Probabilistic Topic Models for Web Services Clustering and Discovery

Correlated Topic Model for Web Services Ranking

Ranking Web Services using Web Service Popularity Score

Web Services Discovery and Recommendation Based on Information Extraction and Symbolic Reputation