Web Services Description Language File Research Articles

Semantic Annotation Based Effective and Quality Oriented Web Service Discovery

The main objective of this work is to design an effective web service discovery engine that retrieves the best matching results based on the measure of syntactic cum semantic similarity between the user query and the web service to be fetched. The idea is to draw bridges across the broad spectrum of lexical entities based on their relativeness. The essence of this work could be extended to support a wide range of applications from ‘being inculcated in search engines to fetch user-relevant data’ to ‘being used for training robots and AI based devices to respond/adhere appropriately to the different phrases of human commands’.With the accelerated revolution of internet, enterprises and organizations highly rely on Service oriented computing. Web services support inter-operation of distributed applications. Humongous amount of web services present on the internet the user is searching for. The paper focuses ondesigninga practical means of fast and relevant retrieval of web services.The phrase used for searching any given web service differs from one person to another. This work deals with the syntactic cum semantic mechanism proposed for retrieving web services based on the measure of similarity between the name of the web service and the search query entered by the user to discover that service. A list of the Web Service Description Language (WSDL) files is taken as the dataset. Protégé is the tool used for semantic annotation of WSDL files for converting them to Semantically Annotated Web Service Description Language (SAWSDL) files. ‘Wordnet’ is used as the lexical dictionary and Java language has been used to build the rest of the package for implementing the search discovery mechanism.Netbeans is used as IDE.Wamp server with PHPMyAdmin was used for managing the database of SAWSDL files. The degree of similarity is measured by evaluating performance of the engine using metrics such as ‘Precision’, ‘Recall’, ‘Accuracy’ and ‘F-measure’.While the syntactic approach is easier to implement, it suffers from keyword polysemy issues. The proposed search discovery mechanism is based on semantically annotating WSDL files and retrieving the files based on a novel syntactic cum semantic discovery algorithm which usesLeocockChordorow function for computing the similarity. The effectiveness of the proposed algorithm is tested experimentally by building a desktop application using Java.The WSDL files from various domains were semantically annotated by tagging related concepts using ontology. Themachine learning algorithm that best classifies the web services on the basis of their performance metrics is identified. The related services are retrieved by the application of the proposed LCH based Syntactic cum Semantic discovery algorithm (LCH based SSDA).A ranking system is proposed to rank the results by evaluating various QoS attributes. The results of the experiment showed that the proposed system yielded high precision and recall value.The solution has been found to be effective in minimizing the execution time and in improving the degree of relevancy. With an impending need for constructing a semantic context based secure structure,the proposed solution will- help in refining the search results and in minimizing users’ cognitive load during search formulation and execution.

Detection of web service anti-patterns using weighted extreme learning machine

‘Anti-Pattern’ is a term often used by software engineers and practitioners nowadays. An anti-pattern is a supplement of the design pattern. Similar to design patterns, an anti-pattern is a template and a repeatable way of solving a specific problem, but in a non-optimal and ineffective way. Therefore, there is a requirement for the timely identification and modification of anti-patterns to increase software systems performance and efficiency. Anti-pattern detection using the source code metric can be used as an initial step in the software development life cycle, both to reduce the maintenance of the software system and to improve the quality of the software. The work in this paper empirically investigates the effectiveness of two machine learning algorithms, i.e., Extreme Learning Machine (ELM) and Weighted Extreme Learning Machine (WELM), with four different kernels in detecting web service anti-patterns. This work also investigates the application of different aggregation techniques and data sampling techniques to handle imbalanced data in predicting five different anti-patterns. The inference of this research is studied over 226 WSDL (Web Service Description Language) files. The experimental findings reveal that the model developed by WELM has superior prediction accuracy compared to the model developed by ELM.

Cross-Language Semantic Web Service Discovery to Improve the Selection Mechanism by using Data Mining Techniques

Web service became one of the important methods for communication through the internet and its usage increased in the levels of users and developers. Semantic web service represents the second generation of web services that contains more description and information about its contents. Searching and dealing with web service is done through process called web service discovery which returns a Semantic Web Service Description Language file (SWSDL) for each web service. This research aims to expand the semantic web service usage through adding the multilanguage capability to the web service’s discovery process and through recommending other web services to the user based on his history in using web services. These aims were achieved by modifying the web service discovery model through adding two important techniques the Cross Language Information Retrieval (CLIR) technique and the data mining association rules technique. This research proposed two sub models, the first sub model proposed the application of CLIR techniques and information retrieval method to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on SWSDL content and user’s query to be ready for CLIR methods, this sub model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue.org/ and achieving 99.38 % accuracy and 87.23 precision of the effectiveness of the monolingual system. The second sub model proposed a process of web service recommendation by applying the data mining techniques to suggest another web service beside the one he got from the discovery process based on the user’s history. This sub model was tested on the mention curated web services site and the results were 65 % of users chose services from the services that recommended by the proposed sub model.

The "GDSClient" Collecting Tool for Networked Solid Earth Science Data

The data center of our institute distributes solid earth science data obtained by the Ocean Hemisphere Project (OHP) network through the website of Pacific 21. We have developed Java-based software GDSClient, which enables us to collect not only the data of the OHP network but also those distributed from other data centers by means of the web service technology. It is possible to request the data controlling parameters such as data centers, observatories, a data period, and other auxiliary detailed parameters. It is unnecessary to know differences between data centers with preparing a WSDL (Web Services Description Language) file, in which information of user interface is described in XML format. The latest GDSClients are released from the website of Pacific 21.

BioModels.net Web Services, a free and integrated toolkit for computational modelling software

Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.

Web service clustering using text mining techniques

The idea of a decentralised, self-organising service-oriented architecture seems to be more and more plausible than the traditional registry-based ones in view of the success of the web and the reluctance in taking up web service technologies. Automatically clustering Web Service Description Language (WSDL) files on the web into functionally similar homogeneous service groups can be seen as a bootstrapping step for creating a service search engine and, at the same time, reducing the search space for service discovery. This paper proposes techniques to automatically gather, discover and integrate features related to a set of WSDL files and cluster them into naturally occurring service groups. Despite the lack of a common platform for assessing the performance of web service cluster discovery, our initial experiments using real-world WSDL files demonstrated the great potential of the proposed techniques.