An investigation into the implementation issues and challenges of service oriented architecture

Several literatures have been published about the semantic web services being the solution to interoperability challenges within the Service Oriented Architecture (SOA) framework. The aim of this dissertation was to find out, if the introduction of the semantic layer into the SOA infrastructure will actually solve these challenges. In order to determine the existence of these challenges, a traditional web service built on XML technology was developed; first to understand the technology behind web services and secondly to demonstrate the limitations of the original SOA framework especially in the area of automatic service discovery and automatic service composition. To further investigate how the Semantic layer could solve these limitations; a semantic web service was developed, to explore the tools and models available to develop semantic web services and the possible challenges that could arise from the inclusion of the semantic layer into the SOA infrastructure. These two applications were evaluated and compared in terms of their capabilities and underlying technologies to find out if truly, the semantic web services could solve the interoperability challenges within the SOA infrastructure. Since semantic web services are built using ontologies, they have well described interfaces that allow for automatic web service discovery and invocation; it was found out that truly, they can solve the interoperability challenges in the SOA framework. However, there are a number of challenges that could impede the development of the Semantic SOA; such challenges were discussed in this paper. Finally, this paper concludes by highlighting areas in which the work in this research could be extended

RETRACTED: A Novel Approavh to Discover Web Services Using WSDL and UDDI

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy).This article has been retracted at the request of the scientific committee of International Conference on Computer, Communication and Convergence (ICCC 2015). The authors have plagiarized part of a paper that had already appeared in the International Journal of Information Technology and Computer Science(IJITCS), 6 (2014) 56–62, DOI: 10.5815/ijitcs.2014.10.08. (http://www.mecs-press.org/ijitcs/ijitcs-v6-n10/v6n10-8.html). One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents an abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the ICCC 2015 submission process

Semantic Support for Log Analysis of Safety-Critical Embedded Systems

Testing is a relevant activity for the development life-cycle of Safety Critical Embedded systems. In particular, much effort is spent for analysis and classification of test logs from SCADA subsystems, especially when failures occur. The human expertise is needful to understand the reasons of failures, for tracing back the errors, as well as to understand which requirements are affected by errors and which ones will be affected by eventual changes in the system design. Semantic techniques and full text search are used to support human experts for the analysis and classification of test logs, in order to speedup and improve the diagnosis phase. Moreover, retrieval of tests and requirements, which can be related to the current failure, is supported in order to allow the discovery of available alternatives and solutions for a better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic discovery, ontology, big dat

OBOME - Ontology based opinion mining in UBIPOL

Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated

Large Scale Data Analytics with Language Integrated Query

Databases can easily reach petabytes (1,048,576 gigabytes) in scale. A system to enable users to efficiently retrieve or query data from multiple databases simultaneously is needed. This research introduces a new, cloud-based query framework, designed and built using Language Integrated Query, to query existing data sources without the need to integrate or restructure existing databases. Protein data obtained through the query framework proves its feasibility and cost effectiveness

WorkflowHunt : um mecanismo de busca híbrida para repositórios de workflows científicos

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os experimentos científicos e os conjuntos de dados gerados a partir deles estão crescendo em tamanho e complexidade. Os cientistas estão enfrentando dificuldades para compartilhar esses recursos e permitir a reprodutibilidade do experimento. Algumas iniciativas surgiram para tentar resolver esse problema. Uma delas envolve o uso de workflows científicos para representar a execução de experimentos científicos. Existe um número crescente de workflows que são potencialmente relevantes para mais de um domínio científico. Criar um workflow leva tempo e recursos e sua reutilização ajuda aos cientistas a criar novos workflows de forma mais rápida e confiável. No entanto, é difícil encontrar workflows adequados para reutilização. Geralmente, os repositórios de workflows possuem mecanismos de busca com muitas limitações, o que afeta negativamente a descoberta de workflows relevantes para um cientista ou seu time. Esta dissertação apresenta WorkflowHunt, uma arquitetura híbrida para busca e descoberta de workflows em repositórios genéricos, combinando busca baseada em palavras-chave e busca semântica para encontrar workflows relevantes usando diferentes métodos de busca. Ao contrário da maioria das pesquisas correlatas, nossa proposta e sua implementação são genéricas. Nosso sistema de indexação e anotação é automático e independe de domínio ou ontologia específica. A arquitetura foi validada por meio de um protótipo que usa workflows e metadados reais do myExperiment, um dos maiores repositórios de workflows científicos. Nosso sistema também compara seus resultados com o mecanismo de busca do myExperiment para analisar em que casos um sistema supera o outroAbstract: Scientific experiments and the datasets generated from them are growing in size and complexity. Scientists are facing difficulties to share those resources in a way that allows reproducibility of the experiment. Some initiatives have emerged to try to solve this problem. One of them involves the use of scientific workflows to represent and enact the execution of scientific experiments. There is an increasing number of workflows that are potentially relevant for more than one scientific domain. Creating a workflow takes time and resources, and their reuse helps scientists to build new workflows faster and in a more reliable way. However, it is hard to find workflows suitable for reuse for an experiment. Usually, workflow repositories have search mechanisms with many limitations, which affects negatively the discovery of relevant workflows. This dissertation presents WorkflowHunt, a hybrid architecture for workflow search and discovery for generic repositories, which combines keyword and semantic search to find relevant workflows using different search methods. Unlike most related work, our proposal and its implementation are generic. Our indexing and annotation mechanism are automatic and not restricted to a specific domain or ontology. We validated our architecture creating a prototype that uses real workflows and metadata from myExperiment, one of the largest online scientific workflow repositories. Our system also compares its results with myExperiment¿s search engine to analyze in which cases one retrieval system outperforms the otherMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

ARIANA: Adaptive Robust and Integrative Analysis for finding Novel Associations

The effective mining of biological literature can provide a range of services such as hypothesis-generation, semantic-sensitive information retrieval, and knowledge discovery, which can be important to understand the confluence of different diseases, genes, and risk factors. Furthermore, integration of different tools at specific levels could be valuable. The main focus of the dissertation is developing and integrating tools in finding network of semantically related entities. The key contribution is the design and implementation of an Adaptive Robust and Integrative Analysis for finding Novel Associations. ARIANA is a software architecture and a web-based system for efficient and scalable knowledge discovery. It integrates semantic-sensitive analysis of text-data through ontology-mapping with database search technology to ensure the required specificity. ARIANA was prototyped using the Medical Subject Headings ontology and PubMed database and has demonstrated great success as a dynamic-data-driven system. ARIANA has five main components: (i) Data Stratification, (ii) Ontology-Mapping, (iii) Parameter Optimized Latent Semantic Analysis, (iv) Relevance Model and (v) Interface and Visualization. The other contribution is integration of ARIANA with Online Mendelian Inheritance in Man database, and Medical Subject Headings ontology to provide gene-disease associations. Empirical studies produced some exciting knowledge discovery instances. Among them was the connection between the hexamethonium and pulmonary inflammation and fibrosis. In 2001, a research study at John Hopkins used the drug hexamethonium on a healthy volunteer that ended in a tragic death due to pulmonary inflammation and fibrosis. This accident might have been prevented if the researcher knew of published case report. Since the original case report in 1955, there has not been any publications regarding that association. ARIANA extracted this knowledge even though its database contains publications from 1960 to 2012. Out of 2,545 concepts, ARIANA ranked “Scleroderma, Systemic”, “Neoplasms, Fibrous Tissue”, “Pneumonia”, “Fibroma”, and “Pulmonary Fibrosis” as the 13th, 16th, 38th, 174th and 257th ranked concept respectively. The researcher had access to such knowledge this drug would likely not have been used on healthy subjects.In today\u27s world where data and knowledge are moving away from each other, semantic-sensitive tools such as ARIANA can bridge that gap and advance dissemination of knowledge