MAGALHÃES, Regis Pires. Um ambiente para processamento de consultas federadas em linked data Mashups. 2012. 117 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2012. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-12T16:08:12Z No. of bitstreams: 1 2012_dis_rpmagalhaes.pdf: 2883929 bytes, checksum: 1a04484a7e875cd8ead588d91693577a (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-21T16:05:44Z (GMT) No. of bitstreams: 1 2012_dis_rpmagalhaes.pdf: 2883929 bytes, checksum: 1a04484a7e875cd8ead588d91693577a (MD5) / Made available in DSpace on 2016-07-21T16:05:44Z (GMT). No. of bitstreams: 1 2012_dis_rpmagalhaes.pdf: 2883929 bytes, checksum: 1a04484a7e875cd8ead588d91693577a (MD5) Previous issue date: 2012 / Semantic Web technologies like RDF model, URIs and SPARQL query language, can reduce the complexity of data integration by making use of properly established and described links between sources.However, the difficulty to formulate distributed queries has been a challenge to harness the potential of these technologies due to autonomy, distribution and vocabulary of heterogeneous data sources. This scenario demands effective mechanisms for integrating data on Linked Data.Linked Data Mashups allow users to query and integrate structured and linked data on the web. This work proposes two architectures of Linked Data Mashups: one based on the use of mediators and the other based on the use of Linked Data Mashup Services (LIDMS). A module for efficient execution of federated query plans on Linked Data has been developed and is a component common to both proposed architectures.The execution module feasibility has been demonstrated through experiments. Furthermore, a LIDMS execution Web environment also has been defined and implemented as contributions of this work. / Tecnologias da Web Semântica como modelo RDF, URIs e linguagem de consulta SPARQL, podem reduzir a complexidade de integração de dados ao fazer uso de ligações corretamente estabelecidas e descritas entre fontes.No entanto, a dificuldade para formulação de consultas distribuídas tem sido um obstáculo para aproveitar o potencial dessas tecnologias em virtude da autonomia, distribuição e vocabulário heterogêneo das fontes de dados.Esse cenário demanda mecanismos eficientes para integração de dados sobre Linked Data.Linked Data Mashups permitem aos usuários executar consultas e integrar dados estruturados e vinculados na web.O presente trabalho propõe duas arquiteturas de Linked Data Mashups:uma delas baseada no uso de mediadores e a outra baseada no uso de Linked Data Mashup Services (LIDMS). Um módulo para execução eficiente de planos de consulta federados sobre Linked Data foi desenvolvido e é um componente comum a ambas as arquiteturas propostas.A viabilidade do módulo de execução foi demonstrada através de experimentos. Além disso, um ambiente Web para execução de LIDMS também foi definido e implementado como contribuições deste trabalho.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Motivado pelo sucesso de Linked Data e impulsionado pelo crescimento do nÃmero de fontes de dados em formato RDF disponÃveis na Web, novos desafios para processamento de consultas estÃo emergindo, especialmente em configuraÃÃes distribuÃdas. No ambiente de Linked Data, à possÃvel executar consultas federadas, as quais envolvem junÃÃes de dados fornecidos por mÃltiplas fontes. O termo consulta federada à usado quando queremos prover soluÃÃes baseadas em informaÃÃes obtidas de diferentes fontes. Nesse sentido, a concepÃÃo de novos algoritmos e estratÃgias adaptativas para a execuÃÃo de junÃÃes de forma eficiente constitui um desafio importante. Nesse trabalho, apresentamos uma soluÃÃo para a execuÃÃo adaptativa de operaÃÃes de junÃÃes de dados em consultas federadas. A execuÃÃo da operaÃÃo de junÃÃo adaptativa entre informaÃÃes contidas em fontes de dados distribuÃdas baseia-se em estatÃsticas, que sÃo coletadas em tempo de execuÃÃo. Uma informaÃÃo estatÃstica sobre uma determinada fontes seria, por exemplo, o tempo decorrido (Elapsed Time) para obter algum resultado. Para obter as informaÃÃes estatÃsticas atualizadas, usamos uma estratÃgia que coleta essas informaÃÃes durante a execuÃÃo da consulta e,logo apÃs, sÃo armazenadas em uma base de dados local, na qual denominamos como catÃlogo de informaÃÃes estatÃsticas. / Motivated by the success of Linked Data and driven by the growing number of data sources into RDF files available on the web, new challenges for query processing are emerging, especially in distributed settings. These environments allow distributed execution of federated queries, which involve joining data provided by multiple sources, which are often unstable. In this sense, the design of new algorithms and adaptive strategies for efficiently implementing joins is a major challenge. In this paper, we present a solution to the adaptive joins execution in federated queries. The adaptative context of distributed data sources is based on statistics that are collected at runtime. For this, we use a module that updates the information in the catalog as the query is executed. The module works in parallel with the query processor.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Tecnologias da Web SemÃntica como modelo RDF, URIs e linguagem de consulta SPARQL, podem reduzir a complexidade de integraÃÃo de dados ao fazer uso de ligaÃÃes corretamente estabelecidas e descritas entre fontes.No entanto, a dificuldade para formulaÃÃo de consultas distribuÃdas tem sido um obstÃculo para aproveitar o potencial dessas tecnologias em virtude da autonomia, distribuiÃÃo e vocabulÃrio heterogÃneo das fontes de dados.Esse cenÃrio demanda mecanismos eficientes para integraÃÃo de dados sobre Linked Data.Linked Data Mashups permitem aos usuÃrios executar consultas e integrar dados estruturados e vinculados na web.O presente trabalho propÃe duas arquiteturas de Linked Data Mashups:uma delas baseada no uso de mediadores e a outra baseada no uso de Linked Data Mashup Services (LIDMS). Um mÃdulo para execuÃÃo eficiente de planos de consulta federados sobre Linked Data foi desenvolvido e à um componente comum a ambas as arquiteturas propostas.A viabilidade do mÃdulo de execuÃÃo foi demonstrada atravÃs de experimentos. AlÃm disso, um ambiente Web para execuÃÃo de LIDMS tambÃm foi definido e implementado como contribuiÃÃes deste trabalho. / Semantic Web technologies like RDF model, URIs and SPARQL query language, can reduce the complexity of data integration by making use of properly established and described links between sources.However, the difficulty to formulate distributed queries has been a challenge to harness the potential of these technologies due to autonomy, distribution and vocabulary of heterogeneous data sources. This scenario demands effective mechanisms for integrating data on Linked Data.Linked Data Mashups allow users to query and integrate structured and linked data on the web. This work proposes two architectures of Linked Data Mashups: one based on the use of mediators and the other based on the use of Linked Data Mashup Services (LIDMS). A module for efficient execution of federated query plans on Linked Data has been developed and is a component common to both proposed architectures.The execution module feasibility has been demonstrated through experiments. Furthermore, a LIDMS execution Web environment also has been defined and implemented as contributions of this work.

Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.

Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.