�ݺ�ߣshows by User: KemeleMuhammed

�ݺ�ߣshows by User: KemeleMuhammed / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: KemeleMuhammed / Mon, 03 Sep 2018 10:45:55 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: KemeleMuhammed BOUNCER: A Privacy-aware Query Processing Over Federations of RDF Datasets /slideshow/bouncer-a-privacyaware-query-processing-over-federations-of-rdf-datasets/112777443 bouncer-dexa2018-slides-180903104555
Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for the citizens. However, effective data-centric applications demand data management techniques able to process a large volume of data which may include sensitive data, e.g., financial transactions, medical procedures, or personal data. Managing sensitive data requires the enforcement of privacy and access control regulations, particularly, during the execution of queries against datasets that include sensitive and non-sensitive data. In this paper, we tackle the problem of enforcing privacy regulations during query processing, and propose BOUNCER, a privacy-aware query engine over federations of RDF datasets. BOUNCER allows for the description of RDF datasets in terms of RDF molecule templates, i.e., abstract descriptions of the properties of the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that allow for accessing these relevant entities. We empirically evaluate the effectiveness of the BOUNCER privacy-aware techniques over state-of-the-art benchmarks of RDF datasets. The observed results suggest that BOUNCER can effectively enforce access control regulations at different granularity without impacting the performance of query processing.]]>
Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for the citizens. However, effective data-centric applications demand data management techniques able to process a large volume of data which may include sensitive data, e.g., financial transactions, medical procedures, or personal data. Managing sensitive data requires the enforcement of privacy and access control regulations, particularly, during the execution of queries against datasets that include sensitive and non-sensitive data. In this paper, we tackle the problem of enforcing privacy regulations during query processing, and propose BOUNCER, a privacy-aware query engine over federations of RDF datasets. BOUNCER allows for the description of RDF datasets in terms of RDF molecule templates, i.e., abstract descriptions of the properties of the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that allow for accessing these relevant entities. We empirically evaluate the effectiveness of the BOUNCER privacy-aware techniques over state-of-the-art benchmarks of RDF datasets. The observed results suggest that BOUNCER can effectively enforce access control regulations at different granularity without impacting the performance of query processing.]]> Mon, 03 Sep 2018 10:45:55 GMT /slideshow/bouncer-a-privacyaware-query-processing-over-federations-of-rdf-datasets/112777443 KemeleMuhammed@slideshare.net(KemeleMuhammed) BOUNCER: A Privacy-aware Query Processing Over Federations of RDF Datasets KemeleMuhammed Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for the citizens. However, effective data-centric applications demand data management techniques able to process a large volume of data which may include sensitive data, e.g., financial transactions, medical procedures, or personal data. Managing sensitive data requires the enforcement of privacy and access control regulations, particularly, during the execution of queries against datasets that include sensitive and non-sensitive data. In this paper, we tackle the problem of enforcing privacy regulations during query processing, and propose BOUNCER, a privacy-aware query engine over federations of RDF datasets. BOUNCER allows for the description of RDF datasets in terms of RDF molecule templates, i.e., abstract descriptions of the properties of the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that allow for accessing these relevant entities. We empirically evaluate the effectiveness of the BOUNCER privacy-aware techniques over state-of-the-art benchmarks of RDF datasets. The observed results suggest that BOUNCER can effectively enforce access control regulations at different granularity without impacting the performance of query processing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bouncer-dexa2018-slides-180903104555-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for the citizens. However, effective data-centric applications demand data management techniques able to process a large volume of data which may include sensitive data, e.g., financial transactions, medical procedures, or personal data. Managing sensitive data requires the enforcement of privacy and access control regulations, particularly, during the execution of queries against datasets that include sensitive and non-sensitive data. In this paper, we tackle the problem of enforcing privacy regulations during query processing, and propose BOUNCER, a privacy-aware query engine over federations of RDF datasets. BOUNCER allows for the description of RDF datasets in terms of RDF molecule templates, i.e., abstract descriptions of the properties of the entities in an RDF dataset and their privacy regulations. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over RDF datasets that not only contain the relevant entities to answer a query, but that are also regulated by policies that allow for accessing these relevant entities. We empirically evaluate the effectiveness of the BOUNCER privacy-aware techniques over state-of-the-art benchmarks of RDF datasets. The observed results suggest that BOUNCER can effectively enforce access control regulations at different granularity without impacting the performance of query processing.

BOUNCER: A Privacy-aware Query Processing Over Federations of RDF Datasets from Kemele M. Endris

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0 Dataset reuse: An analysis of references in community discussions, publications and data /KemeleMuhammed/dataset-reuse-an-analysis-of-references-in-community-discussions-publications-and-data k-cap2017-short-datasetreuse-presentation-171205193820
Following the Linked Data principles means maximizing the reusability of data over the Web. Reuse of datasets can become apparent when datasets are linked to from other datasets, and referred in scientific articles or community discussions. It can thus be measured, similarly to citations of papers. In this paper we propose dataset reuse metrics and use these metrics to analyze indications of dataset reuse in different communication channels within a scientific community. In particular we consider mailing lists and publications in the Semantic Web community and their correlation with data interlinking. Our results demonstrate that indications of dataset reuse across different communication channels and reuse in terms of data interlinking are positively correlated.]]>
Following the Linked Data principles means maximizing the reusability of data over the Web. Reuse of datasets can become apparent when datasets are linked to from other datasets, and referred in scientific articles or community discussions. It can thus be measured, similarly to citations of papers. In this paper we propose dataset reuse metrics and use these metrics to analyze indications of dataset reuse in different communication channels within a scientific community. In particular we consider mailing lists and publications in the Semantic Web community and their correlation with data interlinking. Our results demonstrate that indications of dataset reuse across different communication channels and reuse in terms of data interlinking are positively correlated.]]> Tue, 05 Dec 2017 19:38:19 GMT /KemeleMuhammed/dataset-reuse-an-analysis-of-references-in-community-discussions-publications-and-data KemeleMuhammed@slideshare.net(KemeleMuhammed) Dataset reuse: An analysis of references in community discussions, publications and data KemeleMuhammed Following the Linked Data principles means maximizing the reusability of data over the Web. Reuse of datasets can become apparent when datasets are linked to from other datasets, and referred in scientific articles or community discussions. It can thus be measured, similarly to citations of papers. In this paper we propose dataset reuse metrics and use these metrics to analyze indications of dataset reuse in different communication channels within a scientific community. In particular we consider mailing lists and publications in the Semantic Web community and their correlation with data interlinking. Our results demonstrate that indications of dataset reuse across different communication channels and reuse in terms of data interlinking are positively correlated. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/k-cap2017-short-datasetreuse-presentation-171205193820-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Following the Linked Data principles means maximizing the reusability of data over the Web. Reuse of datasets can become apparent when datasets are linked to from other datasets, and referred in scientific articles or community discussions. It can thus be measured, similarly to citations of papers. In this paper we propose dataset reuse metrics and use these metrics to analyze indications of dataset reuse in different communication channels within a scientific community. In particular we consider mailing lists and publications in the Semantic Web community and their correlation with data interlinking. Our results demonstrate that indications of dataset reuse across different communication channels and reuse in terms of data interlinking are positively correlated.

Dataset reuse: An analysis of references in community discussions, publications and data from Kemele M. Endris

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0 MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates /slideshow/querying-the-linked-data-web-by-bridging-rdf-molecule-templates/79214022 mulder-dexa2017-full-170828120849
The increasing number of RDF data sources that allow for querying Linked Data via Web services form the basis for federated SPARQL query processing. Federated SPARQL query engines provide a unified view of a federation of RDF data sources, and rely on source descriptions for selecting the data sources over which unified queries will be executed. Albeit efficient, existing federated SPARQL query engines usually ignore the meaning of data accessible from a data source, and describe sources only in terms of the vocabularies utilized in the data source. Lack of source description may conduce to the erroneous selection of data sources for a query, thus affecting the performance of query processing over the federation. We tackle the problem of federated SPARQL query processing and devise MULDER, a query engine for federations of RDF data sources. MULDER describes data sources in terms of RDF molecule templates, i.e., abstract descriptions of entities belonging to the same RDF class. Moreover, MULDER utilizes RDF molecule templates for source selection, and query decomposition and optimization. We empirically study the performance of MULDER on existing benchmarks, and compare MULDER performance with state-of-the-art federated SPARQL query engines. Experimental results suggest that RDF molecule templates empower MULDER federated query processing, and allow for the selection of RDF data sources that not only reduce execution time, but also increase answer completeness.]]>
The increasing number of RDF data sources that allow for querying Linked Data via Web services form the basis for federated SPARQL query processing. Federated SPARQL query engines provide a unified view of a federation of RDF data sources, and rely on source descriptions for selecting the data sources over which unified queries will be executed. Albeit efficient, existing federated SPARQL query engines usually ignore the meaning of data accessible from a data source, and describe sources only in terms of the vocabularies utilized in the data source. Lack of source description may conduce to the erroneous selection of data sources for a query, thus affecting the performance of query processing over the federation. We tackle the problem of federated SPARQL query processing and devise MULDER, a query engine for federations of RDF data sources. MULDER describes data sources in terms of RDF molecule templates, i.e., abstract descriptions of entities belonging to the same RDF class. Moreover, MULDER utilizes RDF molecule templates for source selection, and query decomposition and optimization. We empirically study the performance of MULDER on existing benchmarks, and compare MULDER performance with state-of-the-art federated SPARQL query engines. Experimental results suggest that RDF molecule templates empower MULDER federated query processing, and allow for the selection of RDF data sources that not only reduce execution time, but also increase answer completeness.]]> Mon, 28 Aug 2017 12:08:49 GMT /slideshow/querying-the-linked-data-web-by-bridging-rdf-molecule-templates/79214022 KemeleMuhammed@slideshare.net(KemeleMuhammed) MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates KemeleMuhammed The increasing number of RDF data sources that allow for querying Linked Data via Web services form the basis for federated SPARQL query processing. Federated SPARQL query engines provide a unified view of a federation of RDF data sources, and rely on source descriptions for selecting the data sources over which unified queries will be executed. Albeit efficient, existing federated SPARQL query engines usually ignore the meaning of data accessible from a data source, and describe sources only in terms of the vocabularies utilized in the data source. Lack of source description may conduce to the erroneous selection of data sources for a query, thus affecting the performance of query processing over the federation. We tackle the problem of federated SPARQL query processing and devise MULDER, a query engine for federations of RDF data sources. MULDER describes data sources in terms of RDF molecule templates, i.e., abstract descriptions of entities belonging to the same RDF class. Moreover, MULDER utilizes RDF molecule templates for source selection, and query decomposition and optimization. We empirically study the performance of MULDER on existing benchmarks, and compare MULDER performance with state-of-the-art federated SPARQL query engines. Experimental results suggest that RDF molecule templates empower MULDER federated query processing, and allow for the selection of RDF data sources that not only reduce execution time, but also increase answer completeness. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mulder-dexa2017-full-170828120849-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> The increasing number of RDF data sources that allow for querying Linked Data via Web services form the basis for federated SPARQL query processing. Federated SPARQL query engines provide a unified view of a federation of RDF data sources, and rely on source descriptions for selecting the data sources over which unified queries will be executed. Albeit efficient, existing federated SPARQL query engines usually ignore the meaning of data accessible from a data source, and describe sources only in terms of the vocabularies utilized in the data source. Lack of source description may conduce to the erroneous selection of data sources for a query, thus affecting the performance of query processing over the federation. We tackle the problem of federated SPARQL query processing and devise MULDER, a query engine for federations of RDF data sources. MULDER describes data sources in terms of RDF molecule templates, i.e., abstract descriptions of entities belonging to the same RDF class. Moreover, MULDER utilizes RDF molecule templates for source selection, and query decomposition and optimization. We empirically study the performance of MULDER on existing benchmarks, and compare MULDER performance with state-of-the-art federated SPARQL query engines. Experimental results suggest that RDF molecule templates empower MULDER federated query processing, and allow for the selection of RDF data sources that not only reduce execution time, but also increase answer completeness.

MULDER: Querying the Linked Data Web by Bridging RDF Molecule Templates from Kemele M. Endris

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png http://eis.iai.uni-bonn.de/Kemele_M_Endris https://cdn.slidesharecdn.com/ss_thumbnails/bouncer-dexa2018-slides-180903104555-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/bouncer-a-privacyaware-query-processing-over-federations-of-rdf-datasets/112777443 BOUNCER: A Privacy-awa... https://cdn.slidesharecdn.com/ss_thumbnails/k-cap2017-short-datasetreuse-presentation-171205193820-thumbnail.jpg?width=320&height=320&fit=bounds KemeleMuhammed/dataset-reuse-an-analysis-of-references-in-community-discussions-publications-and-data Dataset reuse: An anal... https://cdn.slidesharecdn.com/ss_thumbnails/mulder-dexa2017-full-170828120849-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/querying-the-linked-data-web-by-bridging-rdf-molecule-templates/79214022 MULDER: Querying the L...