The Experts below are selected from a list of 186 Experts worldwide ranked by ideXlab platform

Ratnesh Sahay - One of the best experts on this subject based on the ideXlab platform.

  • SAFE: SPARQL Federation over RDF Data Cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholz-schuhmann, Ratnesh Sahay
    Abstract:

    Background Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc . Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. Results We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. Conclusions We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe sparql federation over rdf data cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholzschuhmann, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe policy aware sparql query federation over rdf data cubes
    SWAT4LS, 2014
    Co-Authors: Yasar Khan, Muhammad Saleem, Aftab Iqbal, Muntazir Mehdi, Aidan Hogan, Axelcyrille Ngonga Ngomo, Stefan Decker, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In this paper, we present SAFE, a SPARQL query federation engine that enables decentralised, policy-aware Access to clinical information represented as RDF data cubes. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy Linked Datasets. In our evaluation, we show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the query execution time when compared with general-purpose SPARQL query federation engine.

Yasar Khan - One of the best experts on this subject based on the ideXlab platform.

  • SAFE: SPARQL Federation over RDF Data Cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholz-schuhmann, Ratnesh Sahay
    Abstract:

    Background Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc . Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. Results We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. Conclusions We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe sparql federation over rdf data cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholzschuhmann, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe policy aware sparql query federation over rdf data cubes
    SWAT4LS, 2014
    Co-Authors: Yasar Khan, Muhammad Saleem, Aftab Iqbal, Muntazir Mehdi, Aidan Hogan, Axelcyrille Ngonga Ngomo, Stefan Decker, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In this paper, we present SAFE, a SPARQL query federation engine that enables decentralised, policy-aware Access to clinical information represented as RDF data cubes. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy Linked Datasets. In our evaluation, we show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the query execution time when compared with general-purpose SPARQL query federation engine.

Muhammad Saleem - One of the best experts on this subject based on the ideXlab platform.

  • SAFE: SPARQL Federation over RDF Data Cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholz-schuhmann, Ratnesh Sahay
    Abstract:

    Background Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc . Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. Results We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. Conclusions We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe sparql federation over rdf data cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholzschuhmann, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe policy aware sparql query federation over rdf data cubes
    SWAT4LS, 2014
    Co-Authors: Yasar Khan, Muhammad Saleem, Aftab Iqbal, Muntazir Mehdi, Aidan Hogan, Axelcyrille Ngonga Ngomo, Stefan Decker, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In this paper, we present SAFE, a SPARQL query federation engine that enables decentralised, policy-aware Access to clinical information represented as RDF data cubes. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy Linked Datasets. In our evaluation, we show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the query execution time when compared with general-purpose SPARQL query federation engine.

Muntazir Mehdi - One of the best experts on this subject based on the ideXlab platform.

  • SAFE: SPARQL Federation over RDF Data Cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholz-schuhmann, Ratnesh Sahay
    Abstract:

    Background Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc . Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. Results We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. Conclusions We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe sparql federation over rdf data cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholzschuhmann, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe policy aware sparql query federation over rdf data cubes
    SWAT4LS, 2014
    Co-Authors: Yasar Khan, Muhammad Saleem, Aftab Iqbal, Muntazir Mehdi, Aidan Hogan, Axelcyrille Ngonga Ngomo, Stefan Decker, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In this paper, we present SAFE, a SPARQL query federation engine that enables decentralised, policy-aware Access to clinical information represented as RDF data cubes. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy Linked Datasets. In our evaluation, we show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the query execution time when compared with general-purpose SPARQL query federation engine.

Aidan Hogan - One of the best experts on this subject based on the ideXlab platform.

  • SAFE: SPARQL Federation over RDF Data Cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholz-schuhmann, Ratnesh Sahay
    Abstract:

    Background Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc . Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. Results We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. Conclusions We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe sparql federation over rdf data cubes with Access Control
    Journal of Biomedical Semantics, 2017
    Co-Authors: Yasar Khan, Muhammad Saleem, Muntazir Mehdi, Aidan Hogan, Qaiser Mehmood, Dietrich Rebholzschuhmann, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through Access Control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data. We present SAFE: a query federation engine that enables policy-aware Access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where Access Control is coupled with source selection, user profiles and their Access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter Access Control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

  • safe policy aware sparql query federation over rdf data cubes
    SWAT4LS, 2014
    Co-Authors: Yasar Khan, Muhammad Saleem, Aftab Iqbal, Muntazir Mehdi, Aidan Hogan, Axelcyrille Ngonga Ngomo, Stefan Decker, Ratnesh Sahay
    Abstract:

    Several query federation engines have been proposed for Accessing public Linked Open Data sources. However, in many domains, resources are sensitive and Access to these resources is tightly Controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In this paper, we present SAFE, a SPARQL query federation engine that enables decentralised, policy-aware Access to clinical information represented as RDF data cubes. We validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy Linked Datasets. In our evaluation, we show that SAFE enables granular graph-Level Access Control over distributed clinical RDF data cubes and efficiently reduces the query execution time when compared with general-purpose SPARQL query federation engine.