Distributed Data Management with OGSA–DAI

Jackson, Michael; Antonioletti, Mario; Dobrzelecki, Bartosz; Hong, Neil Chue

doi:10.1007/978-3-642-20045-8_4

Cited by 14 publications

(15 citation statements)

References 17 publications

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“…WS-based access is a widely used technology in the data intensive scientific workflow community, and several systems for accessing large amounts of data already use this approach in their implementation. Some of these data workflow systems are presented in [18,12,19]. These systems have been successfully used in a variety of data intensive scenarios like analyzing data from the Southern California Earthquake Center [20], data from biological domains like post genomic research [21], analysis of proteins and peptides from tandem mass spectrometry data [22], cancer research [23], meteorological phenomena [24] or used in the German grid platform [25].…”

Section: Implementation Of Sparql-dqp and Welldesigned Patterns Optimmentioning

confidence: 99%

“…Some of the existing engines supporting the SPARQL 1.1 Federated Query extension are ARQ 12 , RDF-Query 13 , Rasqal RDF query Library 14 and ANAP-SID [10] among others. There are also other systems which implement a distributed query processing system for SPARQL like DARQ [4], Networked Graphs [5], SPLENDID [37], FedX 1.1 [6], the system by Ladwig et al [38] and SemWIQ [39], but they do not follow the official SPARQL 1.1 Federation specification.…”

Section: Other Federated Sparql Querying Processing Systemsmentioning

confidence: 99%

“…(which stands for SPARQL Distributed Query Processing), is built on top of the OGSA-DAI and OGSA-DQP infrastructures [12,13] that allow dealing with large amounts of data in distributed settings, supporting for example an indirect access mode that is normally used in the development of data-intensive workflows. In summary, the main contributions of this paper are:…”

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confidence: 99%

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Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Buil-Aranda

Arenas²,

Corcho

et al. 2013

Journal of Web Semantics

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Given the sustained growth that we are experiencing in the number of SPARQL endpoints available, the need to be able to send federated SPARQL queries across these has also grown. To address this use case, the W3C SPARQL working group is defining a federation extension for SPARQL 1.1 which allows for combining graph patterns that can be evaluated over several endpoints within a single query. In this paper, we describe the syntax of that extension and formalize its semantics. Additionally, we describe how a query evaluation system can be implemented for that federation extension, describing some static optimization techniques and reusing a query engine used for data-intensive science, so as to deal with large amounts of intermediate and final results. Finally we carry out a series of experiments that show that our optimizations speed up the federated query evaluation process. ing complex queries by combining data from different, sometimes heterogeneous and often physically distributed datasets. SPARQL endpoints are RESTful services that accept queries over HTTP written in the SPARQL query language [1,2] adhering to the SPARQL protocol [3], as defined by the respective W3C recommendation documents. However, the current SPARQL recommendation has an important limitation in terms of defining and executing queries that span across distributed datasets, since it hides the physical distribution of data across endpoints, and has normally been used for querying isolated endpoints. Hence users willing to federate queries across a number of SPARQL endpoints have been forced to create ad-hoc extensions of the query language and protocol, to include additional information about data sources in the configuration of their SPARQL endpoint servers [4,5,6] or to devise engineering solutions where data from remote endpoints is copied into the endpoint being queried. Given the need to address these types of queries, the SPARQL working group has proposed a query federation extension for the upcoming SPARQL 1.1 language [7] which is now under discussion in order to generate a new W3C recommendation in the coming months.

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Section: Implementation Of Sparql-dqp and Welldesigned Patterns Optimmentioning

confidence: 99%

Section: Other Federated Sparql Querying Processing Systemsmentioning

confidence: 99%

mentioning

confidence: 99%

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Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Buil-Aranda

Arenas²,

Corcho

et al. 2013

Journal of Web Semantics

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“…JDBC/ODBC drivers (Orfali and Harkey 1996)) to provide direct access to the underlying ISs, and (iii) wrappers designed to be used in an information grid infrastructure that may initially rely upon the OGSA-DAI (Jackson et al 2011) grid middleware. Wrappers (also called translators) (Papakonstantinou et al 1995) provide a common query language and common data model for extracting the information needed to bring data uniformly back into the grid.…”

Section: The Metamed Service-based Architecturementioning

confidence: 99%

Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment

et al. 2014

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Research and development activities relating to the grid have generally focused on applications where data is stored in files. However, many scientific and commercial applications are highly dependent on Information Servers (ISs) for storage and organization of their data. A datainformation system that supports operations on multiple informa-tion servers in a grid environment is referred to as an interoperable grid system. Different perceptions by end-users of interoperable systems in a grid environment may lead to dif-ferent reasons for integrating data. Even the same user might want to integrate the same distributed data in various ways to suit different needs, roles or tasks. Therefore multiple mediator views are needed to support this diversity. This paper describes our approach to supporting semantic interoperability in a heterogeneous multi-information server grid envi-ronment. It is based on using Integration Operators for generating multiple semantically rich RDF/OWL-based user defined mediator views above the grid participating ISs. These views support different perceptions of the distributed and heterogeneous data available. A set of grid services are developed for the implementation of the mediator views.

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“…In this paper we present the results of the project ADMIRE from the point of view of our environmental pilot application. We describe the methods ADMIRE uses to integrate geographically distributed data sets, stream them through a series of filters and processing elements using the OGSA-DAI platform [9], and deliver the results to the end users who have requested them. The project has successfully finished with a final review in July 2011, and the final platform allows for the easy development of complex DMI scenarios using an existing library of processing elements.…”

Section: Introductionmentioning

confidence: 99%

Using Advanced Data Mining and Integration in Environmental Prediction Scenarios

Habala

Hluchý

Tran

et al. 2012

csci

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We present one of the meteorological and hydrological experiments performed in the FP7 project ADMIRE. It serves as an experimental platform for hydrologists, and we have used it also as a testing platform for a suite of advanced data integration and data mining (DMI) tools, developed within ADMIRE. The idea of ADMIRE is to develop an advanced DMI platform accessible even to users who are not familiar with data mining techniques. To this end, we have designed a novel DMI architecture, supported by a set of software tools, managed by DMI process descriptions written in a specialized high-level DMI language called DISPEL, and controlled via several different user interfaces, each performing a different set of tasks and targeting different user group.

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Distributed Data Management with OGSA–DAI

Cited by 14 publications

References 17 publications

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Integration operators for generating RDF/OWL-based user defined mediator views in a grid environment

Using Advanced Data Mining and Integration in Environmental Prediction Scenarios

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