Boca: an open-source RDF store for building Semantic Web applications

Feigenbaum, Lee; Martin, Sean; Roy, Matthew N.; Szekely, Benjamin; Yung, Wing C.

doi:10.1093/bib/bbm017

Cited by 6 publications

(7 citation statements)

References 2 publications

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“…Articles focusing on OSS applications that help researchers organize and share digital information for research purposes such as: Mooney and Baenziger (2008), Stanford andMikula (2008), Alvord et al (2007), Feigenbaum et al (2007), Greco (2007), , Spudich et al (2007), Xi et al (2007), Dera (2006), Field et al (2006), Pestian et al (2006), Smith et al (2006), Srinivas (2006), Stajich and Lapp (2006), Isken (2005), Skiba (2005), Yoo and Ackerman (2005), Bleasby (2004), David (2004), Fitzgerald and Kenny (2004), Overbeek et al (2004), Whitfield (2003), Matthiesen (2002), Malakoff (2001), Stewart et al (2001).…”

Section: Academic and Commercial Researchmentioning

confidence: 99%

A Comprehensive Review and Synthesis of Open Source Research

Aksulu¹,

Wade²

2010

JAIS

131

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The open source movement has grown steadily and matured in recent years, and this growth has been mirrored by a rise in open source related research. The objective of this paper is to pause and reflect on the state of the field. We start by conducting a comprehensive literature review of open source research, and organize the resulting 618 peer-reviewed articles into a taxonomy. Elements of this taxonomy are defined and described. We then draw on a number of existing categorization schemes to develop a framework to situate open source research within a wider nomological network. Building on concepts from systems theory, we propose a holistic framework of open source research. This framework incorporates current research, as represented by the taxonomy, identifies gaps and areas of overlap, and charts a path for future work.

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Section: Academic and Commercial Researchmentioning

confidence: 99%

A Comprehensive Review and Synthesis of Open Source Research

Aksulu¹,

Wade²

2010

JAIS

131

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“…The Workbench allows scientists and scientific editors to work with the ontology in a very friendly, AJAX-style, Web application. Instances created in the course of using the Workbench are written to a triple store -currently IBM's BOCA triple store [22]. The SWAN Workbench is designed to organize Hypotheses, Claims, and Evidence within a discourse; and to highlight relationships between them.…”

Section: Applying the Ontology In Practicementioning

confidence: 99%

The SWAN biomedical discourse ontology

Ciccarese

Wong

et al. 2008

Journal of Biomedical Informatics

113

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Developing cures for highly complex diseases, such as neurodegenerative disorders, requires extensive interdisciplinary collaboration and exchange of biomedical information in context. Our ability to exchange such information across sub-specialties today is limited by the current scientific knowledge ecosystem's inability to properly contextualize and integrate data and discourse in machine-interpretable form. This inherently limits the productivity of research and the progress toward cures for devastating diseases such as Alzheimer's and Parkinson's. SWAN (Semantic Web Applications in Neuromedicine) is an interdisciplinary project to develop a practical, common, semantically structured, framework for biomedical discourse initially applied, but not limited, to significant problems in Alzheimer Disease (AD) research. The SWAN ontology has been developed in the context of building a series of applications for biomedical researchers, as well as in extensive discussions and collaborations with the larger bio-ontologies community. In this paper, we present and discuss the SWAN ontology of biomedical discourse. We ground its development theoretically, present its design approach, explain its main classes and their application, and show its relationship to other ongoing activities in biomedicine and bio-ontologies.

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“…In addition, it is exceeding the memory size and congestion of the RDF data sets. Nevertheless, this approach has been implemented by systems like Oracle [14], 3store [5], Redland [20], RDFStore [21] and rdfDB [22]. The research community later introduces an alternative solution for improving the triples table and minimize the number of selfjoins issues.…”

Section: Related Workmentioning

confidence: 99%

“…In summary, property tables are rarely used due to their complexity and inability to handle multi-valued attributes. However, this approach has been used by tools like Sesame [23], Jena2 [12], RDFSuite [24] and 4store [21].…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Scalable RDF Indexing Strategy based on B-Hashed-Bitmap Algorithm using CUDA

Sankar¹,

Singh²,

Sayed³

et al. 2014

IJCA

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Indexing enormous databases such as RDF has been a focus of intense research. As is well understood, indexing plays a pivotal role in speeding up data retrieval operations and query performance. Besides expediting search, an index can motivate new data-store schemes and technologies that can possibly revolutionize large data-analytics engine design, more often relevant to semantic web. Due to the proliferation of internet and the ease of creating and generating data on the fly-handling, storing and the subsequent semantic processing has proven to be a major bottleneck for the RDF data community. Handling data of such scale and magnitude requires a parallel approach as provided by the GPUs (Graphical processing units). In this paper, a new efficient and scalable index is proposed that uses a combination of B+ trees, hashing and sparse matrices. These data structures have an edge over others in terms of their implementation as a parallel algorithm using the CUDA (Compute Unified Device Architecture) framework meant to program massively parallel GPU multicores. So far, RDF data has been mostly implemented either as a RDBMS or as a non-native datastore, in both cases the sequential indexing strategy fails miserably with the scaling of the data-store. Parallel implementation of indices provides a suitable option for dealing with scalable and dynamically generated data over distributed networks. The crucial sparse matrix part of the proposed index is benchmarked against different CUDA memory implementations to derive optimal matrix processing options. The sparse matrix search is profiled using cudamemchk and visual profiler for identifying bottlenecks and inconsistencies in thread execution called thread divergence. Benchmarking the data provides promising results for a B+ tree based index coupled with hashing and sparse matrix implementations.

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Boca: an open-source RDF store for building Semantic Web applications

Cited by 6 publications

References 2 publications

A Comprehensive Review and Synthesis of Open Source Research

A Comprehensive Review and Synthesis of Open Source Research

The SWAN biomedical discourse ontology

An Efficient and Scalable RDF Indexing Strategy based on B-Hashed-Bitmap Algorithm using CUDA

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