Semantic Provenance for eScience: Managing the Deluge of Scientific Data

Sahoo, Satya S.; Sheth, Amit P.; Henson, Cory

doi:10.1109/mic.2008.86

Cited by 74 publications

(48 citation statements)

References 4 publications

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“…This could be considered a type of provenance broadly, but goes beyond the range of provenance information typically collected in production information systems. Various approaches have been tried, such as for glycoproteomics, 15 but no clear guidelines yet exist.…”

Section: Veracitymentioning

confidence: 99%

NIST Big Data Interoperability Framework: Volume 4, Security and Privacy

2015

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The Information Technology Laboratory (ITL) at NIST promotes the U.S. economy and public welfare by providing technical leadership for the Nation's measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology (IT). ITL's responsibilities include the development of management, administrative, technical, and physical standards and guidelines for the cost-effective security and privacy of other than national security-related information in federal information systems. This document reports on ITL's research, guidance, and outreach efforts in IT and its collaborative activities with industry, government, and academic organizations. AbstractBig Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework series of volumes. This volume, Volume 4, contains an exploration of security and privacy topics with respect to Big Data. This volume considers new aspects of security and privacy with respect to Big Data, reviews security and privacy use cases, proposes security and privacy taxonomies, presents details of the Security and Privacy Fabric of the NIST Big Data Reference Architecture (NBDRA), and begins mapping the security and privacy use cases to the NBDRA.

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Section: Veracitymentioning

confidence: 99%

NIST Big Data Interoperability Framework: Volume 4, Security and Privacy

2015

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“…While it is considered best practice in any domain to link a new ontology to a relevant predecessor (Euzenat and Shvaiko, 2007;Smith et al, 2007;Sahoo et al, 2008), there are no quasi-legal requirements to do so. No laws of priority or rules for typifying classes exist that, if violated, would render the new ontology invalid.…”

Section: Nomenclatural and Taxonomic Legacymentioning

confidence: 99%

Biological Taxonomy and Ontology Development: Scope and Limitations

Franz

2011

Biodiv. Inf.

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Abstract.  The prospects of integrating full-blown biological taxonomies into an ontological reasoning framework are reviewed. Traditionally ontological representations of taxonomy have adopted the model of a single and static hierarchy. This model is contrasted with a more realistic situation involving dynamic revisions of particular groups and alignments among alternative taxonomic perspectives. Taxonomic practice is bound by a range of epistemological constraints and linguistic conventions that run orthogonal to the logical background from which ontological entities and relationships originate, resulting in severe challenges for ontological representation and reasoning. In particular, the purported existence of a single hierarchy in nature forces taxonomists to gradually approximate this hierarchy and make frequent rearrangements in light of new evidence. The evolvability of taxa implies that taxon-defining features may be lost in subordinate members or independently gained across multiple sections of the tree of life. As a result, many terms for phenotypic properties are phylogenetically underdetermined and have limited hierarchical transitivity. The standard approach of defining taxa both in reference to properties (intensional) and members (ostensive) undermines the individual/class dichotomy that sustains conventional ontologies, and compromises the reasoning capabilities associated with this distinction. Neither the use of Linnaean ranks in taxonomy nor the 250-year legacy of nomenclatural adjustments have obvious analogues in the ontological realm. Lastly, the piece-meal appearance of full-blown taxonomic information makes it necessary to use expert alignments to obtain a comprehensive static perspective. In light of these limitations, research along the taxonomy/ontology interface should focus either on strictly nomenclatural entities and relationships or on ontology-driven strategies for aligning multiple taxonomies, but not on building static networks for large portions of the tree of life. The prospects of using ontology-based services in taxonomy will largely depend on the ability of the taxonomic expert community to present its products in ways that are more compatible with ontological principles than concurrent practice.

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“…One approach to achieve this is described in [88], where authors propose to use specialized provenance services that can be integrated into a scientific workflow on demand. For example, while the core functionality of a scientific workflow management system records a data object instance consumed by some task, a specialized provenance service records the same information, but using domain terms, such as "protein complex" for the data object and "alignment" for the task.…”

Section: Provenance Model Managementmentioning

confidence: 99%

“…Recently, the Semantic Web [16,94] technologies have been increasingly used for provenance management due to their flexibility and semantics support [48,50,65,88,121], such that provenance metadata is represented and captured via Resource Description Framework (RDF) [111,114], RDF Schema (RDFS) [113], and Web Ontology Language (OWL) [110], and queried using the SPARQL [115] query language. This technological suite, enhanced with the Semantic Web inference support, was shown to address [88] the four functional requirements for provenance identified by the Open Provenance Model: (1) provenance information interoperability, (2) ease of application development, (3) precise description of provenance information, and (4) inference capability and digital representation of provenance. In addition, in our work, we choose a Semantic Web approach for provenance management due to its several advantages.…”

Section: Introductionmentioning

confidence: 99%

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

Chebotko¹,

Lu²,

Fei³

et al. 2010

Data & Knowledge Engineering

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Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFPROV, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFPROV system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.

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Semantic Provenance for eScience: Managing the Deluge of Scientific Data

Cited by 74 publications

References 4 publications

NIST Big Data Interoperability Framework: Volume 4, Security and Privacy

NIST Big Data Interoperability Framework: Volume 4, Security and Privacy

Biological Taxonomy and Ontology Development: Scope and Limitations

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

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