Efficient RDF data management including provenance and uncertainty

McGlothlin, James P.; Khan, Latifur

doi:10.1145/1866480.1866508

Cited by 3 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much of the research for efficient taxonomic and type encodings that can be applied to ontologies and RDF graph structures go back many years, to the mid-19980s at the minimum. For bit-vector representation to support RDF triples, we investigated [14][15][16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Fast Semantic Attribute-Role-Based Access Control (ARBAC) in a Collaborative Environment

Obrsta¹,

McCandlessb²,

Ferrella³

2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

View full text Add to dashboard Cite

This paper is an early report of our continuing effort to provide a platform-independent framework so that information originators and security administrators can specify access rights to information consistently and completely, and that this specification is then rigorously enforced. To accomplish this objective it is necessary to link a security policy model to a policy language with sufficient expressive power to ensure logical consistency. For the purposes of this research we are using a modified Attribute-Role-Based Access Control (ARBAC) security model and the Web Ontology Language (OWL) with additional rules in a logic programming framework to express access policy, going beyond the limitations of previous attempts in this vein. In addition we are developing a mechanism using knowledge compilation techniques that allows access policy constraint checking to be implemented in real-time, via a bit-vector encoding that can be used for rapid run-time reasoning.

show abstract

Section: Related Workmentioning

confidence: 99%

Fast Semantic Attribute-Role-Based Access Control (ARBAC) in a Collaborative Environment

Obrsta¹,

McCandlessb²,

Ferrella³

2012

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

View full text Add to dashboard Cite

show abstract

“…RDF has a variety of serialization formats and syntaxes, including RDF/XML, 3 Turtle, 4 Notation3 (N3), 5 N-triples, 6 N-quads, 7 JSON-LD, 8 RDF/JSON, 9 RDFa, 10 and HTML5 Microdata. 11 These make it possible to express RDF statements differently for applications that require compatibility with XML, the most compact representation possible, define property values in website markup attributes, and so on [3].…”

Section: Introduction To Rdf Provenancementioning

confidence: 99%

“…These benefits make RDF appealing for a wide range of applications; however, RDF has shortcomings when it comes to encapsulating metadata to statements. With the proliferation of heterogeneous structured data sources, such as triplestores and LOD datasets, capturing data provenance, 12 i.e., the origin or source of data [7], and the technique used to extract it, is becoming more and more important, because it enables the verification of data, the assessment of reliability [8], the analysis of the processes that generated the data [9], decision support for areas such as cybersecurity [10,11], cyberthreat intelligence [12], and cyber-situational awareness [13], and helps express trustworthiness [14,15], uncertainty [16], and data quality [17]. Yet, the RDF data model does not have a built-in mechanism to attach provenance to triples or elements of triples.…”

Section: Introduction To Rdf Provenancementioning

confidence: 99%

“…While the World Wide Web Consortium (W3C) suggested RDF extensions in 2010 to support provenance in the upcoming version of the standard [20,21], none of these have been implemented in the next release in 2014, namely in RDF 1.1. 14 Related efforts 3 https://www.w3.org/TR/rdf-syntax-grammar/ 4 https://www.w3.org/TR/turtle/ 5 https://www.w3.org/TeamSubmission/n3/ 6 https://www.w3.org/TR/n-triples/ 7 https://www.w3.org/TR/n-quads/ 8 https://www.w3.org/TR/json-ld/ 9 https://www.w3.org/TR/rdf-json/ 10 https://www.w3.org/TR/rdfa-primer/ 11 https://www.w3.org/TR/microdata/ of the W3C have been kept to a minimum with the shutdown of the Provenance Working Group in 2013 [22].…”

Section: Introduction To Rdf Provenancementioning

confidence: 99%

See 1 more Smart Citation

Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs

Sikos

Philp

2020

Data Sci. Eng.

View full text Add to dashboard Cite

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs.

show abstract

Massive-Scale RDF Processing Using Compressed Bitmap Indexes

Madduri

2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The Resource Description Framework (RDF) is a popular data model for representing linked data sets arising from the web, as well as large scientific data repositories such as UniProt. RDF data intrinsically represents a labeled and directed multi-graph. SPARQL is a query language for RDF that expresses subgraph pattern-finding queries on this implicit multigraph in a SQLlike syntax. SPARQL queries generate complex intermediate join queries; to compute these joins efficiently, we propose a new strategy based on bitmap indexes. We store the RDF data in column-oriented structures as compressed bitmaps along with two dictionaries. This paper makes three new contributions. (i) We present an efficient parallel strategy for parsing the raw RDF data, building dictionaries of unique entities, and creating compressed bitmap indexes of the data. (ii) We utilize the constructed bitmap indexes to efficiently answer SPARQL queries, simplifying the join evaluations. (iii) To quantify the performance impact of using bitmap indexes, we compare our approach to the state-of-the-art triple-store RDF-3X. We find that our bitmap index-based approach to answering queries is up to an order of magnitude faster for a variety of SPARQL queries, on gigascale RDF data sets.

show abstract

Efficient RDF data management including provenance and uncertainty

Cited by 3 publications

References 7 publications

Fast Semantic Attribute-Role-Based Access Control (ARBAC) in a Collaborative Environment

Fast Semantic Attribute-Role-Based Access Control (ARBAC) in a Collaborative Environment

Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs

Massive-Scale RDF Processing Using Compressed Bitmap Indexes

Contact Info

Product

Resources

About