Provenance for SPARQL Queries

Damásio, Carlos Viegas; Analyti, Anastasia; Antoniou, Grigoris

doi:10.1007/978-3-642-35176-1_39

Cited by 18 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9 The queries are then encoded into a logical plan (a tree of operators), which is then optimized into a physical query plan as in any standard database system. The system supports all basic SPARQL operations, in- 9 We note that our current system does not parse full SPARQL queries at this stage. Adapting a SPARQL parser is currently in progress.…”

Section: Methodsmentioning

confidence: 99%

“…While named graphs are often used for provenance, they are also used for other purposes, for example, to track access control information. Thus, while RDF databases (i.e., triple stores) support named graphs, there has only been a relatively small amount of works specifically focused on provenance within the triple store itself and much of it has been focused on theoretical aspects of the problem [9,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Executing Provenance-Enabled Queries over Web Data

Wylot

Cudré-Mauroux

Groth

2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, because of this heterogeneity, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triple stores. In this paper, we tackle the problem of efficiently executing provenance-enabled queries over RDF data. We propose, implement and empirically evaluate five different query execution strategies for RDF queries that incorporate knowledge of provenance. The evaluation is conducted on Web Data obtained from two different Web crawls (The Billion Triple Challenge, and the Web Data Commons). Our evaluation shows that using an adaptive query materialization execution strategy performs best in our context. Interestingly, we find that because provenance is prevalent within Web Data and is highly selective, it can be used to improve query processing performance. This is a counterintuitive result as provenance is often associated with additional overhead.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Executing Provenance-Enabled Queries over Web Data

Wylot

Cudré-Mauroux

Groth

2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

show abstract

“…Therefore, the following approaches have been introduced. Damásio et al developed an approach for querying provenance information for LOD obtained from SPARQL endpoints [121]. This approach translates SPARQL into annotated relational algebra, in which the annotated relations have values from the most general m-semiring.…”

Section: Querying Rdf Provenancementioning

confidence: 99%

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

“…Queries, for example expressed in the standard SPARQL query language, represent complex patterns of triples. Negation is an important feature of Semantic Web languages, so semirings with monus are also deployed in this setting [9]; these semirings with monus must also verify additional axioms imposed by the semantics of SPARQL [15].…”

Section: Beyond Relational Provenancementioning

confidence: 99%

Provenance in Databases: Principles and Applications

Senellart

2019

Reasoning Web. Explainable Artificial Intelligence

View full text Add to dashboard Cite

Data provenance is extra information computed during query evaluation over databases, which provides additional context about query results. Several formal frameworks for data provenance have been proposed, in particular based on provenance semirings. The provenance of a query can be computed in these frameworks for a variety of query languages. Provenance has applications in various settings, such as probabilistic databases, view maintenance, or explanation of query results. Though the theory of provenance semirings has mostly been developed in the setting of relational databases, it can also apply to other data representations, such as XML, graph, and triple-store databases.

show abstract

Provenance for SPARQL Queries

Cited by 18 publications

References 13 publications

Executing Provenance-Enabled Queries over Web Data

Executing Provenance-Enabled Queries over Web Data

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

Provenance in Databases: Principles and Applications

Contact Info

Product

Resources

About