Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling

Heling, Lars; Acosta, Maribel

doi:10.1007/978-3-030-49461-2_10

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…Definition 2.1 (Profile Feature [17]). Given an RDF graph G, a profile feature F (G) is defined as a characteristic describing a statistical feature F of graph G.…”

Section: Profile Featuresmentioning

confidence: 99%

“…Definition 2.2 (Profile Feature Estimation [17]). Given an RDF graph G, a projection function φ, a subgraph H ⊂ G, and the profile feature F (•), a profile feature estimation F (•) for G is defined as…”

Section: Profile Feature Estimationmentioning

confidence: 99%

“…In this work, we focus on the Characteristic Sets Profile Feature (CSPF) [17], which is a statistical feature of RDF datasets that includes the characteristic sets, their occurrence distribution, and the multiplicity of their predicates. We focus on this particular profile features as an informative statistical characterization of RDF datasets due to the following reasons.…”

Section: Introductionmentioning

confidence: 99%

“…Contributions This paper builds on a previous work of ours [17], which focuses on a sampling-based approach for estimating characteristic sets for RDF dataset profiles. We extend this work regarding two major aspects.…”

Section: Introductionmentioning

confidence: 99%

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Characteristic sets profile features: Estimation and application to SPARQL query planning

Heling

Acosta

2023

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RDF dataset profiling is the task of extracting a formal representation of a dataset’s features. Such features may cover various aspects of the RDF dataset ranging from information on licensing and provenance to statistical descriptors of the data distribution and its semantics. In this work, we focus on the characteristics sets profile features that capture both structural and semantic information of an RDF dataset, making them a valuable resource for different downstream applications. While previous research demonstrated the benefits of characteristic sets in centralized and federated query processing, access to these fine-grained statistics is taken for granted. However, especially in federated query processing, computing this profile feature is challenging as it can be difficult and/or costly to access and process the entire data from all federation members. We address this shortcoming by introducing the concept of a profile feature estimation and propose a sampling-based approach to generate estimations for the characteristic sets profile feature. In addition, we showcase the applicability of these feature estimations in federated querying by proposing a query planning approach that is specifically designed to leverage these feature estimations. In our first experimental study, we intrinsically evaluate our approach on the representativeness of the feature estimation. The results show that even small samples of just 0.5 % of the original graph’s entities allow for estimating both structural and statistical properties of the characteristic sets profile features. Our second experimental study extrinsically evaluates the estimations by investigating their applicability in our query planner using the well-known FedBench benchmark. The results of the experiments show that the estimated profile features allow for obtaining efficient query plans.

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“…Definition 2.1 (Profile Feature [17]). Given an RDF graph G, a profile feature F (G) is defined as a characteristic describing a statistical feature F of graph G.…”

Section: Profile Featuresmentioning

confidence: 99%

Section: Profile Feature Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characteristic sets profile features: Estimation and application to SPARQL query planning

Heling

Acosta

2023

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“…In heterogeneous federations, however, the query planning approaches cannot always rely on the same level of statistics from all sources and need to be adjusted to the statistics available at the individual sources. For instance, obtaining fine-grained statistics might require access to the entire dataset of a source for efficient computation [14] or require the services to be able to execute complex SPARQL expressions, such as aggregate queries. Furthermore, in the case that the interface language of an LDF service does not support the evaluation of a subexpression from the decomposition, the planner needs to obtain an efficient subplan for evaluating the subexpression over that service.…”

Section: Query Plannermentioning

confidence: 99%

A Framework for Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments

Heling¹,

Acosta²

2021

Preprint

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Linked Data Fragments (LDFs) refer to Web interfaces that allow for accessing and querying Knowledge Graphs on the Web. These interfaces, such as SPARQL endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions they can evaluate and the metadata they provide. Client-side query processing approaches have been proposed and are tailored to evaluate queries over individual interfaces. Moreover, federated query processing has focused on federations with a single type of LDF interface, typically SPARQL endpoints. In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. To this end, we formalize the concept of federations of Linked Data Fragment and propose a framework for federated querying over heterogeneous federations with different LDF interfaces. The framework comprises query decomposition, query planning, and physical operators adapted to the particularities of different LDF interfaces. Further, we propose an approach for each component of our framework and evaluate them in an experimental study on the well-known FedBench benchmark. The results show a substantial improvement in performance achieved by devising these interfaceaware approaches exploiting the capabilities of heterogeneous interfaces in federations.

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