Efficient Query Processing in RDF Databases

Boncz, Peter; Erling, Orri; Duc, P. Minh

doi:10.1201/b16859-13

“…Table 2 illustrates that 2620 the tool can process data sets of different sizes. Thus, the schema transformation is feasible and efficient even for large data sets by benefiting from indexing techniques to deploy efficient access on SPARQL endpoints [37]. Table 7 show the percentage of users that successfully performed the redefinition and enrichment tasks with the tool, respectively.…”

Section: Levelmemberoutdegreementioning

confidence: 99%

“…At the time of writing 37 http://www.w3.org/2011/gld/wiki/ISO_Extensions_ to_SKOS 38 http://rdf-vocabulary.ddialliance.org/xkos.html this paper, this proposal is in a preliminary status.…”

mentioning

confidence: 99%

Dimensional Enrichment of Statistical Linked Open Data

Varga

¹

,

Vaisman

²

,

Romero

³

et al. 2016

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On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.

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“…Table 2 illustrates that 2620 the tool can process data sets of different sizes. Thus, the schema transformation is feasible and efficient even for large data sets by benefiting from indexing techniques to deploy efficient access on SPARQL endpoints [37]. Usability.…”

Section: Levelmemberoutdegreementioning

confidence: 99%

“…Kämpgen et al [43,44] attempt to override the lack of structure in QB, discussed in Section 3, defining an OLAP data model on top of QB and other related vocabularies, e.g., some proposed ISO extensions to SKOS. 37 . They propose to represent each level as an in-2760 stance of a class skosclass:ClassificationLevel and organize levels in hierarchies via stating the depth of each level in the hierarchy using the skosclass:depth property.…”

mentioning

confidence: 99%

Dimensional enrichment of statistical linked open data

Varga

¹

,

Vaisman

²

,

Romero

³

et al. 2016

Journal of Web Semantics

View full text Add to dashboard Cite

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process.Peer ReviewedPostprint (author's final draft

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