Query-oriented summarization of RDF graphs

Čebirić, Šejla; Goasdoué, François; Manolescu, Ioana

doi:10.14778/2824032.2824124

Cited by 30 publications

(4 citation statements)

References 6 publications

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“…However, most of the solutions introduced to infer structures from documents encoded in XML could not be applied to documents encoded in JSON. Furthermore, other efforts were conducted to infer RDF data [46]. The problem with this class of work is none of these approaches is designed to deal with massive datasets whereas current applications are data intensive, and they are using JSON encoding.…”

Section: Schema Heterogeneitymentioning

confidence: 99%

A dataspace-based framework for OLAP analyses in a high-variety multistore

et al. 2021

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The success of NoSQL DBMSs has pushed the adoption of polyglot storage systems that take advantage of the best characteristics of different technologies and data models. While operational applications take great benefit from this choice, analytical applications suffer the absence of schema consistency, not only between different DBMSs but within a single NoSQL system as well. In this context, the discipline of data science is steering analysts away from traditional data warehousing and toward a more flexible and lightweight approach to data analysis. The idea is to perform OLAP analyses in a pay-as-you-go manner across heterogeneous schemas and data models, where the integration is progressively carried out by the user as the available data is explored. In this paper, we propose an approach to support data analysis within a high-variety multistore, with heterogeneous schemas and overlapping records. Our approach supports relational, document, wide-column, and key-value data models by automatically handling both data model and schema heterogeneity through a dataspace layer on top of the underlying DBMSs. The expressiveness we enable corresponds to GPSJ queries, which are the most common class of queries in OLAP applications. We rely on nested relational algebra to define a cross-database execution plan. The system has been prototyped on Apache Spark.

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Section: Schema Heterogeneitymentioning

confidence: 99%

A dataspace-based framework for OLAP analyses in a high-variety multistore

et al. 2021

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“…Two main categories of approaches can be distinguished: (1) aggregation approaches [35,36], which rely on strategies for grouping the graph nodes into groups based on diverse functions (e.g., similarity of values of attributes, relationships to adjacent nodes, application of aggregation functions, etc. ); and (2) structural approaches [37,38,39] which rely on extracting a schema representing a summary of the graph, in general, based on equivalence relations of nodes. The approach we propose here falls into the former.…”

Section: Rdf Storage and Sparql Query Optimisation While Performancementioning

confidence: 99%

Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores

Ravat

Song

Teste

et al. 2020

International Journal of Information Management

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This paper proposes an approach to tackle the problem of querying large volume of statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the analysis of this kind of data. Specifically, we define a conceptual model to represent original RDF data with aggregates in a multidimensional structure. A set of translations rules for converting a well-known multidimensional RDF modelling vocabulary into the proposed conceptual model is then proposed. We implement the conceptual model in six different data stores: two RDF triple stores (Jena TDB and Virtuoso), one graph-oriented NoSQL database (Neo4j), one column-oriented data store (Cassandra), and two relational databases (MySQL and PostGreSQL). We compare the querying performance, with and without aggregates, in these data stores. Experimental results, on real-world datasets containing 81.92 million triplets, show that preaggregation allows for reducing query runtime in all data stores. Neo4j NoSQL and relational databases with aggregates outperform triple stores speeding up to 99% query runtime.

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“…RDF summaries defined in [13,14,12] adapt the idea of quotient summaries to two characteristic features of RDF graphs: (i) the presence of type triples (zero, one, or any number of types for a given resource), and (ii) the presence of schema triples. As we explained in Section 2.1, RDF Schema information is also expressed by means of triples, which are part of G. [12] shows that quotient summarization of schema triples does more harm than good, as it destroys the semantics of the original graph.…”

Section: Other Structural Quotient Summariesmentioning

confidence: 99%

Summarizing semantic graphs: a survey

et al. 2018

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The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area, and identify the most pertinent summarization method for a variety of usage scenarios.

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Query-oriented summarization of RDF graphs

Cited by 30 publications

References 6 publications

A dataspace-based framework for OLAP analyses in a high-variety multistore

A dataspace-based framework for OLAP analyses in a high-variety multistore

Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL, RDF and relational data stores

Summarizing semantic graphs: a survey

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