Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources

Mami, Mohamed Nadjib; Graux, Damien; Scerri, Simon; Jabeen, Hajira; Auer, Sören; Lehmann, Jens

doi:10.1007/978-3-030-30796-7_15

Cited by 32 publications

(22 citation statements)

References 26 publications

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“…The definition, application and optimization of new functions and constraints to address other challenges for querying tabular data is one of the main lines for future work [17]. We also want to study the performance of the proposed workflow over OBDA distributed query systems such as the ones proposed in [15,22]. In addition, we will study the challenges for querying other data formats (e.g., XML, JSON) in an OBDA context and extend our approach to incorporate them.…”

Section: Discussionmentioning

confidence: 99%

“…starting, query rewriting, query translation, and execution phases. In previous proposals [22,24], when D = D tabular , the starting phase time is negatively impacted since the system has to load the data sources to a RDBMS before executing the rest of the phases. Additionally, the performance is also affected during the execution phase due the absence of integrity constraints over the schema.…”

Section: The Morph-csv Frameworkmentioning

confidence: 99%

“…[16] extends a column-store, whereas [1] extends a relational DBMS and Drill loads the data into NoSQL databases. Current Ontology Based Data Integration (OBDI) open source systems that take tabular data as input are Ontario [15] and Squerall [22]. Ontario is a federated query processing approach for heterogeneous data sources.…”

Section: Related Workmentioning

confidence: 99%

“…Implicit constraints over data sources are usually explicitly defined in mappings and tabular metadata, e.g., the W3C recommendation to annotate tabular data, CSVW [28]. This information has not been included in the majority of OBDA query translation engines [24,15], and those engines that have included it [22], are not fully documented for their use. Examples of these constraints are the standardization of the column format (e.g., dates), integrity constraints or datatypes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV

Chaves-Fraga,

Ruckhaus,

Priyatna

et al. 2020

Preprint

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Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational database, CSV, JSON), either by materializing integrated data into RDF or by performing on-the-fly integration via SPARQL-to-SQL query translation. In the specific case of tabular datasets comprised of several CSV or Excel files, query translation approaches have been applied taking as input a lightweight schema with table and column names, and considering each source as a single table that can be loaded into a relational database system (RDB). This naïve approach does not consider implicit constraints in this type of data, e.g., referential integrity among data sources, datatypes, or data integrity; thus, completeness and performance of query processing can be affected. Our work is focused on explicitly enforcing implicit constraints during OBDA query translation over tabular data. We propose Morph-CSV, a framework that enforces constraints and can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV resorts to both a Constraints component and a set of operators that apply each type of constraint to the input with the aim of enhancing query completeness and performance. We evaluate Morph-CSV against a set of real-world open tabular datasets in the domain of the public transport; Morph-CSV is compared with existing approaches in terms of query result completeness and performance.

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Section: Discussionmentioning

confidence: 99%

Section: The Morph-csv Frameworkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV

Chaves-Fraga,

Ruckhaus,

Priyatna

et al. 2020

Preprint

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show abstract

“…Similarly, declarative languages have been proposed to allow for the definition of functions in the mappings; exemplar approaches include R2RML-F [8], FunUL [17], RML+FnO [7], and D-REPR [26]. Moreover, mapping engines enable to interpret functions in declarative mappings (e.g., Squerall [20], RMLStreamer 14 and CARML 15 for RML+FnO), as well as in non-declarative formalisms [18]. FunMap optimizations currently are performed over static data and require the implementation of the join conditions by a KG creation engine.…”

Section: Related Workmentioning

confidence: 99%

FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

Jozashoori,

Chaves-Fraga,

Iglesias

et al. 2020

Preprint

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Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -specified with functionbased mapping languages like FunUL and RML+FnO -can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RMLcompliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.

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A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

Gomez,

Ruipérez‐Valiente,

García Clemente

2023

Softw Pract Exp

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During the last few years, there has been increasing attention paid to serious games (SGs), which are games used for non‐entertainment purposes. SGs offer the potential for more valid and reliable assessments compared to traditional methods such as paper‐and‐pencil tests. However, the incorporation of assessment features into SGs is still in its early stages, requiring specific design efforts for each game and adding significant time to the design of Game‐based Assessments (GBAs). In this research, we present a completely novel framework that aims to perform interoperable GBAs by: (a) integrating a common GBA ontology model to process RDF data; (b) developing in‐game metrics to infer useful information and assess learners; (c) integrating a service API to provide an easy way to interact with the framework. We then validate our approach through performance evaluation and two use cases, demonstrating its effectiveness in real‐world scenarios with large‐scale datasets. Our results show that the developed framework achieves excellent performance, replicating metrics from previous literature. We anticipate that our work will help alleviate current limitations in the field and facilitate the deployment of GBAs as a Service.

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Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources

Cited by 32 publications

References 26 publications

Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV

Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV

FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

A framework to support interoperable Game‐based Assessments as a Service (GBAaaS): Design, development, and use cases

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