RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration

Pinkel, Christoph; Binnig, Carsten; Jiménez-Ruiz, Ernesto; May, Wolfgang; Ritze, Dominique; Skjæveland, Martin G.; Solimando, Alessandro; Kharlamov, Evgeny

doi:10.1007/978-3-319-18818-8_2

Cited by 21 publications

(20 citation statements)

References 18 publications

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“…In this section we summarize the main contributions of the RODI benchmark. We note that an earlier version of RODI has been introduced in [42]. In this paper we significantly extend our earlier results in several important directions: we extended the systematic analyses of challenges and related approaches; we now cover several new benchmark scenarios as well as additional test categories; we significantly extended the scope of the experimental study and now we cover seven different systems.…”

Section: Contributionsmentioning

confidence: 63%

RODI: Benchmarking relational-to-ontology mapping generation quality

Pinkel

Binnig

Jiménez-Ruiz

et al. 2017

Self Cite

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Accessing and utilizing enterprise or Web data that is scattered across multiple data sources is an important task for both applications and users. Ontology-based data integration, where an ontology mediates between the raw data and its consumers, is a promising approach to facilitate such scenarios. This approach crucially relies on useful mappings to relate the ontology and the data, the latter being typically stored in relational databases. A number of systems to support the construction of such mappings have recently been developed. A generic and effective benchmark for reliable and comparable evaluation of the practical utility of such systems would make an important contribution to the development of ontology-based data integration systems and their application in practice. We have proposed such a benchmark, called RODI. In this paper, we present a new version of RODI, which significantly extends our previous benchmark, and we evaluate various systems with it. RODI includes test scenarios from the domains of scientific conferences, geographical data, and oil and gas exploration. Scenarios are constituted of databases, ontologies, and queries to test expected results. Systems that compute relational-to-ontology mappings can be evaluated using RODI by checking how well they can handle various features of relational schemas and ontologies, and how well the computed mappings work for query answering. Using RODI, we conducted a comprehensive evaluation of seven systems.

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Section: Contributionsmentioning

confidence: 63%

RODI: Benchmarking relational-to-ontology mapping generation quality

Pinkel

Binnig

Jiménez-Ruiz

et al. 2017

Self Cite

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“…In order to support OPTIQUE's deployment we developed a BOOTOX [6,13] system for "bootstrapping" (i.e., extracting) W3C standardised OWL 2 ontologies and R2RML mappings from static and streaming relational schema and data. Consider, e.g., a class Turbine; a mapping for it is an expression of the form: Turbine(f ( x)) ← ∃ y SQL( x, y), that can be seen as a view definition, where SQL( x, y) is an SQL query, x are its output variables, y are its variables that are projected out and f is a function that converts tuples returned by SQL into identifiers of objects populating the class Turbine.…”

Section: Optique Systemmentioning

confidence: 99%

Enabling semantic access to static and streaming distributed data with optique

Kharlamov

Brandt

Giese

et al. 2016

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems

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Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work, we show how Semantic Technologies implemented in our system OPTIQUE can simplify such complex diagnostics by providing an abstraction layerontology-that integrates heterogeneous data. In a nutshell, OP-TIQUE allows complex diagnostic tasks to be expressed with just a few high-level semantic queries, which can be easily formulated with our visual query formulation system. OPTIQUE can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment.

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“…The Optique platform allows to: create and edit mappings; create, edit, and import ontologies [54][55][56][57][58][59]; integrate several relational databases and data streams; formulate and visualise one time and continuous queries; efficiently process both static and streaming information; and browse query results. Thus, the Optique platform satisfies all the Siemens system Requirements R1-R5.…”

Section: Optique Platformmentioning

confidence: 99%

Semantic access to streaming and static data at Siemens

Kharlamov

Mailis

Mehdi

et al. 2017

Journal of Web Semantics

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We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

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RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration

Cited by 21 publications

References 18 publications

RODI: Benchmarking relational-to-ontology mapping generation quality

RODI: Benchmarking relational-to-ontology mapping generation quality

Enabling semantic access to static and streaming distributed data with optique

Semantic access to streaming and static data at Siemens

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