Semantic mediator querying

Bouchou, Béatrice; Niang, Cheikh

doi:10.1145/2628194.2628218

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…There has been earlier work on making SQL queries easier [8,5]. The basic idea is to provide the business analyst a way to express queries in a simpler language (SPARQL query).…”

Section: Related Work: Extracting Event Data From Databasesmentioning

confidence: 99%

Extracting Multiple Viewpoint Models from Relational Databases

Berti

Aalst

2020

Lecture Notes in Business Information Processing

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Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in reallife processes often different case notions are intertwined. For example, events of the same order handling process may refer to customers, orders, order lines, deliveries, and payments. Therefore, we propose to use Multiple Viewpoint (MVP) models that relate events through objects and that relate activities through classes. The required event data are much closer to existing relational databases. MVP models provide a holistic view on the process, but also allow for the extraction of classical event logs using different viewpoints. This way existing process mining techniques can be used for each viewpoint without the need for new data extractions and transformations. We provide a toolchain allowing for the discovery of MVP models (annotated with performance and frequency information) from relational databases. Moreover, we demonstrate that classical process mining techniques can be applied to any selected viewpoint.

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“…There has been earlier work on making SQL queries easier [8,5]. The basic idea is to provide the business analyst a way to express queries in a simpler language (SPARQL query).…”

Section: Related Work: Extracting Event Data From Databasesmentioning

confidence: 99%

Extracting Multiple Viewpoint Models from Relational Databases

Berti

Aalst

2020

Lecture Notes in Business Information Processing

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“…Typical applications include, e.g. simultaneous querying of multiple knowledge bases [31][32][33][34] or, as addressed here, the mapping of semantic content from a source ontology S to a target ontology T .…”

Section: Ontology Matchingmentioning

confidence: 99%

Applications of the Metadata Standards

Horsch

Chiacchiera

Cavalcanti

et al. 2021

SpringerBriefs in Applied Sciences and Technology

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This chapter addresses issues related to the practical use of the metadata standards, including syntactic interoperability and concrete scenarios from molecular modelling and simulation. It discusses challenges that arise from semantic heterogeneity, wherever multiple interoperability standards are concurrently employed for identical or overlapping domains of knowledge, or where domain ontologies need to be matched to top-level ontologies such as the European Materials and Modelling Ontology (EMMO).

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“…else if n = UNION(n1, n2) then Once it finishes processing the leaves, it continues to the upper levels in the tree (lines [7][8][9][10][11][12][13][14][15][16][17], where the SPARQL operators (JOIN, OPTIONAL, UNION, FIL-TER, and PROJECT) are translated into the corresponding SQL operators (InnerJoin, LeftJoin, Union, Filter, and Project, respectively). Once the root is translated, the process is finished and the resulting SQL expression is returned.…”

Section: From Sparql To Sqlmentioning

confidence: 99%

“…10 For example, the EPNet project 11 relies on Ontop to improve access for scholars to historical and cultural data on food production and commercial trade system during the Roman Empire from several data sources [14]. Also, Ontop is used in Semantic Mediator [7], for accessing electronic health records [46], and for querying temporal and streaming data in OBDA [41].…”

Section: Industrial Applicationsmentioning

confidence: 99%

Ontop: Answering SPARQL queries over relational databases

et al. 2016

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Abstract. We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL 2 QL and RDFS ontologies), and its support for all major relational databases.

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Semantic mediator querying

Cited by 13 publications

References 11 publications

Extracting Multiple Viewpoint Models from Relational Databases

Extracting Multiple Viewpoint Models from Relational Databases

Applications of the Metadata Standards

Ontop: Answering SPARQL queries over relational databases

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