Data Management Fundamentals: Database Management System

Elmasri, Ramez

doi:10.1007/978-1-4899-7993-3_80674-1

Cited by 72 publications

(100 citation statements)

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“…We can group the existing view models into four categories, namely; (a) classical (or relational) views [4,22], (b) Object-Oriented (00) view models [5,23], (c) semi-structured (namely XML) view models [24][25][26] and (d) view models for SW. An extensive set of literature can be found in both academic and industry forums in relation to various view related issues such as (i) models, (ii) design, (iii) performance, (iv) automation and (v) turning and refinement, mainly supporting the 2-Es; data Extraction and Elaboration (with and some research directions towards 3-Es, i.e. 2-Es and data Extension).…”

Section: Related Workmentioning

confidence: 99%

“…Conceptual Objects (CO): CO refers to model elements (objects, their properties, constraints and relationships) and their semantic inter-relationships (such as composition, ordering, association, sequence, all etc) captured at the conceptual level, using a well-defined modeling language such as UML, or XSemantic nets [ 10,11], OWL or E-ERD [4] etc. A CO can be either of type simple content (Scontent) or complex content (Ccontent) depending on its internal structure [10,41,44].…”

Section: Conceptual View Propertiesmentioning

confidence: 99%

“…The set of binary and unary operators provided here is a complete or basic set; i.e. other operators, such as division operator [4] and compression {see section 6) can be derived from these basic set of operators.…”

Section: Conceptual View Operatorsmentioning

confidence: 99%

“…In the case of views, it is usually specified by the data languages (and mostly excluding constraints associated with data semantics) in which they are defined in. For example, in relational model, views are defined using SQL and a limited set of constraints can be defined using SQL [4,22], namely, (i) presentation specific (such as display headings, column width, pattern order etc), (ii) range and string patterns for aggregate fields, (iii) input formats for updatable views, and (iv) other DBMS specific (such view materialization, table block, size, caching options etc).…”

Section: Conceptual View Constraintsmentioning

confidence: 99%

“…They have well defined basic principles [4] on which they are built upon. Due to this, supporting data intensive technologies, such as transaction processing, business queries, data warehousing, data mining etc.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modeling Ontology Views

Rajugan

Chang

Dillon

et al.

Industrial Applications of Semantic Web

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Abstract:The emergence of Semantic Web (SW) and the related technologies promise to make the web a meaningful experience. However, high level modeling, design and querying techniques proves to be a challenging task for organizations that are hoping to utilize the SW paradigm for their industrial applications. To address one such issue, in this paper, we propose an abstract view model with conceptual extensions for the SW. First we outline the view model, its properties and some modeling issues with the help of an industrial case study example. Then, we provide some discussions on constructing such views (at the conceptual level) using a set of operators. Later we provide a brief discussion on how such this view model can utilized in the MOVE [1] system, to design and construct materialized Ontology views to support Ontology extraction.Keywords: Semantic Web (SW), view models. Ontology views, Object-Oriented conceptual models (OOCM), conceptual views. Ontology extraction INTRODUCTIONThe emergence of Semantic Web (SW) and the related technologies promise to make the web a meaningful experience. Conversely, success of SW and its applications depends heavily on utilization and interoperability of well formulated Ontology bases in an automated, heterogeneous environment. This creates a need to investigate utilization of (materialized) Ontology views [2] in SW applications, such as; (a) Ontology extraction, (b) Ontology versioning, (c) sub-ontology bases, and (d) SW-wrappers for traditional data sources, in industrial settings. However, unlike

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Section: Related Workmentioning

confidence: 99%

Section: Conceptual View Propertiesmentioning

confidence: 99%

Section: Conceptual View Operatorsmentioning

confidence: 99%

Section: Conceptual View Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Ontology Views

Rajugan

Chang

Dillon

et al.

Industrial Applications of Semantic Web

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Improving Interoperability of Engineering Tools – Data Exchange in Plant Design

Fillinger

Bonart

Welscher

et al. 2017

Chemie Ingenieur Technik

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Software interoperability plays a key role regarding digitalization in process industries. In this contribution, an overview of current developments in the field of data exchange and standardization is given. A prototypical framework for the heterogeneous data exchange between the piping and instrumentation diagram (P&ID) software X-Visual PlantEngineer, the integrated engineering tool MOSAIC PlantDesign, and the 3D CAD software AVEVA PDMS is presented for distillation columns. Data exchange between P&ID tools of different vendors, developed by the DEXPI initiative, is considered as an instance of homogeneous data transfer.

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Database Semantics for Talking Autonomous Robots

Hausser

2020

Lecture Notes in Computer Science

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Data Management Fundamentals: Database Management System

Cited by 72 publications

References 0 publications

Modeling Ontology Views

Modeling Ontology Views

Improving Interoperability of Engineering Tools – Data Exchange in Plant Design

Database Semantics for Talking Autonomous Robots

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