Using a conceptual data language to describe a database and its interface

Процессные модели предметной области широко применяются при проектировании баз данных, а именно в ходе концептуального моделирования данных. Предлагается решение проблемы неоднозначности преобразования процессных доменных моделей типа SADT в концептуальные модели данных. Domain process models are widely used in database design, namely in conceptual data modeling. The solution of the problem of ambiguity of transformation of process domain models of the SADT type into conceptual data models is proposed.

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Conceptual data modeling of expert systems

IEEE Expert ◽

10.1109/64.21900 ◽

1989 ◽

Vol 4 (1) ◽

pp. 50-61 ◽

Cited By ~ 11

Author(s):

J.P. Held ◽

J.V. Carlis

Keyword(s):

Expert Systems ◽

Data Modeling ◽

Conceptual Data

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A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽

10.3233/sw-200416 ◽

2020 ◽

pp. 1-16

Author(s):

Francesco Beretta

Keyword(s):

Semantic Analysis ◽

Historical Research ◽

Data Modelling ◽

Fine Grained ◽

Fair Principles ◽

Active Research ◽

New Research ◽

High Level ◽

Conceptual Data ◽

Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

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Converting XML DTDs to UML diagrams for conceptual data integration

Data & Knowledge Engineering ◽

10.1016/s0169-023x(02)00142-8 ◽

2003 ◽

Vol 44 (3) ◽

pp. 323-346 ◽

Cited By ~ 16

Author(s):

Mikael R. Jensen ◽

Thomas H. Møller ◽

Torben Bach Pedersen

Keyword(s):

Data Integration ◽

Uml Diagrams ◽

Conceptual Data

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A METRIC-BASED APPROACH FOR PREDICTING CONCEPTUAL DATA MODELS MAINTAINABILITY

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194001000736 ◽

2001 ◽

Vol 11 (06) ◽

pp. 703-729 ◽

Cited By ~ 11

Author(s):

MARIO PIATTINI ◽

MARCELA GENERO ◽

LUIS JIMÉNEZ

Keyword(s):

Structural Complexity ◽

Data Models ◽

Major Step ◽

Development Life Cycle ◽

Novel Method ◽

Is Development ◽

Entity Relationship ◽

Conceptual Data ◽

Entity Relationship Diagrams

It is generally accepted in the information system (IS) field that IS quality is highly dependent on the decisions made early in the development life cycle. The construction of conceptual data models is often an important task of this early development. Therefore, improving the quality of conceptual data models will be a major step towards the quality improvement of the IS development. Several quality frameworks for conceptual data models have been proposed, but most of them lack valid quantitative measures in order to evaluate the quality of conceptual data models in an objective way. In this article we will define measures for the structural complexity (internal attribute) of entity relationship diagrams (ERD) and use them for predicting their maintainability (external attribute). We will theoretically validate the proposed metrics following Briand et al.'s framework with the goal of demonstrating the properties that characterise each metric. We will also show how it is possible to predict each of the maintainability sub-characteristics using a prediction model generated using a novel method for induction of fuzzy rules.

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A Framework of Local Geospatial Data Infrastructure for Sustainable Urban Development

Jurnal Teknologi ◽

10.11113/jt.v71.3834 ◽

2014 ◽

Vol 71 (4) ◽

Author(s):

Azman Ariffin ◽

Nabila Ibrahim ◽

Ghazali Desa ◽

Uznir Ujang ◽

Hishamuddin Mohd Ali ◽

...

Keyword(s):

Urban Development ◽

Spatial Data ◽

Geospatial Data ◽

Sustainable Urban Development ◽

Data Infrastructure ◽

Shape Category ◽

Key Dimensions ◽

Data Layers ◽

Conceptual Data

This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.

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