Data Modeling: Entity-Relationship Data Model

2003 ◽  
pp. 489-503
Author(s):  
Salvatore T. March
Keyword(s):  
Data Model ◽  
Data Modeling ◽  
Entity Relationship

scholarly journals A Method For Reverse Engineering Legacy Accounting Systems

1998 ◽  
Vol 2 (3) ◽  
pp. 93-104
Author(s):  
Joseph H. Callaghan ◽  
Thomas W. Lauer ◽  
Eileen Peacock
Keyword(s):  
Reverse Engineering ◽  
Data Model ◽  
Data Modeling ◽  
Future Research ◽  
Accounting Systems ◽  
Reverse Engineer ◽  
Modeling Approach ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
General Journal

The growing emphasis on information for the development of products, services, and managing activities has increased the need for the integration of data which has been collected and used for various IS applications. Problems arise from the integration of information from such a variety of sources. One approach to this problem is reverse engineer these systems. Reverse engineering derives a data model from existing sys-tems with the aim of redesigning it. The paper describes a data modeling approach that takes data from general journal and an archetypal specialized journal and translates it into an entity-relationship diagram. The article also discusses areas for future research.

scholarly journals Mapping Common Errors in Entity Relationship Diagram Design of Novice Designers

2021 ◽  
Vol 13 (1) ◽  
pp. 1-19
Author(s):  
Rami Rashkovits ◽  
Ilana Lavy
Keyword(s):  
Data Model ◽  
System Development ◽  
Data Modeling ◽  
Database Schema ◽  
Solo Taxonomy ◽  
Key Factor ◽  
Novice Designers ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Conceptual Data

Data modeling in the context of database design is a challenging task for any database designer, even more so for novice designers. A proper database schema is a key factor for the success of any information systems, hence conceptual data modeling that yields the database schema is an essential process of the system development. However, novice designers encounter difficulties in understanding and implementing such models. This study aims to identify the difficulties in understanding and implementing data models and explore the origins of these difficulties. This research examines the data model produced by students and maps the errors done by the students. The errors were classified using the SOLO taxonomy. The study also sheds light on the underlying reasons for the errors done during the design of the data model based on interviews conducted with a representative group of the study participants. We also suggest ways to improve novice designer's performances more effectively, so they can draw more accurate models and make use of advanced design constituents such as entity hierarchies, ternary relationships, aggregated entities, and alike. The research findings might enrich the data body research on data model design from the students' perspectives.

An Event-Oriented Data Modeling Technique Based on the Cognitive Semantics Theory

2012 ◽  
Vol 23 (4) ◽  
pp. 52-74 ◽  
Author(s):  
Dinesh Batra
Keyword(s):  
Data Model ◽  
Data Modeling ◽  
Modeling Technique ◽  
Cognitive Semantics ◽  
Conceptual Semantics ◽  
Comprehensive Data ◽  
Business Application ◽  
Business Events ◽  
Entity Relationship

The Resource-Event-Agent (REA) model has been proposed as a data modeling approach for representing accounting transactions. However, most business events are not transactions; thus, the REA formulation is incomplete. Based on the Conceptual Semantics theory, this paper discusses the entity-relationship event network (EREN) model, which extends the REA model and provides a comprehensive data template for a business event. Specifically, the notions of resource, event, and agent in the REA model are extended to include more discriminating entity types. The EREN technique can be used to identify events, sketch a network of events, and develop a data model of a business application by applying the EREN template to each event. Most extant techniques facilitate only the descriptive role whereas the EREN technique facilitates both the design and descriptive role of data modeling.

Data Modeling and Workflow Analysis of Cyber-Manufacturing Systems

2020 ◽  
Author(s):  
Zhengyi Song ◽  
Young Moon
Keyword(s):  
Data Model ◽  
Manufacturing Systems ◽  
Data Modeling ◽  
Web Based ◽  
Manufacturing Intelligence ◽  
Main Driver ◽  
Manufacturing Resources ◽  
Generic Data Model ◽  
Factory Of The Future ◽  
Entity Relationship

Abstract Cyber-Manufacturing System (CMS) is a vision for the factory of the future, where physical manufacturing resources and processes are integrated with computational workflows to provide on-demand, adaptive, and scalable manufacturing services. In CMS, functional manufacturing components in a factory floor are digitized and encapsulated in production services; and are accessible by users throughout the network. CMS utilizes data-centric technologies to program manufacturing activities in factory floors. Leveraging advanced technologies, CMS can provide robust solutions to achieve better manufacturing agility, flexibility, scalability, and sustainability than from traditional factories. While data is the main driver of the manufacturing activities in CMS, the lack of (i) a generic data model of explicit representation of the entities and stakeholders in CMS and (ii) workflow definition and analysis for service-orientated functionalities and manufacturing intelligence of CMS is still hindering the implementation of a fully executable CMS. To address such problems, this paper (i) formalizes a data modeling of CMS using Entity-Relationship (E-R) diagram, (ii) presents the definition and analysis of workflows along with data pipelines and Extract/Transform/Load (ETL) processes that automate the entire lifecycle activities in CMS and (iii) deploys the proposed data model and workflows in a Web-based application, and (iv) tests the functionality of this application with an industrial case and eventually validates the proposed data model and workflows.

Integrated 3D Spatial Data Model of Object-Oriented Digital Landslide

2012 ◽  
Vol 204-208 ◽  
pp. 4872-4877
Author(s):  
Da Xi Ma ◽  
Xiao Hong Liu ◽  
Li Wei Ma
Keyword(s):  
Spatial Data ◽  
Data Model ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Data Modeling ◽  
Object Oriented ◽  
Landslide Modeling ◽  
Space Data ◽  
Spatial Data Model ◽  
Three Dimensional Space

By analyzing the attributes of three-dimensional space data model, the integrated 3D spatial data adopts object-oriented method for digital landslide modeling. It achieves spatial data modeling for landslide geological entity. An experimental case is given to indicate the feasibility of this approach for spatial data modeling.

Designing an Engineering Database for Telephone Networks

1991 ◽  
Author(s):  
Scott G. Danielson
Keyword(s):  
Data Structure ◽  
Data Model ◽  
Database Modeling ◽  
Telephone Networks ◽  
Semantic Data ◽  
Engineering Data ◽  
Engineering Database ◽  
Engineering Work ◽  
Entity Relationship ◽  
Semantic Data Model

Abstract An engineering database modeling telephone outside plant networks is developed. Semantic and relational database design methodologies are used with the semantic data model developed based on an extended entity-relationship approach. This logical model is used to generate a normalized relational data structure. This database holds engineering data supporting engineering analyses, engineering work order generation procedures, and network planning activities. The database has been linked to separate network analysis programs and CAD-based network maps by a database application.

scholarly journals A Rule-based Conversion of an EER Schema to Neo4j Schema Constraints

2021 ◽  
Author(s):  
Telmo Henrique Valverde da Silva ◽  
Ronaldo dos Santos Mello
Keyword(s):  
Data Model ◽  
Design Methodology ◽  
A Priori ◽  
Database Management System ◽  
Conversion Process ◽  
Graph Database ◽  
Graph Databases ◽  
Rule Based ◽  
Promising Solution ◽  
Entity Relationship

Several application domains hold highly connected data, like supply chain and social network. In this context, NoSQL graph databases raise as a promising solution since relationships are first class citizens in their data model. Nevertheless, a traditional database design methodology initially defines a conceptual schema of the domain data, and the Enhanced Entity-Relationship (EER) model is a common tool. This paper presents a rule-based conversion process from an EER schema to Neo4j schema constraints, as Neo4j is the most representative NoSQL graph database management system with an expressive data model. Different from related work, our conversion process deals with all EER model concepts and generates rules for ensuring schema constraints through a set of Cypher instructions ready to run into a Neo4j database instance, as Neo4J is a schemaless system, and it is not possible to create a schema a priori. We also present an experimental evaluation that demonstrates the viability of our process in terms of performance.

NoSQL Data Modeling

2018 ◽  
pp. 94-123
Keyword(s):  
Conceptual Model ◽  
Data Modeling ◽  
Basic Structure ◽  
Object Modeling ◽  
Similar Model ◽  
Nosql Databases ◽  
Nosql Database ◽  
Er Model ◽  
Entity Relationship ◽  
Huge Challenge

The chapter discusses the necessity for data modeling in NoSQL world. The NoSQL data modeling is a huge challenge because one of the main features of NoSQL databases is that they are schema-free, that is they allow data manipulation without the need for the previous modeling or developing an entity-relationship (ER) or similar model. Although the absence of a schema can be an advantage in some situations, with the increase in the number of NoSQL database implementations, it appears that the absence of a conceptual model can be a source of substantial problems. In order to better understand the need for data modeling in NoSQL databases, first the basic structure of an ER model and an analysis of its limitations are summarized, especially regarding an application in NoSQL databases. The concept and Object modeling notation is presented as one of the possible solutions for data modeling in NoSQL databases.

Construction of Liver Fibrosis Diagnosis Ontology From Fuzzy Extended ER Modeling

2020 ◽  
Vol 12 (1) ◽  
pp. 46-69 ◽  
Author(s):  
Sara Sweidan ◽  
Hazem El-Bakry ◽  
Sahar F Sabbeh
Keyword(s):  
Liver Fibrosis ◽  
Data Model ◽  
Liver Diseases ◽  
Research Study ◽  
Semantic Knowledge ◽  
Health Records ◽  
Fuzzy Ontology ◽  
Development Methodology ◽  
Entity Relationship ◽  
System Knowledge

Liver fibrosis diagnoses is a critical and core research study field due to its importance to the patient's life. Moreover, electronic health records (EHR) contain wealthy semantics connected to liver diseases yet ontological implementation is still a challenge. Ontology however, can play critical roles in E-health as a formalization of medical terminologies and decision support system knowledge base. But since clinical data contains a lot of data that is imprecise and vague, classical approaches of ontology construction would not be fruitful. However, Fuzzy ontology, an extension of the crisp ontology that requires different development methodology, can be implemented in this field due to its previous success in modeling semantic knowledge in various domains. In this article, the authors construct a fuzzy ontology by using a fuzzy extended entity relationship (EER) data model for liver fibrosis diagnosis. The resulting ontology is complete and consistent because it is based on a formal methodology of mapping the EER model into a fuzzy ontology.

3SST Model

2010 ◽  
pp. 1-14
Author(s):  
Andreea Sabau
Keyword(s):  
Data Model ◽  
Conceptual Modeling ◽  
Domain Model ◽  
Relational Model ◽  
Temporal Data ◽  
Spatio Temporal ◽  
Entity Relationship ◽  
Temporal Objects ◽  
Database Platform ◽  
Event Based

In order to represent spatio-temporal data, many conceptual models have been designed and a part of them have been implemented. This chapter describes an approach of the conceptual modeling of spatio-temporal data, called 3SST. Also, the spatio-temporal conceptual and relational data models obtained by following the proposed phases are presented. The 3SST data model is obtained by following three steps: the construction of an entity-relationship spatio-temporal model, the specification of the domain model and the design of a class diagram which includes the objects characteristic to a spatiotemporal application and other needed elements. The relational model of the 3SST conceptual model is the implementation of the conceptual 3SST data model on a relational database platform. Both models are characterized by generality in representing spatial, temporal and spatio-temporal data. The spatial objects can be represented as points or objects with shape and the evolution of the spatio-temporal objects can be implemented as discrete or continuous in time, on time instants or time intervals. More than that, different types of spatial, temporal, spatio-temporal and event-based queries can be performed on represented data. Therefore, the proposed 3SST relational model can be considered the core of a spatio-temporal data model.

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