Suo Zhao, Meng Liu, Feng Xiong · 2025
To enhance the interoperability the portable software, interface modeling has been adopted in many open system architectures. As data modeling is still in its nascent stage of adoption, data model construction is heavily influenced by individuals creating them. There is no convention to building an unambiguous, reusable, and easy-to-read model. This paper proposes a series of best practices and design patterns based on Open Universal Data Definition Language used in Future airborne capability environment, which make the data models easier to design, reuse and understand. Furtherly, using the proposed pattern in data modeling of the onboard warning system demonstrates the feasibility of the data modeling pattern in engineering practice.
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Jyotika Singh · 2023
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David Nettleton · Elsevier eBooks · 2014
Cited by 21
Hock Chuan Chan, Chuan‐Hoo Tan, Hock‐Hai Teo · Journal of Database Management · 2014
Research on ontology and conceptual data modeling builds on the premise that the world could be represented in terms of concepts (also known as “things”), their properties and their (things') relationships. A persistent effort in the representation is how to distinguish things from their properties, as well as how to denote relationships that relate two things together. Following the tradition of deriving guidelines from ontology studies, this paper proposes a need to make a clear distinction between relationships among things and relationships among things' properties. The central thrust of this paper is in proposing an ontologically guided principle: For the same relationship, working with the relationship among things will lead to better user performance than working with the relationship among things' properties. This principle, called Relationship without Pointers principle, is robustly validated by re-analyzing a set of experiment data on user data modeling performance with three database models. This principle may be applicable to other contexts that study relationships.
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2026
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Alison Box · Apress eBooks · 2023
Data modeling may not seem glamorous or exciting, which is why many new Power BI users tend to skip right past it to dive into the more exhilarating task of creating visualizations. But the best visualizations are those that actually deliver something worthwhile, and you'll need a solid data model to get at those powerful insights. However, it still persists that people pay the least attention to designing the data model that will sit at the heart of their report. Often, it will be a case of importing tables, letting Power BI link the tables with default relationships and leaving it at that. Unfortunately, this approach can lead to the creation of problematic many-to-many bi-directional relationships that hinder accurate reporting and calculations. To achieve meaningful results, it's essential to invest time and attention in designing a robust data model that forms the core of your report.
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Shashi Shekhar, Hui Xiong · Encyclopedia of GIS · 2008
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Priyanka Tyagi · 2021
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Clare Atkins · IGI Global eBooks · 2011
Despite extensive changes in technology and methodology, anecdotal and empirical evidence (e.g., Davis et al., 1997) consistently suggests that communication and problem-solving skills are fundamental to the success of an IT professional. As two of the most valued skills in an IT graduate, they should be essential components of an effective education program, regardless of changes in student population or delivery mechanisms. While most educators would concur with this view, significantly more emphasis is generally placed on teaching the tools and techniques that students will require in their future careers, and a corresponding amount of energy is expended in attempting to identify what those tools and techniques might be. In contrast, successful problem solving is often seen either as an inherent capability that some students already possess or as a skill that some will magically acquire during the course of their studies. Data modeling as an activity, by which we mean the gathering and analysis of users’ information needs and their representation in an implementable design, is largely one of communication and problem solving and, consequently, provides an excellent opportunity for explicitly teaching these skills. Data modeling is generally considered to be one of the more difficult skills to teach (e.g., Hitchman, 1995; Pletch, 1989), particularly if the student has no previous understanding of physical data structures (de Carteret & Vidgen, 1995). The essential constructs, such as entities, attributes or objects, may be elegant in their powerful simplicity, but their combination into a useful design is a complex process of categorization in which there is “considerable room for choice and creativity in selecting the most useful classification” (Simsion, 1994 p.82). Data modeling requires not only the ability to communicate about and to solve a problem, but also to create possible solutions and then choose between them. Herein lies the difficulty. It is not enough to learn what the different constructs are, or even to study simple textbook examples of how to put them together. The student must really understand the problem, be able to create and recognize a number of possible ways in which the problem can be solved, and then exercise considerable critical skills in choosing between them. This chapter examines these issues and describes various ways in which final-year undergraduate students, taking a specialist module in data modeling, have been encouraged to develop, and have confidence in, their creative and critical ability to solve problems in a disciplined and systematic way.Request access from your librarian to read this chapter's full text.
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Wang · Figshare · 2024
modeling data
Cited by 0Open access
Philip Seamark, Thomas Martens · Apress eBooks · 2019
Even if Power BI is around for quite some time, it seems that data modeling is often neglected, meaning often reports unfold their magic just upon a single table. Sometimes tremendous effort is spent to gather data from various sources to create a single table, and then this single table is used to create appealing visuals.
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Nadja Damij, Talib Damij · Progress in IS · 2013
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Jeffrey Parsons · 2003
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SpringerReference · 2011
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Nabil R. Adam, Aryya Gangopadhyay · The Kluwer international series on advances in database systems · 1997
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Nadja Damij, Talib Damij · RePEc: Research Papers in Economics
Abstract Within the framework of process modeling, we usually identify and collect many different documents. These documents are connected to various activities of the process analyzed, either as inputs that enter and trigger the execution of the activities, or as outputs that are created as the results of performing the process’s activities.
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2012
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Zoumpolia Dikopoulou · Fuzzy management methods · 2021
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Carolyn Seaman · 1988
Data modeling, in a sense, is an attempt to bring order out of chaos. The data requirements of a system normally consist of an unstructured collection of various types of data descriptions. The aim is to produce from these an understandable, precise, and complete representation of the data requirements. The field of data modeling itself, however, is not so orderly. Even a satisfactory definition of the term is hard to find. The different data modeling methodologies seem as numerous and diverse as the situations to be modeled. Categorizing them is difficult as well. The paper will present a sampling of the data modeling methodologies currently in use and suggest some possible categorizations.
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John R. Herring · Encyclopedia of Geographic Information Science · 2008
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