Defect Data Modeling and Analysis for Improving Product Quality and Productivity in Steel Industry

2018 ◽  
pp. 2233-2238 ◽  
Author(s):  
Xinmin Zhang ◽  
Manabu Kano ◽  
Masahiro Tani ◽  
Junichi Mori ◽  
Kohhei Harada
Keyword(s):  
Product Quality ◽  
Steel Industry ◽  
Data Modeling ◽  
Modeling And Analysis

Organizational Concepts and Measures for the Evaluation of Data Modeling

2001 ◽  
pp. 1-27 ◽  
Author(s):  
Ronald Maier
Keyword(s):  
Product Quality ◽  
Empirical Studies ◽  
Data Modeling ◽  
Data Models ◽  
Evaluation Procedure ◽  
Organizational Issues ◽  
Theoretical Approaches ◽  
Single Data ◽  
Using Data ◽  
Evaluation Of Data

This chapter presents a concept for the evaluation of data modeling which is based on existing theoretical approaches and three empirical studies conducted or supervised by the author. The main results of these studies with respect to evaluation suggest to extend existing approaches for the evaluation of data models. It is necessary to focus more on organizational issues of data modeling, more on process instead of product quality, to consider different application scenarios of data modeling as well as to distinguish the enterprise-wide evaluation of data modeling from the evaluation of single projects using data modeling. The evaluation concept presented here focuses on the evaluation of single data modeling projects and consists of recommendations for the evaluation procedure, persons involved, instruments, the design of important organizational dimensions as well as some concrete measures of process and product quality.

Part 4b: Application of data modeling and analysis techniques to the CO2capture process system

2012 ◽  
Vol 3 (1) ◽  
pp. 81-94 ◽  
Author(s):  
Qing Zhou ◽  
Yuxiang Wu ◽  
Christine W Chan ◽  
Paitoon Tontiwachwuthikul ◽  
Raphael O Idem ◽  
...  
Keyword(s):  
Data Modeling ◽  
Modeling And Analysis ◽  
Analysis Techniques ◽  
Process System

Research Challenges in Financial Data Modeling and Analysis

Big Data ◽  
2017 ◽  
Vol 5 (3) ◽  
pp. 177-188 ◽  
Author(s):  
Lewis Alexander ◽  
Sanjiv R. Das ◽  
Zachary Ives ◽  
H.V. Jagadish ◽  
Claire Monteleoni
Keyword(s):  
Data Modeling ◽  
Financial Data ◽  
Modeling And Analysis ◽  
Research Challenges

Concept-Oriented Query Language for Data Modeling and Analysis

2011 ◽  
pp. 85-101 ◽  
Author(s):  
Alexandr Savinov
Keyword(s):  
Partial Order ◽  
Query Language ◽  
Data Modeling ◽  
Multidimensional Analysis ◽  
Inclusion Relation ◽  
Modeling And Analysis ◽  
Domain Specific

This chapter describes a novel query language, called the concept-oriented query language (COQL), and demonstrates how it can be used for data modeling and analysis. The query language is based on a novel construct, called concept, and two relations between concepts, inclusion and partial order. Concepts generalize conventional classes and are used for describing domain-specific identities. Inclusion relation generalizes inheritance and is used for describing hierarchical address spaces. Partial order among concepts is used to define two main operations: projection and de-projection. This chapter demonstrates how these constructs are used to solve typical tasks in data modeling and analysis such as logical navigation, multidimensional analysis, and inference.

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