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2 edition of Representing and reasoning about quality using enterprise models. found in the catalog.

Representing and reasoning about quality using enterprise models.

Henry M. Kim

Representing and reasoning about quality using enterprise models.

by Henry M. Kim

  • 337 Want to read
  • 21 Currently reading

Published .
Written in English


The Physical Object
Pagination1 v. (in various folialtions).
ID Numbers
Open LibraryOL19988782M
ISBN 100612411893

The second major of the book provides a detailed exploration of the applicability of one particular logical framework, Probabilistic Logic Networks, to real-world reasoning problems. This part is different from the previous ones, in that it comprises primarily original work, rather than literature survey and summary. The quality of our thinking is proportional to the models in our head and their usefulness in the situation at hand. The more models you have—the bigger your toolbox—the more likely you are to have the right models to see reality. It turns out that when it comes to improving your ability to .

Reasoning over BIM models can use current business 1 We use the terms “enterprise model” and “business model” in a conceptual modeling sense, i.e., a collection of elements and relationships typically having a graphical representation, and not in. Model-based enterprise (MBE) is a term used in manufacturing, to describe a strategy where an annotated digital three-dimensional (3D) model of a product serves as the authoritative information source for all activities in that product's lifecycle.. A key advantage of MBE is that it replaces digital drawings. In MBE, a single 3D model contains all the information typically found on in an.

Reasoning with Models. Harvard Computer Science Group Technical Report TR Abstract We develop a model-based approach to reasoning, in which the knowledge base is represented as a set of models (satisfying assignments) rather then a logical formula, and the set of queries is restricted. We show that for every propositional knowledge base. What are Ontologies? An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. To enable such a description, we need to formally specify components such as individuals (instances of objects), classes, attributes and relations as well as restrictions, rules and axioms.


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Representing and reasoning about quality using enterprise models by Henry M. Kim Download PDF EPUB FB2

PDF | On Jan 1,Henry M. Kim and others published Representing and Reasoning about Quality using Enterprise Models | Find, read and cite all the research you need on ResearchGate.

Representing and Reasoning About Quality Using Enterprise Models By Henry M. Kim Enterprise Integration Laboratory Faculty of Applied Sciences and Engineering University of Toronto 4 Taddle Creek Road Toronto, Ontario M5S 3G9 Canada A Thesis submitted in conformity with the requirements for the Degree of Doctor of Philosophy.

Kim, Henry M., “Representing and Reasoning about Quality using Enterprise Models”, Ph.D. Thesis, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario Canada M5S 3G9 (). Google ScholarCited by: The TOVE Quality Ontology-VB is the formal representation (using First-order Logic) of terms, relationships, attributes, and axioms about quality which are generic beyond any specific quality domain.

3 Are enterprise information portals making executive information systems 'Representing and reasoning about quality using enterprise models' Asked in Books and Literature, Definitions. Strategic reasoning about business models is an integral part of service design.

In fast moving markets, businesses must be able to recognize and respond strategically to disruptive change. They have to answer questions such as: what are the threats and opportunities in emerging technologies and innovations.

How should they target customer groups?Cited by: Len Silverston's book on Universal Data Models, "The Data Model Resource Book: Volume 1" is generally well done, and important. The text presents numerous Universal Data Models that can be employed in the design and development of logical data models in support of relational database designs in various industries/5.

practices that educate children about the nature and functions of models. We have identified four forms of models and related modeling practices that show promise for developing model-based reasoning (Lehrer, Schauble, & Penner, ). FORMS OF MODELING Physical Models Physical models, like models of solar systems or elbows, are microcosms of.

CAUSALITY: MODELS, REASONING, AND INFERENCE by Judea Pearl Cambridge University Press, REEVVVIIIEEEWWWEEEDDDB BBYY LEELLLAAANNNDD GEERRRSSSOOONN NEEUUUBBBEEERRRGG Boston University This book seeks to integrate research on cause and effect inference from cog-nitive science, econometrics, epidemiology, philosophy, and statistics+ It puts File Size: KB.

In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. With this approach, the main focus of application development is developing the model.

Then at run time, an "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction. r/programming: Computer Programming. Press J to jump to the feed. Press question mark to learn the rest of the keyboard shortcuts.

Professional reasoning ‐ the emerging term. Broadens the concept to include supervisory, managerial, and educational perspective related toFile Size: KB.

models from traditional Entity Relationship models. This can be used to design data warehouses and data marts based on enterprise data models. The first step of the method involves classifying entities in the data model into a number of catego-ries. The second step involves identifying hierarchies that exist in.

software models as external representations that afford reasoning. Peirce proposes a specific relationship between representations and reasoning, encapsulated in his notion of logic as semiotic. Peirce (b) argues that “logic is the art of reasoning” (p.

11) and that reasoning is the process “to. Software Quality Estimation using Machine Learning: Case-based Reasoning Technique Ekbal Rashid Department of CS & E SOA University, Bhubaneshwar, Orissa Srikanta Patnaik Department of CS & E SOA University, Bhubaneshwar, Orissa Vandana Bhattacherjee Department of CS & E, Birla Institute of Technology, Ranchi, Jharkhand ABSTRACT Software.

Using Enterprise Architecture Models for System Quality Analysis Per Närman, Marten Schönherr, Pontus Johnson, Mathias Ekstedt, Moustafa Chenine Since creating Enterprise Architecture models is expensive and without intrinsic value, it is and attribute causal relations in representing the nodes and relations of a Bayesian network [6.

Quality, control, flexibility and cost effectiveness in the MBE environment requires documented standards, information automation and metrics built into the product development process. ITI provides standards based interoperability software tools and expertise to maximize reuse of digital product data in the model based enterprise.

conclusions, to document reasoning, and to supply adequate supporting evidence, this activ-ity provides one of the best examples of “real life” logical reasoning outside of mathematics 1Re-published by the United States Central Intelligence Agency inthis book is currently available only in download form from the CIA’s website.

2Cited by: 6. Compared to other models, for example Trace, what are the advantages and disadvantages of Phast. Phast has many features that make it unique when compared to other consequence analysis models.

For example, Phast includes: A proprietary dispersion model called the UDM that is suited to handle all type of releases – vapour,File Size: KB. Representing and reasoning over a taxonomy of part–whole relations Representing and reasoning over a taxonomy of part–whole relations Keet, C.

Maria ; Artale, Alessandro Many types of partâ whole relations have been proposed in the literature to aid the conceptual modeller to choose the most appropriate type, but many of those relations lack a formal speciï¬ cation. – Reasoning from design models – Reasoning from first principles – Deep reasoning – Lecture 15 4.

Why Model Based Diagnosis • Familiar task that people do well • Compared to heuristic classification – Don’t need new rule set needed for each deviceFile Size: KB.REVIEW: MODEL BASED REASONING CT Model-Based Reasoning (MBR) is a relatively new reasoning technique in AI and Expert Systems field of Computer Science.

It’s main difference from the other reasoning methodologies is the use of models in the knowledge representation and inferencing through these models (probably multiple) while File Size: 90KB.Daniele Chiffi and Renzo Zanotti note that, while models and reasoning about diagnoses have been extensively investigated, prognosis remains a relatively neglected topic in the literature on the foundations of clinical reasoning.

13 They argue that, while both prognosis and diagnosis involve prediction, only the former involves “creative Cited by: 4.