Rule-Based Ontology Decision Model

2016 ◽  
pp. 271-291
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Rule Based ◽  
The Matrix ◽  
Markov Decision ◽  
Minimization Technique ◽  
Complex Decision

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

Some Selected Case Study Applications

2016 ◽  
pp. 180-270
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Labor Cost ◽  
The Matrix ◽  
Markov Decision ◽  
Decision Support Models ◽  
Minimization Technique

Having examined the modelling principles of underpinning based decision support systems applied to construction in Chapter 6, this chapter will now demonstrate their detail applications in construction practice. Specifically, 7 decision-support systems will be examined. The choices are based on the fact that data for use in the decision support models are available. The decision-support systems considered are the matrix-based used in determining labor cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique, Markov decision process and rule-based systems.

Overview of Decision Support Systems Applied to Construction

2016 ◽  
pp. 77-94
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Markov Decision Processes ◽  
Support Systems ◽  
Decision Processes ◽  
Rule Based ◽  
Markov Decision ◽  
Knowledge Intensive ◽  
The Cost ◽  
Rule Based Systems

The domain of construction is a very knowledge-intensive domain with so many factors involved. This implies undertaking any action requires an understanding of the different factors and how best to combine them to achieve a favourable and optimal outcome. Thus decision-making has been extensively used in the domain of construction. The aim of this chapter is to undertake a review of various decision support systems and to provide insights into their applications in the domain of construction. Specifically, the principle of cost index, sub-work chaining diagram method, linear regression and cost over-runs in time-overrun context (CCOTOV) model and Markov decision processes (MDP), ontology and rule-based systems have been reviewed. Based on the review the Markov decision processes (MDP), ontology and rule-based systems were chosen as the more suitable for the cost control case considered in this study.

Selected Mathematical Theories Underpinning Decision Models

2016 ◽  
pp. 95-121
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Markov Decision Processes ◽  
Support Systems ◽  
Decision Processes ◽  
Decision Models ◽  
Rule Based ◽  
Markov Decision

In Chapter 4, various decision support systems have been examined. The rational for Chapter 4 was to appraise the diiferent decision-support systems that have been used in construction without necessarily detailing the complexities and mathematical underpinnings. This chapter will provide the theory that underpins some selected decision support systems. These are regression models (RLM), artificial neural networks (ANN), Matrices, Markov decision processes (MDP) and the ontology rule-based decision support systems.

scholarly journals Inclusive Design of Earth Observation Decision Support Systems for Environmental Governance: A Case Study of Lake Nokoué

2021 ◽  
Vol 3 ◽  
Author(s):  
Ufuoma Ovienmhada ◽  
Fohla Mouftaou ◽  
Danielle Wood
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Earth Observation ◽  
Inclusive Design ◽  
Observation Data ◽  
Climate Data ◽  
Technology Design ◽  
Data Services

Earth Observation (EO) data can enhance understanding of human-environmental systems for the creation of climate data services, or Decision Support Systems (DSS), to improve monitoring, prediction and mitigation of climate harm. However, EO data is not always incorporated into the workflow for decision-makers for a multitude of reasons including awareness, accessibility and collaboration models. The purpose of this study is to demonstrate a collaborative model that addresses historical power imbalances between communities. This paper highlights a case study of a climate harm mitigation DSS collaboration between the Space Enabled Research Group at the MIT Media Lab and Green Keeper Africa (GKA), an enterprise located in Benin. GKA addresses the management of an invasive plant species that threatens ecosystem health and economic activities on Lake Nokoué. They do this through a social entrepreneurship business model that aims to advance both economic empowerment and environmental health. In demonstrating a Space Enabled-GKA collaboration model that advances GKA's business aims, this study first considers several popular service and technology design methods and offer critiques of each method in terms of their ability to address inclusivity in complex systems. These critiques lead to the selection of the Systems Architecture Framework (SAF) as the technology design method for the case study. In the remainder of the paper, the SAF is applied to the case study to demonstrate how the framework coproduces knowledge that would inform a DSS with Earth Observation data. The paper offers several practical considerations and values related to epistemology, data collection, prioritization and methodology for performing inclusive design of climate data services.

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