Situation awareness approach to context-aware case-based decision support.

Context-aware case-based decision support systems (CACBDSS) use the context of users as one of the features for similarity assessment to provide solutions to problems. The combination of a context-aware case-based reasoning (CBR) with general domain knowledge has been shown to improve similarity assessment, solving domain specific problems and problems of uncertain knowledge. Whilst these CBR approaches in context awareness address problems of incomplete data and domain specific problems, future problems that are situation-dependent cannot be anticipated due to lack of data by the CACBDSS to make predictions. Future problems can be predicted through situation awareness (SA), a psychological concept of knowing what is happening around you in order to know the future. The work conducted in this thesis explores the incorporation of SA to CACBDSS. It develops a framework to decouple the interface and underlying data model using an iterative research and design methodology. Two new approaches of using situation awareness to enhance CACBDSS are presented: (1) situation awareness as a problem identification component of CACBDSS (2) situation awareness for both problem identification and solving in CACBDSS. The first approach comprises of two distinct parts; SA, and CBR parts. The SA part understands the problem by using rules to interpret cues from the environment and users. The CBR part uses the knowledge from the SA part to provide solutions. The second approach is a fusion of the two technologies into a single case-based situation awareness (CBSA) model for situation awareness based on experience rather than rule, and problem solving predictions. The CBSA system perceives the users context and the environment and uses them to understand the current situation by retrieving similar past situations. The futures of new situations are predicted through knowledge of the history of similar past situations. Implementation of the two approaches in flow assurance control domain to predict the formation of hydrate shows improvements in both similarity assessment and problem solving predictions compared to CACBDSS without SA. Specifically, the second approach provides an improved decision support in scenarios where there are experienced situations. In the absence of experienced situations, the second approach offers more reliable solutions because of its rule-based capability. The adaptation of the user interface of the approaches to the current situation and the presentation of a reusable sequence of tasks in the situation reduces memory loads on operators. The integrated research-design methodology used in realising these approaches links theory and practice, thinking and doing, achieving practical as well as research objectives. The action research with practitioners provided the understanding of the domain activities, the social settings, resources, and goals of users. The user-centered design process ensures an understanding of the users. The agile development model ensures an iterative work, enables faster development of a functional prototype, which are more easily communicated and tested, thus giving better input for the next iteration

Modelling Case-Based Reasoning in Situation-Aware Disaster Management

Situation-aware disaster management (SADIM) is a continuous decision-making and action taking process. This process requires prior knowledge of the ongoing environment and context. SADIM comprises two major processes: situation awareness (SA) - a cognitive process that assesses current situations and anticipate future situations in the environment; and, disaster management (DM) which is a decision-making process preventing, preparing, responding, and recovering for and from a disaster. One of the decision-making technologies used in current SADIM is case-based reasoning (CBR) CBR is used for the disaster management element only. Situation awareness process in current SADIM is carried out using domain rules, statistical reasoning and other methods. This paper therefore presents a method of using CBR to carry out both situation assessment and disaster management decision-making processes in SADIM, building on previous work focusing in SA alone. Using CBR for both processes provides the capability of using past experiences to understand the state of the environment and also solve specific disaster problems. The paper evaluates the method through implementation in disaster prevention in the petroleum drilling domain for early kick detection to prevent a blowout disaster. The results show a clear improvement in similarity assessment and problem solving prediction to prevent blowout

Time-Critical Decision Making in Banking Transaction: Using Bayesian Algorithm

This research paper treat the basic perception of time-critical decision making in Banking transaction for bank customers, as it create an awareness approach to ease time spent in performing day-to-day transaction at the bank that may involve either withdrawal with or without the use ATM or deposit as the case may be. Most a time at the bank, people spent a lot of time in trying to either withdraw money or deposit money at the various bank branches all over Nigeria. This is as a result of lack of proper time-critical decision support for people to understand what is happening at bank branches. Waste of man hour at the bank may arise due to location of the banks, day to day banking transaction involving withdrawal or deposit done on Friday. This paper is intended to create a time-critical decision support in the banking transaction to assist individual or groups to understand and be aware of certain situations that will assist banking transaction in any of the bank branches in Nigeria. It uses the Bayesian algorithm which is implemented in MATLAB version 7.7.0 to determine the bank branch to carry out transaction. Choice of location for transaction is dependence on the highest posterior probability calculated for a given predictor in predicting the situation and a graph, showing the plotted points.

Building a Decision Support System for Crude Oil Price Prediction using Bayesian Networks

Decision Support Systems are computer based systems that are aimed at assisting decision-makers in taking productive, agile, innovative and reputable decisions. This work presents a Decision Support System using Bayesian Network to predict crude oil price .Bayesian Network technology and its application in predicting crude oil price is presented. Price data obtained from the Central Bank of Nigeria was classed into High and Low cases to denote the upward and downward price movement in which information was revealed. The input data were used in this model to train the network and to validate its generalization ability in other to deliver the best prediction forecast. A linguistic prediction model which utilized the Bayesian Network whose aim was to integrate linguistic information into a quantitative prediction model was established. The results obtained from the linguistic model demonstrate that linguistic information adds value to oil price prediction

Steam Package Boiler Expert System for Control and Maintenance of Fertilizer Plants using Rule-Base Fuzzy Logic

Generally, expert systems have been found very useful and even in fertilizer plants it has been deployed in handling operations in critical sections, such as material handling systems, online detection systems, granulation, air compressor among others. This paper presents research work for steam package boiler expert system for control and maintenance of fertilizer plants using rule-base fuzzy logic hybrid system, which has not been benefited much from expert system. The system handles cause of boiler failures in terms of controlling and maintaining the functional chemical components of the boiler drum and feed water parameters. validation on the system consistency, correctness, and its precision with six (6) steam package boiler parameters test value cases was conducted involving fourteen (14) fertilizer plant boiler domain partitioners. The boiler drum and feed water qualities with less or higher test value worst-cases validates the boiler system, showing each of the parameters bar turns red, as displayed on the boilers panel, while on test value best-cases, validates the system, displaying green on the boilers panel bar as users entered the right value of parameters as design specification. The expert system prevents damaged and malfunctioning as control the alkalinity, prevent scaling, both mechanica

Situation awareness in context-aware case-based decision support.

Humans naturally reuse recalled knowledge to solve problems and this includes understanding the context i.e. the information that identifies or characterizes these problems. For problems in complex and dynamic environments, providing effective solutions by operators requires their understanding of the situation of the environment together with the context. Context-aware case-based reasoning (CBR) applications uses the context of users to provide solutions to problems. The combination of a context-aware CBR with general domain knowledge has been shown to improve similarity assessment, solving domain specific problems and problems of uncertain knowledge. Whilst these CBR approaches in context awareness address problems of incomplete data and domain specific problems, future problems that are situation-dependent cannot be anticipated due to lack of the facility to predict the state of the environment. This paper builds on prior work to present an approach that combines situation awareness, context awareness, case-based reasoning, and general domain knowledge in a decision support system. In combining these concepts the architecture of this system provides the capability to handle uncertain knowledge and predict the state of the environment in order to solve specific domain problems. The paper evaluates the concepts through a trial implementation in the flow assurance control domain to predict the formation of hydrate in sub-sea oil and gas pipelines. The results show a clear improvement in both similarity assessment and problem solving prediction

Case-based situation awareness.

Situation-aware case-based decision support (SACBDS) systems comprise two distinct parts: situation awareness (SA) and case-based reasoning (CBR). The SA part keeps a finite history of the time space information of the domain and uses rules to interpret cues from the environment with respect to an individual user's context, and then anticipates future situations by performing statistical inference over historical data. The CBR part is the part that seeks to accomplish a particular task with knowledge of the environment from the SA component. This paper discusses the fusion of the CBR model and the SA model into a case-based situation awareness (CBSA) model for situation awareness based on experience rather than rule, similarity assessment and problem solving prediction. The CBSA system perceives the users' context and the environment and uses them to understand the current situation by retrieving similar past situations. Every past situation has a history. The future of a new situation (case) is predicted through knowledge of the history of a similar past situation. The paper evaluates the concept in the flow assurance control domain to predict the formation of hydrate in sub-sea oil and gas pipelines. The results provided the CBSA system with greater number of accurate predictions than the SACBDS system

User interface design for situation-aware decision support systems.

Information recall about general situations incurs memory and cognitive loads on operators. Recognition of information for specific situations identified with users context and the state of the world is helpful to operators in performing tasks in complex environments. The emergence of ubiquitous, ambient, and pervasive technologies is increasingly providing methods to help operators to perform their tasks in smart and intelligent ways. Existing user interface design does not solve the problem of drawing together the information required for situation-aware decision support systems in a way that minimises cognitive load. This paper discusses a framework for user interface design of situation-aware systems that exploit inputs from users and the environment to provide information tailored to the users tasks in specific situations. The user interface can reconfigure automatically in order to adapt to the current situation. The adaptation of the user interface to the current situation and the presentation of a reusable sequence of tasks in the situation reduces memory loads on operators. Hierarchical Task Analysis (HTA) is used to describe tasks for various types of situations. HTA is supplemented with scenarios to stimulate design ideas and requirements analysis is used to represent interrelationships between tasks