Use of evidential reasoning for eliciting bayesian subjective probabilities in human reliability analysis: A maritime case

Modelling the interdependencies among the factors influencing human error (e.g. the common performance conditions (CPCs) in Cognitive Reliability Error Analysis Method (CREAM)) stimulates the use of Bayesian Networks (BNs) in Human Reliability Analysis (HRA). However, subjective probability elicitation for a BN is often a daunting and complex task. To create conditional probability values for each given variable in a BN requires a high degree of knowledge and engineering effort, often from a group of domain experts. This paper presents a novel hybrid approach for incorporating the evidential reasoning (ER) approach with BNs to facilitate HRA under incomplete data. The kernel of this approach is to develop the best and the worst possible conditional subjective probabilities of the nodes representing the factors influencing HRA when using BNs in human error probability (HEP). The proposed hybrid approach is demonstrated by using CREAM to estimate HEP in the maritime area. The findings from the hybrid ER-BN model can effectively facilitate HEP analysis in specific and decision-making under uncertainty in general

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Critical Success Factors in Data Mining Projects.

The increasing awareness of data mining technology, along with the attendant increase in the capturing, warehousing, and utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the next generation of client-server enterprise information technology. The concept of data mining is gaining acceptance in business as a means of seeking higher profits and lower costs. To deploy data mining projects successfully, organizations need to know the key factors for successful data mining. Implementing emerging information systems (IS) can be risky if the critical success factors (CSFs) have been researched insufficiently or documented inadequately. While numerous studies have listed the advantages and described the data mining process, there is little research on the success factors of data mining. This dissertation identifies CSFs in data mining projects. Chapter 1 introduces the history of the data mining process and states the problems, purposes, and significances of this dissertation. Chapter 2 reviews the literature, discusses general concepts of data mining and data mining project contexts, and reviews general concepts of CSF methodologies. It also describes the identification process for the various CSFs used to develop the research framework. Chapter 3 describes the research framework and methodology, detailing how the CSFs were identified and validated from more than 1,300 articles published on data mining and related topics. The validated CSFs, organized into a research framework using 7 factors, generate the research questions and hypotheses. Chapter 4 presents analysis and results, along with the chain of evidence for each research question, the quantitative instrument and survey results. In addition, it discusses how the data were collected and analyzed to answer the research questions. Chapter 5 concludes with a summary of the findings, describing assumptions and limitations and suggesting future research

Textual data mining applications for industrial knowledge management solutions

In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Quantitative human reliability assessment in Marine Engineering Operations

Marine engineering operations rely substantially on high degrees of automation and supervisory control. This brings new opportunities as well as the threat of erroneous human actions, which account for 80-90% of marine incidents and accidents. In this respect, shipping environments are extremely vulnerable. As a result, decision makers and stakeholders have zero tolerance for accidents and environmental damage, and require high transparency on safety issues. The aim of this research is to develop a novel quantitative Human Reliability Assessment (HRA) methodology using the Cognitive Reliability and Error Analysis Method (CREAM) in the maritime industry. This work will facilitate risk assessment of human action and its applications in marine engineering operations. The CREAM model demonstrates the dynamic impact of a context on human performance reliability through Contextual Control Model controlling modes (COCOM-CMs). CREAM human action analysis can be carried out through the core functionality of a method, a classification scheme and a cognitive model. However, CREAM has exposed certain practical limitations in its applications especially in the maritime industry, including the large interval presentation of Human Failure Probability (HFP) values and the lack of organisational factors in its classification scheme. All of these limitations stimulate the development of advanced techniques in CREAM as well as illustrate the significant gap between industrial needs and academic research. To address the above need, four phases of research study are proposed. In the first phase, the adequacy of organisation, one of the key Common Performance Conditions (CPCs) in CREAM, is expanded by identifying the associated Performance Influencing Factors (PIFs) and sub-PIFs in a Bayesian Network (BN) for realising the rational quantification of its assessment. In the second phase, the uncertainty treatment methods' BN, Fuzzy Rule Base (FRB) , Fuzzy Set (FS) theory are used to develop new models and techniques' that enable users to quantify HFP and facilitate the identification of possible initiating events or root causes of erroneous human action in marine engineering operations. In the third phase, the uncertainty treatment method's Evidential Reasoning (ER) is used in correlation with the second phase's developed new models and techniques to produce the solutions to conducting quantitative HRA in conditions in which data is unavailable, incomplete or ill-defined. In the fourth phase, the CREAM's prospective assessment and retrospective analysis models are integrated by using the established Multiple Criteria Decision Making (MCDM) method based on, the combination of Analytical Hierarchical Process (AHP), entropy analysis and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). These enable Decision Makers (DMs) to select the best developed Risk Control Option (RCO) in reducing HFP values. The developed methodology addresses human actions in marine engineering operations with the significant potential of reducing HFP, promoting safety culture and facilitating the current Safety Management System (SMS) and maritime regulative frameworks. Consequently, the resilience of marine engineering operations can be further strengthened and appreciated by industrial stakeholders through addressing the requirements of more safety management attention at all levels. Finally, several real case studies are investigated to show end users tangible benefits of the developed models, such as the reduction of the HFPs and optimisation of risk control resources, while validating the algorithms, models, and methods developed in this thesis