Data mining for decision support with uncertainty on the airplane

This study describes the formalization of the medical decision-making process under uncertainty underpinned by conditional preferences, the theory of evidence and the exploitation of high-utility patterns in data mining. To assist a decision maker, the medical process (clinical pathway) was implemented using a Conditional Preferences Base (CPB). Then for knowledge engineering, a Dempster-Shafer ontology integrating uncertainty underpinned by evidence theory was built. Beliefs from different sources are established with the use of data mining. The result is recorded in an In-flight Electronic Health Records (IEHR). The IEHR contains evidential items corresponding to the variables determining the management of medical incidents. Finally, to manage tolerance to uncertainty, a belief fusion algorithm was developed. There is an inherent risk in the practice of medicine that can affect the conditions of medical activities (diagnostic or therapeutic purposes). The management of uncertainty is also an integral part of decision-making processes in the medical field. Different models of medical decisions under uncertainty have been proposed. Much of the current literature on these models pays particular attention to health economics inspired by how to manage uncertainty in economic decisions. However, these models fail to consider the purely medical aspect of the decision that always remains poorly characterized. Besides, the models achieving interesting decision outcomes are those considering the patient's health variable and other variables such as the costs associated with the care services. These models are aimed at defining health policy (health economics) without a deep consideration for the uncertainty surrounding the medical practices and associated technologies. Our approach is to integrate the management of uncertainty into clinical reasoning models such as Clinical Pathway and to exploit the relationships between the determinants of incident management using data mining tools. To this end, how healthcare professionals see and conceive uncertainty has been investigated. This allowed for the identification of the characteristics determining people under uncertainty and to understand the different forms and representations of uncertainty. Furthermore, what an in-flight medical incident is and how its management is a decision under uncertainty issues was defined. This is the first phase of common data mining that will provide an evidential transaction basis. Subsequently an evidential and ontological rea-soning to manage this uncertainty has been established in order to support decision making processes on the airplane

Linking business analytics to decision making effectiveness: a path model analysis

While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DM

New trends in data mining.

Trends; Data; Data mining;

Impact of the spatial context on human communication activity

Technology development produces terabytes of data generated by hu- man activity in space and time. This enormous amount of data often called big data becomes crucial for delivering new insights to decision makers. It contains behavioral information on different types of human activity influenced by many external factors such as geographic infor- mation and weather forecast. Early recognition and prediction of those human behaviors are of great importance in many societal applications like health-care, risk management and urban planning, etc. In this pa- per, we investigate relevant geographical areas based on their categories of human activities (i.e., working and shopping) which identified from ge- ographic information (i.e., Openstreetmap). We use spectral clustering followed by k-means clustering algorithm based on TF/IDF cosine simi- larity metric. We evaluate the quality of those observed clusters with the use of silhouette coefficients which are estimated based on the similari- ties of the mobile communication activity temporal patterns. The area clusters are further used to explain typical or exceptional communication activities. We demonstrate the study using a real dataset containing 1 million Call Detailed Records. This type of analysis and its application are important for analyzing the dependency of human behaviors from the external factors and hidden relationships and unknown correlations and other useful information that can support decision-making.Comment: 12 pages, 11 figure

Applications of advanced transport aircraft in developing countries

Four representative market scenarios were studied to evaluate the relative performance of air-and surface-based transportation systems in meeting the needs of two developing contries, Brazil and Indonesia, which were selected for detailed case studies. The market scenarios were: remote mining, low-density transport, tropical forestry, and large cargo aircraft serving processing centers in resource-rich, remote areas. The long-term potential of various aircraft types, together with fleet requirements and necessary technology advances, is determined for each application