Fuzzy ontology for knowledge mobilisation and decision support

A growing concern for organisations is how they should deal with increasing amounts of collected data. With fierce competition and smaller margins, organisations that are able to fully realize the potential in the data they collect can gain an advantage over the competitors. It is almost impossible to avoid imprecision when processing large amounts of data. Still, many of the available information systems are not capable of handling imprecise data, even though it can offer various advantages. Expert knowledge stored as linguistic expressions is a good example of imprecise but valuable data, i.e. data that is hard to exactly pinpoint to a definitive value. There is an obvious concern among organisations on how this problem should be handled; finding new methods for processing and storing imprecise data are therefore a key issue. Additionally, it is equally important to show that tacit knowledge and imprecise data can be used with success, which encourages organisations to analyse their imprecise data. The objective of the research conducted was therefore to explore how fuzzy ontologies could facilitate the exploitation and mobilisation of tacit knowledge and imprecise data in organisational and operational decision making processes. The thesis introduces both practical and theoretical advances on how fuzzy logic, ontologies (fuzzy ontologies) and OWA operators can be utilized for different decision making problems. It is demonstrated how a fuzzy ontology can model tacit knowledge which was collected from wine connoisseurs. The approach can be generalised and applied also to other practically important problems, such as intrusion detection. Additionally, a fuzzy ontology is applied in a novel consensus model for group decision making. By combining the fuzzy ontology with Semantic Web affiliated techniques novel applications have been designed. These applications show how the mobilisation of knowledge can successfully utilize also imprecise data. An important part of decision making processes is undeniably aggregation, which in combination with a fuzzy ontology provides a promising basis for demonstrating the benefits that one can retrieve from handling imprecise data. The new aggregation operators defined in the thesis often provide new possibilities to handle imprecision and expert opinions. This is demonstrated through both theoretical examples and practical implementations. This thesis shows the benefits of utilizing all the available data one possess, including imprecise data. By combining the concept of fuzzy ontology with the Semantic Web movement, it aspires to show the corporate world and industry the benefits of embracing fuzzy ontologies and imprecision.

Designing a Bit Preservation System

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Nurses’ Acceptance of an Internet-Based Support System in The Care of Adolescents with Depression

The overall goal of the study was to describe nurses’ acceptance of an Internet-based support system in the care of adolescents with depression. The data were collected in four phases during the period 2006 – 2010 from nurses working in adolescent psychiatric outpatient clinics and from professionals working with adolescents in basic public services. In the first phase, the nurses’ anticipated perceptions of the usefulness of the Internet-based support system before its implementation was explored. In the second phase, the nurses’ perceived ease of computer and Internet use and attitudes toward it were explored. In the third phase, the features of the support system and its implementation process were described. In the fourth phase, the nurses’ experiences of behavioural intention and actual system use of the Internet-based support were described in psychiatric out-patient care after one year use. The Technology Acceptance Model (TAM) was used to structure the various research phases. Several benefits were identified from the nurses’ perspective in using the Internet-based support system in the care of adolescents with depression. The nurses’ technology skills were good and their attitudes towards computer use were positive. The support system was developed in various phases to meet the adolescents’ needs. Before the implementation of the information technology (IT)-based support system, it is important to pay attention to the nurses’ IT-training, technology support, resources, and safety as well as ethical issues related to the support system. After one year of using the system, the nurses perceived the Internet-based support system to be useful in the care of adolescents with depression. The adolescents’ independent work with the support system at home and the program’s systematic character were experienced as conducive from the point of view of the treatment. However, the Internet-based support system was integrated only partly into the nurseadolescent interaction even though the nurses’ perceptions of it were positive. The use of the IT-based system as part of the adolescents’ depression care was seen positively and its benefits were recognized. This serves as a good basis for future IT-based techniques. Successful implementations of IT-based support systems need a systematic implementation plan and commitment from the part of the organization and its managers. Supporting and evaluating the implementation of an IT-based system should pay attention to changing the nurses’ work styles. Health care organizations should be offered more flexible opportunities to utilize IT-based systems in direct patient care in the future.

Improving reporting management with relational database management system

Data management consists of collecting, storing, and processing the data into the format which provides value-adding information for decision-making process. The development of data management has enabled of designing increasingly effective database management systems to support business needs. Therefore as well as advanced systems are designed for reporting purposes, also operational systems allow reporting and data analyzing. The used research method in the theory part is qualitative research and the research type in the empirical part is case study. Objective of this paper is to examine database management system requirements from reporting managements and data managements perspectives. In the theory part these requirements are identified and the appropriateness of the relational data model is evaluated. In addition key performance indicators applied to the operational monitoring of production are studied. The study has revealed that the appropriate operational key performance indicators of production takes into account time, quality, flexibility and cost aspects. Especially manufacturing efficiency has been highlighted. In this paper, reporting management is defined as a continuous monitoring of given performance measures. According to the literature review, the data management tool should cover performance, usability, reliability, scalability, and data privacy aspects in order to fulfill reporting managements demands. A framework is created for the system development phase based on requirements, and is used in the empirical part of the thesis where such a system is designed and created for reporting management purposes for a company which operates in the manufacturing industry. Relational data modeling and database architectures are utilized when the system is built for relational database platform.

Evaluating land-use related environmental impacts of biomass value chains for decision-support : Comparison and testing of methodologies proposed for environmental life cycle assessment

Life cycle assessment (LCA) is one of the most established quantitative tools for environmental impact assessment of products. To be able to provide support to environmentally-aware decision makers on environmental impacts of biomass value-chains, the scope of LCA methodology needs to be augmented to cover landuse related environmental impacts. This dissertation focuses on analysing and discussing potential impact assessment methods, conceptual models and environmental indicators that have been proposed to be implemented into the LCA framework for impacts of land use. The applicability of proposed indicators and impact assessment frameworks is tested from practitioners' perspective, especially focusing on forest biomass value chains. The impacts of land use on biodiversity, resource depletion, climate change and other ecosystem services is analysed and discussed and the interplay in between value choices in LCA modelling and the decision-making situations to be supported is critically discussed. It was found out that land use impact indicators are necessary in LCA in highlighting differences in impacts from distinct land use classes. However, many open questions remain on certainty of highlighting actual impacts of land use, especially regarding impacts of managed forest land use on biodiversity and ecosystem services such as water regulation and purification. The climate impact of energy use of boreal stemwood was found to be higher in the short term and lower in the long-term in comparison with fossil fuels that emit identical amount of CO2 in combustion, due to changes implied to forest C stocks. The climate impacts of energy use of boreal stemwood were found to be higher than the previous estimates suggest on forest residues and stumps. The product lifetime was found to have much higher influence on the climate impacts of woodbased value chains than the origin of stemwood either from thinnings or final fellings. Climate neutrality seems to be likely only in the case when almost all the carbon of harvested wood is stored in long-lived wooden products. In the current form, the land use impacts cannot be modelled with a high degree of certainty nor communicated with adequate level of clarity to decision makers. The academia needs to keep on improving the modelling framework, and more importantly, clearly communicate to decision-makers the limited certainty on whether land-use intensive activities can help in meeting the strict mitigation targets we are globally facing.

A Web-Based Environmental Toolkit to Support Small and Medium-Sized Enterprises in the Implementation of Their Own Environmental Management System

With small and medium sized-enterprises (SMEs) taking up the majority of the global businesses, it is important they act in an environmentally responsible manner. Environmental management systems (EMS) help companies evaluate and improve their environmental impact but they often require human, financial, and temporary resources that not all SMEs can provide. This research encompasses interviews with representatives of two small enterprises in Germany to provide insights into their understanding, and knowledge of an EMS and how they perceive their responsibility towards the environment. Furthermore, it presents a toolkit created especially for small and medium-sized enterprises that serves as a simplified version of an EMS based on the ISO 14001 standard and is evaluated by the representatives of the SMEs. Some of the findings are: while being open to the idea of improving their environmental impact, SMEs do not always feel it is their responsibility to do so; they seem to lack the means to fully implement an EMS. The developed toolkit is considered useful and usable and recommendations are drawn for its future enhancement.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

Model of Big Data Failure: Review of Information System Failure

In the new age of information technology, big data has grown to be the prominent phenomena. As information technology evolves, organizations have begun to adopt big data and apply it as a tool throughout their decision-making processes. Research on big data has grown in the past years however mainly from a technical stance and there is a void in business related cases. This thesis fills the gap in the research by addressing big data challenges and failure cases. The Technology-Organization-Environment framework was applied to carry out a literature review on trends in Business Intelligence and Knowledge management information system failures. A review of extant literature was carried out using a collection of leading information system journals. Academic papers and articles on big data, Business Intelligence, Decision Support Systems, and Knowledge Management systems were studied from both failure and success aspects in order to build a model for big data failure. I continue and delineate the contribution of the Information System failure literature as it is the principal dynamics behind technology-organization-environment framework. The gathered literature was then categorised and a failure model was developed from the identified critical failure points. The failure constructs were further categorized, defined, and tabulated into a contextual diagram. The developed model and table were designed to act as comprehensive starting point and as general guidance for academics, CIOs or other system stakeholders to facilitate decision-making in big data adoption process by measuring the effect of technological, organizational, and environmental variables with perceived benefits, dissatisfaction and discontinued use.

The Influence of Arousal on Moral Decision-making for Individuals with and without Mild Head Injury

Recent research has shown that University students with a history of self-reported mild head injury (MHI) are more willing to endorse moral transgressions associated with personal, relative to impersonal, dilemmas (Chiappetta & Good, 2008). However, the terms 'personal' and 'impersonal' in these dilemmas have functionally confounded the 'intentionality' of the transgression with the 'personal impact' or 'outcome' of the transgression. In this study we used a modified version of these moral dilemmas to investigate decision-making and sympathetic nervous system responsivity. Forty-eight University students (24 with MHI, 24 with no-MHI) read 24 scenarios depicting moral dilemmas varying as a function of 'intentionality' of the act (deliberate or unintentional) and its 'outcome' (physical harm, no physical harm, non-moral) and were required to rate their willingness to engage in the act. Physiological indices of arousal (e.g., heart rate - HR) were recorded throughout. Additionally, participants completed several neurocognitive tests. Results indicated significantly lowered HR activity at baseline, prior to, and during (but not after) making a decision for each type of dilemma for participants with MHI compared to their non-injured cohort. Further, they were more likely than their cohort to authorize personal injuries that were deliberately induced. MHI history was also associated with better performance on tasks of cognitive flexibility and attention; while students' complaints of postconcussive symptoms and their social problem solving abilities did not differ as a function of MHI history. The results provide subtle support for the hypothesis that both emotional and cognitive information guide moral decision making in ambiguous and emotionally distressing situations. Persons with even a MHI have diminished physiological arousal that may reflect disruption to the neural pathways of the VMPFC/OFC similar to those with more severe injuries.

Study of baleen whales’ ecology and interaction with maritime traffic activities to support management of a complex socio-ecological system

Thesis written in co-mentorship with Robert Michaud.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

A model of Distributed Ayurvedic Diagnosis and Therapy System for Hridroga Using Agents

Diagnosis of Hridroga (cardiac disorders) in Ayurveda requires the combination of many different types of data, including personal details, patient symptoms, patient histories, general examination results, Ashtavidha pareeksha results etc. Computer-assisted decision support systems must be able to combine these data types into a seamless system. Intelligent agents, an approach that has been used chiefly in business applications, is used in medical diagnosis in this case. This paper is about a multi-agent system named “Distributed Ayurvedic Diagnosis and Therapy System for Hridroga using Agents” (DADTSHUA). It describes the architecture of the DADTSHUA model .This system is using mobile agents and ontology for passing data through the network. Due to this, transport delay can be minimized. It is a system which will be very helpful for the beginning physicians to eliminate his ambiguity in diagnosis and therapy. The system is implemented using Java Agent DEvelopment framework (JADE), which is a java-complaint mobile agent platform from TILab.

SIDUR: development of an information system, using controlled vocabulary designed to the characterization and sample of population with and without disability, attended by Rehabilitation programs, University of Rosario

Introduction: the statistical record used in the Field Academic Programs (PAC for it’s initials in Spanish) of Rehabilitation denotes generalities in the data conceptualization, which complicates the reliable guidance in making decisions and provides a low support for research in rehabilitation and disability. In response, the Research Group in Rehabilitation and Social Integration of Persons with Disabilities has worked on the creation of a registry to characterize the population seen by Rehabilitation PAC. This registry includes the use of the International Classification of Functioning, Disability and Health (ICF) of the WHO. Methodology: the proposed methodology includes two phases: the first one is a descriptive study and the second one involves performing methodology Methontology, which integrates the identification and development of ontology knowledge. This article contextualizes the progress made in the second phase. Results: the development of the registry in 2008, as an information system, included documentary review and the analysis of possible use scenarios to help guide the design and development of the SIDUR system. The system uses the ICF given that it is a terminology standardization that allows the reduction of ambiguity and that makes easier the transformation of health facts into data translatable to information systems. The record raises three categories and a total of 129 variables Conclusions: SIDUR facilitates accessibility to accurate and updated information, useful for decision making and research.