Enhancing Data Management Support for Case-based Reasoning Systems

Case-based reasoning offers a novel approach to develop knowledge based systems. A case-based system (CBS) stores problem solving expertise as cases in its casebase. A case captures a problem description and the description of a solution to the problem. A CBS solves a problem by starting with an approximate solution found in a case in its casebase. When presented with a problem, a CBS analyzes it to extract salient features relevant for problem solving. It searches the casebase to identify cases with similar features. All such cases are retrieved and compared with the problem to select the best matching case. The solution in the best case is adapted to develop a solution to the problem. The proposed solution is evaluated. A new case is formed by combining the problem with the proposed solution. This case, if found suitable, is stored in the casebase. A CBS, thus, augments its casebase with new cases as it solves new problems. Case-based reasoning has been used in a wide range of application domains to develop problem solving and advisory systems. A limitation of these systems is that they lack adequate data management support for casebases. Most current CBS are small memory-resident systems. They use small casebases, which are loaded into primary memory during processing. This limits the size of the casebase and restricts the scope of the CBS. Since a CBS develops a solution by starting with an approximate solution from its casebase, its problem solving ability depends to a great extent on the variety and number of cases available in its casebase. It is more likely to find a closely matching case for a given problem in a large casebase compared to that in a smaller casebase. A CBS, therefore, needs a large casebase to operate at an acceptable level of expertise. As a CBS solves new problems, it adds new cases to its casebase. Thus the casebase keeps growing with the daily use of the system. A major research issue confronting CBS research is how to create large systems that can handle large casebases comprising hundreds and thousands of cases (Kolodner 1993). Our research addresses this important issue of providing data management support to large casebases

Current Knowledge Management in Manual Assembly – Further Development by the Analytical Hierarchy Process, Incentive and Cognitive Assistance Systems

The complexity of manual assembly is continuously increasing due to a large variety of products, multi-product assembly or a batch size of one. To stay ahead in competency and competition, and to ensure adaptability and flexibility in today’s dynamic production environment, awareness of knowledge as the 4th factor of production, as well as the effective management of knowledge, are crucial. The present research therefore aimed at further advancing knowledge management in manual assembly by (1) assessing cognitive assistance systems and organisational incentive systems by use of an online survey distributed to German production companies, and by (2) applying the Analytical Hierarchy Process (AHP) as a transparent decision-making tool for knowledge-based improvements in the manual assembly process and workplace design. By employing an exemplary case of two feasible assembly alternatives, the AHP was applied as a method of knowledge measurement in a specific use case revealing priorities for knowledge-based ideas. To properly compute a final priority ranking of workers’ knowledge ideas, an algorithm written in Python programming language in accordance with the problem-solving framework previously published by Thomas L. Saaty (Decision Sciences, 18: 157-177, 1987). The performance of the algorithm shows that the rating process can be standardised and automated to a high level, and that the AHP may thus provide supportive evidence for assembly optimisation. The AHP-derived results can be used as a suitable basis for a bonus-point incentive system, which should contain both material and immaterial incentives. To operationalise this, it is therefore recommended to integrate the AHP rating process into a knowledge management application of hand-held devices, such as tablets, which are widely used in the production environment

The Effect of Domain and Technical Expertise on the Training Outcomes for Case Management Systems in High Domain Expertise Fields

The successful implementation of an enterprise system requires training and end users in the new systems and procedures. There has been no research reporting a relationship between Domain Expertise (DE) and the successful implementation of an enterprise system. This study sought to begin filling this knowledge gap by exploring the relationship between DE, technical proficiency, training outcomes, and perceived training effectiveness for a new enterprise system, specifically a Case Management System (CMS) in a small and medium enterprise (SME). The research examines different subjects of technical expertise including skills, abilities, and knowledge to increase professional acceptance in the high domain of expertise field. In order to understand the complex nature of expertise and the significant impact, an exploratory approach is undertaken. Purposive sampling was utilized to select the 88 respondents to participate in the research, in which the role of domain expertise and technical expertise is explored. Based upon analysis, research showed the relevance of domain expertise and technical expertise in the deployment of successful case management systems. The results contributed to literature by showing that how training influences soft skills such as tacit knowledge on organizational culture and potential clients, deliver best solutions to the project management. Meanwhile, the outcomes provided significant traits on perceived training effectiveness, which drive increase in knowledge, practical implication, and quality of project delivered, presentation skills, communication and problem-solving abilities. The study also contributed to the literature in terms of defining how technical and domain expertise not only effect the outcomes of case management systems but also develop greater coordination for dealing the intricacies, project difficulties, and task-related complexities

Cased Based Reasoning in Business Process Management Design

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementArtificial intelligence became increasingly useful since the 1990s, trying to imitate the human brain with its thinking, reasoning, and learning using the key concepts of machine learning, deep learning, and artificial neural networks. Case-based reasoning (CBR), another form of artificial intelligence, stores and retrieves past cases that can be adapted to find a solution to a current problem. The new solution can then be retained and made available to solve other future problems. Business Process Management (BPM) analyzes and optimizes business processes to make them more effective and efficient for an organization’s strategy to ultimately increasing shareholder value. CBR can help to support BPM, making better decisions with existing knowledge when solving process problems. This study investigates effectively store, retrieve, and adapt Business Process Management Notation (BPMN) solutions that best fit the underlying BPM problem using case-based reasoning as a tool. Therefore, a theoretical model was proposed, containing each CBR live cycle phase with different possible tools applied to BPMN diagrams, which was validated by expert interviews. This study concludes that a whole CBR life cycle can be applied to BPMN diagram problems with the need for human intervention. This work did not have the objective to solve the whole problem but to contribute to a possible solution by using CBR through a theoretical model

Applying self-organised learning to develop critical thinkers for learning organisation: a conversational action research report.

The information explosion characteristic of a knowledge-based economy is fuelled by rapid technological changes. As technology continues to permeate our lives, there will be fresh demands upon the conduct of learning and teaching to ensure that learners are equipped with new economy skills and dispositions for creating significant and relevant meaning out of the large chunks of transmitted data. In the spirit of building learning organisations, this paper proposes that a two-pronged strategy of promoting self-organised learning (SoL) amongst educators and students be adopted. As an enabling framework based on social constructivism, the model of SoL, originally developed by Harri-Augstein & Thomas, is described and applied to an educational setting. For educators engaged in action research, SoL is suited as an approach for managing and reflecting upon change. The use of two such thinking tools, the Personal Learning Contract and the Purpose-Strategy-Outcome-Review (PSOR) reflective learning scaffolds are considered. For students who are now expected to learn independently in situations requiring problem-solving skills, much akin to real life contexts, this article also considers the application of Learning Plans as a conversational tool for personal project management. The authors conclude that SoL promotes skilful critical thinking through a systems thinking process of continuous reflective learning. It is proposed that these are essential qualities for citizens working in a technological age. Case study samples of the thinking tools used in this action research project are included as appendices and evaluated in this article

Integrated systems for site management

The operation of an efficient and integrated site management system is one of the problems that still requires a considerable amount of attention in most of the construction companies in U.K. This thesis describes the research I have undertaken on this problem and how a computer-aided construction management system can assist in solving the problem. The thesis has been divided into three sections according to the research. The first section describes the research I have undertaken as surveys on; the literature as an existing knowledge of efficient and integrated site management systems; what systems are applied on site and the degree of satisfaction from them; the facilities that can be provided by the available site management software. From the above surveys, the problem has been identified and the objectives established for the research. The second section of the thesis describes my development of a software model to facilitate collecting, processing and analyzing data from the site for producing control data and reports. The section also describes the integration of the model to the other construction management systems (i. e. estimating, planning, cashflow forecasting and valuation), as well as being self-contained. The last section of the thesis describes my research in investigating how well the model achieved the research objectives. This section described a number of case studies based as a demonstration of the model, its functions and mechanism, using slides and on-computer seminars. From this evaluation I have established a list of comments, some of them were used to modify the model or as conclusions and recommendations for any future research in this field

An SDI for the GIS-education at the UGent Geography Department

The UGent Geography Department (GD) (ca. 200 students; 10 professors) has been teaching GIS since the mid 90’s. Ever since, GIS has evolved from Geographic Information Systems, to GIScience, to GIServices; implying that a GIS specialist nowadays has to deal with more than just desktop GIS. Knowledge about the interaction between different components of an SDI (spatial data, technologies, laws and policies, people and standards) is crucial for a graduated Master student. For its GIS education, the GD has until recently been using different sources of datasets, which were stored in a non-centralized system. In conformity with the INSPIRE Directive and the Flemish SDI Decree, the GD aims to set-up its own SDI using free and open source software components, to improve the management, user-friendliness, copyright protection and centralization of datasets and the knowledge of state of the art SDI structure and technology. The central part of the system is a PostGIS-database in which both staff and students can create and share information stored in a multitude of tables and schemas. A web-based application facilitates upper-level management of the database for administrators and staff members. Exercises in various courses not only focus on accessing and handling data from the SDI through common GIS-applications as QuantumGIS or GRASS, but also aim at familiarizing students with the set-up of widely used SDI-elements as WMS, WFS and WCS services. The (dis)advantages of the new SDI will be tested in a case study in which the workflow of a typical ‘GIS Applications’ exercise is elaborated. By solving a problem of optimal location, students interact in various ways with geographic data. A comparison is made between the situation before and after the implementation of the SDI

Dynamic knowledge assets management to interactive problem solving and sustained learning : a collaborative CBR system in chronic and palliative care

Knowledge management is a decision-making approach for facilitating the development and application of a variety types of knowledge assets. There are a number of key questions in the field, including “how can we gather knowledge assets?” and “How can we evaluate knowledge management initiatives planned for improving user experiences?”. The identification of the key knowledge asset value drivers and their relations allows stakeholders to define priorities. It is also important to utilize existing knowledge effectively in the proper knowledge management of knowledge-based assets. Accordingly, building a knowledge-based system to solve new and similar problems is a research challenge that this thesis aims to address. Although search engines and question-answering systems already serve as crucial tools for knowledge workers, understanding texts and using knowledge obtained from the texts for problem-solving is far from routine. Thus, this work addresses the problem of developing a collaborative knowledge-based system that can learn from user experience and knowledge assets. The research described in this dissertation involved an investigation of the use of word association strength based on the statistical cohesions between words to build a semantic profile of a text. This approach in the retrieval of relevant information can provide reasoning information from a text in a manner that has traditionally required the use of human experts; this information then be reused in the analysis of new problems. In developing an artificial intelligence (AI)-based problem-solving technique, this study investigated the use of case-based reasoning (CBR), a methodology in which data representing information on solved problems is stored for reuse in new problem-solving processes. The choice of past cases to be reused is based on similarity measures in the retrieval process as extracted from all stored cases in the case base. Each similarity measure characterizes a set of heuristics for approximating the unidentified utility of a case, and the quality of similarity measures can be improved by integrating as much knowledge regarding the specific application domain as possible into them. Features relations from ontology and fuzzy logic can also be integrated into CBR similarity measures to handle the ambiguities and uncertainties that are characteristically present in knowledge-intensive processes. The system developed in this research – DePicT CLASS – is based on the DePicT concept, in which diseases are detected and predicted using image classification and text information from personal health records. DePicT CLASS was developed to serve as a collaborative case-based system to support caregivers and patients’ relatives by preparing relevant references and learning material to help them understand the patients’ medical issues. The main characteristics of DePicT and DePicT CLASS are demonstrated in this work using instances from two disease domains: dementia and melanoma.Wissensmanagement ist ein Ansatz der Entscheidungsfindung, um die Entwicklung und Anwendung von Wissensressourcen unterschiedlicher Art zu erleichtern. Es gibt eine Reihe von Schlüsselfragen in diesem Bereich, einschließlich „Wie können wir Wissensressourcen sammeln?“ und „Wie können wir Initiativen zum Wissensmanagement bewerten, die zur Verbesserung der Benutzererfahrungen geplant sind?“. Die Identifikation der wichtigsten Wissensbestandswerttreiber und deren Beziehungen ermöglichen es den Stakeholdern, Prioritäten zu definieren. Darüber hinaus ist es wichtig, vorhandenes Wissen effektiv für das richtige Wissensmanagement von wissensbasierten Beständen zu nutzen. Dementsprechend ist der Aufbau eines wissensbasierten Systems zur Lösung neuer und ähnlicher Probleme eine Forschungsherausforderung, die mit dieser Dissertation angegangen werden soll. Obwohl Suchmaschinen und Frage-Antwort-Systeme bereits als entscheidende Werkzeuge für Wissensarbeiter dienen, ist das Verstehen von Texten und das Verwenden von Wissen, das aus den Texten zur Problemlösung gewonnen wird, weit von einer Routine entfernt. Daher befasst sich diese Arbeit mit dem Problem der Entwicklung eines kollaborativen wissensbasierten Systems, das von Benutzererfahrungen und Wissensressourcen lernen kann. Die in dieser Dissertation beschriebene Forschung beinhaltete eine Untersuchung der Verwendung von Wortvereinigungsstärke basierend auf den statistischen Zusammenhängen zwischen Wörtern, um ein semantisches Profil eines Textes aufzubauen. Dieser Ansatz bei der Suche nach relevanten Informationen kann aus einem Text aufschlussreiche Informationen liefern, die traditionell den Einsatz von Experten erfordern. Diese Informationen werden dann bei der Analyse neuer Probleme wiederverwendet. Bei der Entwicklung einer auf künstlicher Intelligenz beruhenden Problemlösungs-Technik untersuchte diese Studie die Verwendung von fallbasiertem Denken (CBR), eine Methodik, bei der Daten, die Informationen zu gelösten Problemen darstellen, zur Wiederverwendung in neuen Problemlösungsprozessen gespeichert werden. Die Auswahl der zu verwendenden Fälle in der Vergangenheit basiert auf Ähnlichkeitsmaßen im Abrufprozess, die aus allen gespeicherten Fällen in der Fallbasis extrahiert werden. Jedes Ähnlichkeitsmaß charakterisiert eine Menge von Heuristiken zur Approximation des nicht identifizierten Nutzens eines Falls, und die Qualität von Ähnlichkeitsmaßen kann verbessert werden, indem so viel Wissen wie möglich in die spezifische Anwendungsdomäne integriert wird. Eigenschaften-Relationen aus Ontologie und Fuzzy-Logik können auch in CBR-Ähnlichkeitsmaße integriert werden, um die Mehrdeutigkeiten und Unsicherheiten zu bewältigen, die charakteristisch in wissensintensiven Prozessen vorhanden sind. Das in dieser Studie entwickelte System – DePicT CLASS - basiert auf dem DePicT Konzept, bei dem Krankheiten anhand von Bildklassifikationen und Textinformationen aus persönlichen Gesundheitsakten erkannt und vorhergesagt werden. DePicT CLASS wurde entwickelt, um als kollaboratives fallbasiertes System zur Unterstützung von Angehörigen und Angehörigen von Patienten zu dienen, indem relevante Referenzen und Lernmaterialien erstellt werden, die ihnen helfen, die medizinischen Probleme der Patienten zu verstehen. Die Hauptmerkmale von DePicT und DePicT CLASS werden in dieser Arbeit anhand von Beispielen aus zwei Krankheitsdomänen demonstriert: Demenz und Melanom

Moving KM to the next generation: The contribution of critical systems thinking

Knowledge Management (KM) is multifaceted and grounded in various disciplines including psychology, strategy, organizational behavior, economics, and management. It is therefore not surprising that KM has developed rapidly as a field with a myriad of frameworks designed to address KM needs in organizations. The emphasis of studies tends to be on the application of KM with paucity in the discussion of its theory and underpinning philosophy. As a result, KM is varied in definition and application. The range of KM tools and practices has caused some concern with authors suggesting that there is need for KM to be applied in an integrated manner. Systems Thinking (ST) is the conceptual framework for problem solving that views situations holistically. Critical Systems Thinking (CST) is the latest movement in ST that was born from the need to appreciate the diversity in approaches so as to identify the most suitable methodology for a problem context. CST is described by the commitments of critical awareness, sociological awareness, pluralism, complementarity and human emancipation. The application of CST is said to have reformed ST through its commitments and brought synthesis through the provision of a rational approach of combining system methodologies. Activities that create, capture and utilize knowledge are inherent in systems methods thus indicating a similarity between ST and KM. Authors have as such, called for the use of CST to underpin KM theory and practice. This paper highlights the contribution of CST to the maturity of Systems Thinking as a discipline. Potential use of CST in developing more unified, systemic and holistic approaches to handling KM is put forward. The aim is to spark conversation on the need for a new generation of KM that is grounded theoretically and philosophically, and based on more than practical case studies