Passive Fault-Tolerance Management in Component-based Embedded Systems

It is imperative to accept that failures can and will occur, even in meticulously designed distributed systems, and design proper measures to counter those failures. Passive replication minimises resource consumption by only activating redundant replicas in case of failures, as typically providing and applying state updates is less resource demanding than requesting execution. However, most existing solutions for passive fault tolerance are usually designed and configured at design time, explicitly and statically identifying the most critical components and their number of replicas, lacking the needed flexibility to handle the runtime dynamics of distributed component-based embedded systems. This paper proposes a cost-effective adaptive fault tolerance solution with a significant lower overhead compared to a strict active redundancy-based approach, achieving a high error coverage with the minimum amount of redundancy. The activation of passive replicas is coordinated through a feedback-based coordination model that reduces the complexity of the needed interactions among components until a new collective global service solution is determined, improving the overall maintainability and robustness of the system.

Risk-based decision support system for life cycle management of industrials plants

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Model-based diagnosis system: application to constructed wetlands

Report for the scientific sojourn carried out at the Model-based Systems and Qualitative Reasoning Group (Technical University of Munich), from September until December 2005. Constructed wetlands (CWs), or modified natural wetlands, are used all over the world as wastewater treatment systems for small communities because they can provide high treatment efficiency with low energy consumption and low construction, operation and maintenance costs. Their treatment process is very complex because it includes physical, chemical and biological mechanisms like microorganism oxidation, microorganism reduction, filtration, sedimentation and chemical precipitation. Besides, these processes can be influenced by different factors. In order to guarantee the performance of CWs, an operation and maintenance program must be defined for each Wastewater Treatment Plant (WWTP). The main objective of this project is to provide a computer support to the definition of the most appropriate operation and maintenance protocols to guarantee the correct performance of CWs. To reach them, the definition of models which represent the knowledge about CW has been proposed: components involved in the sanitation process, relation among these units and processes to remove pollutants. Horizontal Subsurface Flow CWs are chosen as a case study and the filtration process is selected as first modelling-process application. However, the goal is to represent the process knowledge in such a way that it can be reused for other types of WWTP.

Interacting software-agents to support a kind of expert supervision systems

Expert supervision systems are software applications specially designed to automate process monitoring. The goal is to reduce the dependency on human operators to assure the correct operation of a process including faulty situations. Construction of this kind of application involves an important task of design and development in order to represent and to manipulate process data and behaviour at different degrees of abstraction for interfacing with data acquisition systems connected to the process. This is an open problem that becomes more complex with the number of variables, parameters and relations to account for the complexity of the process. Multiple specialised modules tuned to solve simpler tasks that operate under a co-ordination provide a solution. A modular architecture based on concepts of software agents, taking advantage of the integration of diverse knowledge-based techniques, is proposed for this purpose. The components (software agents, communication mechanisms and perception/action mechanisms) are based on ICa (Intelligent Control architecture), software middleware supporting the build-up of applications with software agent features

The Role of an Enterprise Portal in Knowledge Management Strategy, Case: Private Equity Investor

Työ tutkii yritysportaalin roolia organisaation tietojohtamisessa. Tutkimusongelman ratkaisemiseksi luodaan viitekehys, jossa yritysportaalin ja tietojohtamisen teoriat linkittyvät. Työn empiirisessä osassa viitekehys on pohjana case-yritykselle rakennettavalle yritysportaalille. Laadullinen tutkimus käsittää teoriaosuuden sekä osallistuvaan case-tutkimukseen perustuvan empiriaosuuden. Työn runko muodostuu kahden vastakkaisen tietojohtamisajattelun vuoropuhelusta, jotka ovat informaatioteknologiaan- ja strategiseen johtamiseen perustuvat näkökulmat. Toimivan tietojohtamismallin täytyy sisältää molemmat aspektit. Jokainen organisaatio tarvitsee informaation hallintaan liittyviä toiminnallisuuksia ja täten eksplisiittisen tiedon hallinta tietojärjestelmien avulla on onnistuneen tietojohtamisen kulmakiviä. Tätä perusinfrastruktuuria on mahdollista laajentaa hiljaisen tiedon hallintaan perustuvilla tietojohtamismenetelmillä. Työn ratkaisu näiden kahden näkemyksen, 'kovan' informaatioteknogiaan painottuvan sekä 'pehmeän' ihmisnäkökulman integrointiin, on yritysportaali. Työssä käytettävä yritysportaalin viitekehys rakentuu kolmeen päätoiminnallisuuteen; sisällönhallintaan, yhteistyöominaisuuksiin ja liiketoimintatiedon hallintaan. Työ todistaa yhteyden viitekehyksen sekä tietojohtamisen perusmallien, kuten tietojohtamisen prosessimallin sekä tietoympäristöjen välillä. Yritysportaali voi täten toimia, ei ainoastaan yksittäisten tietojohtamistyökalujen implementoinnissa, vaan tietojohtamisstrategian luomisen apuna tarjoten alustan tai 'katalyytin' kokonaisvaltaiselle tietojohtamiselle.

External knowledge transfer mechanisms in service business acquisition

In knowledge-intensive economy an effective knowledge transfer is a part of the firm’s strategy to achieve a competitive advantage in the market. Knowledge transfer related to a variety of mechanisms depends on the nature of knowledge and context. The topic is, however, very little empirical studied and there is a research gap in scientific literature. This study examined and analyzed external knowledge transfer mechanisms in service business and especially in the context of acquisitions. The aim was to find out what kind of mechanisms was used when the buyer began to transfer data e.g. their own agendas and practices to the purchased units. Another major research goal was to identify the critical factors which contributed to knowledge transfer through different mechanisms. The study was conducted as a multiple-case study in a consultative service business company, in its four business units acquired by acquisition, in various parts of the country. The empirical part of the study was carried out as focus group interviews in each unit, and the data were analyzed using qualitative methods. The main findings of this study were firstly the nine different knowledge transfer mechanisms in service business acquisition: acquisition management team as an initiator, unit manager as a translator, formal training, self-directed learning, rooming-in, IT systems implementation, customer relationship management, codified database and ecommunication. The used mechanisms brought up several aspects as giving the face to changing, security of receiving right knowledge and correctly interpreted we-ness atmosphere, and orientation to use more consultative touch with customers. The study pointed out seven critical factors contributed to different mechanisms: absorption, motivation, organizational learning, social interaction, trust, interpretation and time resource. The two last mentioned were new findings compared to previous studies. Each of the mechanisms and the related critical factors contributed in different ways to the activity in different units after the acquisition. The role of knowledge management strategy was the most significant managerial contribution of the study. Phenomenon is not recognized enough although it is strongly linked in knowledge based companies. The recognition would help to develop a better understanding of the business through acquisitions, especially in situations such as where two different knowledge strategies combines in new common company.

Knowledge Sharing between Generations in an Organisation - Retention of the Old or Building the New?

The study explores knowledge transfer between retiring employees and their successors in expert work. My aim is to ascertain whether there is knowledge development or building new knowledge related to this organisational knowledge transfer between generations; in other words, is the transfer of knowledge from experienced, retiring employees to their successors merely retention of the existing organisational knowledge by distributing it from one individual to another or does this transfer lead to building new and meaningful organisational knowledge. I call knowledge transfer between generations and the possibly related knowledge building in this study knowledge sharing between generations. The study examines the organisation and knowledge management from a knowledge-based and constructionist view. From this standpoint, I see knowledge transfer as an interactive process, and the exploration is based on how the people involved in this process understand and experience the phenomenon studied. The research method is organisational ethnography. I conducted the analysis of data using thematic analysis and the articulation method, which has not been used before in organisational knowledge studies. The primary empirical data consists of theme interviews with twelve employees involved in knowledge transfer in the organisation being studied and five follow-up theme interviews. Six of the interviewees are expert duty employees due to retire shortly, and six are their successors. All those participating in the follow-up interviews are successors of those soon to retire from their expert responsibilities. The organisation in the study is a medium-sized Finnish firm, which designs and manufactures electrical equipment and systems for the global market. The results of the study show that expert work-related knowledge transfer between generations can mean knowledge building which produces new, meaningful knowledge for the organisation. This knowledge is distributed in the organisation to all those that find it useful in increasing the efficiency and competitiveness of the whole organisation. The transfer and building of knowledge together create an act of knowledge sharing between generations where the building of knowledge presupposes transfer. Knowledge sharing proceeds between the expert and the novice through eight phases. During the phases of knowledge transfer the expert guides the novice to absorb the knowledge to be transferred. With the expert’s help the novice gradually comes to understand the knowledge and in the end he or she is capable of using it in his or her work. During the phases of knowledge building the expert helps the novice to further develop the knowledge being transferred so that it becomes new, useful knowledge for the organisation. After that the novice takes the built knowledge to use in his or her work. Based on the results of the study, knowledge sharing between generations takes place in interaction and ends when knowledge is taken to use. The results I obtained in the interviews by the articulation method show that knowledge sharing between generations is shaped by the novices’ conceptions of their own work goals, knowledge needs and duties. These are not only based on the official definition of the work, but also how the novices find their work or how they prioritise the given objectives and responsibilities. The study shows that the novices see their work primarily as maintenance or development. Those primarily involved in maintenance duties do not necessarily need knowledge defined as transferred between generations. Therefore, they do not necessarily transfer knowledge with their assigned experts, even though this can happen in favourable circumstances. They do not build knowledge because their view of their work goals and duties does not require the building of new knowledge. Those primarily involved in development duties, however, do need knowledge available from their assigned experts. Therefore, regardless of circumstances they transfer knowledge with their assigned experts and also build knowledge because their work goals and duties create a basis for building new knowledge. The literature on knowledge transfer between generations has focused on describing either the knowledge being transferred or the means by which it is transferred. Based on the results of this study, however, knowledge sharing between generations, that is, transfer and building is determined by how the novice considers his or her own knowledge needs and work practices. This is why studies on knowledge sharing between generations and its implementation should be based not only on the knowledge content and how it is shared, but also on the context of the work in which the novice interprets and shares knowledge. The existing literature has not considered the possibility that knowledge transfer between generations may mean building knowledge. The results of this study, however, show that this is possible. In knowledge building, the expert’s existing organisational knowledge is combined with the new knowledge that the novice brings to the organisation. In their interaction this combination of the expert’s “old” and the novice’s “new” knowledge becomes new, meaningful organisational knowledge. Previous studies show that knowledge development between the members of an organisation is the prerequisite for organisational renewal which in turn is essential for improved competitiveness. Against this background, knowledge building enables organisational renewal and thus enhances competitiveness. Hence, when knowledge transfer between generations is followed by knowledge building, the organisation kills two birds with one stone. In knowledge transfer the organisation retains the existing knowledge and thus maintains its competitiveness. In knowledge building the organisation developsnew knowledge and thus improves its competitiveness.

Organisational learning and knowledge acquisition. A case study of internationalising manufacturers of wood-based prefabricated buildings

The study investigates organisational learning and knowledge acquisition of wood-based prefabricated building manufacturers. This certain group of case companies was chosen, because their management and their employees generally have a strong manufacturing and engineering background, while the housing sector is characterised by national norms, regulations, as well as local building styles. Considering this setting, it was investigated, how the case companies develop organisational learning capabilities, acquire and transfer knowledge for their internationalisation. The theoretical framework of this study constitutes the knowledge-based conceptualisation of internationalisation, which combines the traditional internationalisation process, as well as the international new venture perspective based on their commonalities in the knowledge-based view of the firm. Different theories of internationalisation, including the network-perspective, were outlined and a framework on organisational learning and knowledge acquisition was established. The empirical research followed a qualitative approach, deploying a multiple-case study with five case companies from Austria, Finland and Germany. In the study, the development of the wood-based prefabricated building industry and of the case companies are described, and the motives, facilitators and challenges for foreign expansion, as well as the companies’ internationalisation approaches are compared. Different methods of how companies facilitate the knowledge-exchange or learn about new markets are also outlined. Experience, market knowledge and personal contacts are considered essential for the internationalisation process. The major finding of the study is that it is not necessary to acquire the market knowledge internally in a slow process as proposed by the Uppsala model. In four cases companies engaged knowledge in symbiotic relations with local business partners. Thereby, the building manufacturers contribute their design and production capabilities, and in return, their local partners provide them with knowledge about the market and local regulations; while they manage the sales and construction operations. Thus, the study provides strong evidence for the propositions of network perspective. One case company developed the knowledge internally in a gradual process: it entered the market sequentially with several business lines, showing an increasing level of complexity. In both of the observed strategies, single-loop and double-loop learning processes occurred.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Barriers of evidence based policy making in iran's health system

La formation des sociétés fondées sur la connaissance, le progrès de la technologie de communications et un meilleur échange d'informations au niveau mondial permet une meilleure utilisation des connaissances produites lors des décisions prises dans le système de santé. Dans des pays en voie de développement, quelques études sont menées sur des obstacles qui empêchent la prise des décisions fondées sur des preuves (PDFDP) alors que des études similaires dans le monde développé sont vraiment rares. L'Iran est le pays qui a connu la plus forte croissance dans les publications scientifiques au cours de ces dernières années, mais la question qui se pose est la suivante : quels sont les obstacles qui empêchent l'utilisation de ces connaissances de même que celle des données mondiales? Cette étude embrasse trois articles consécutifs. Le but du premier article a été de trouver un modèle pour évaluer l'état de l'utilisation des connaissances dans ces circonstances en Iran à l’aide d'un examen vaste et systématique des sources suivie par une étude qualitative basée sur la méthode de la Grounded Theory. Ensuite au cours du deuxième et troisième article, les obstacles aux décisions fondées sur des preuves en Iran, sont étudiés en interrogeant les directeurs, les décideurs du secteur de la santé et les chercheurs qui travaillent à produire des preuves scientifiques pour la PDFDP en Iran. Après avoir examiné les modèles disponibles existants et la réalisation d'une étude qualitative, le premier article est sorti sous le titre de «Conception d'un modèle d'application des connaissances». Ce premier article sert de cadre pour les deux autres articles qui évaluent les obstacles à «pull» et «push» pour des PDFDP dans le pays. En Iran, en tant que pays en développement, les problèmes se situent dans toutes les étapes du processus de production, de partage et d’utilisation de la preuve dans la prise de décision du système de santé. Les obstacles qui existent à la prise de décision fondée sur des preuves sont divers et cela aux différents niveaux; les solutions multi-dimensionnelles sont nécessaires pour renforcer l'impact de preuves scientifiques sur les prises de décision. Ces solutions devraient entraîner des changements dans la culture et le milieu de la prise de décision afin de valoriser la prise de décisions fondées sur des preuves. Les critères de sélection des gestionnaires et leur nomination inappropriée ainsi que leurs remplaçants rapides et les différences de paiement dans les secteurs public et privé peuvent affaiblir la PDFDP de deux façons : d’une part en influant sur la motivation des décideurs et d'autre part en détruisant la continuité du programme. De même, tandis que la sélection et le remplacement des chercheurs n'est pas comme ceux des gestionnaires, il n'y a aucun critère pour encourager ces deux groupes à soutenir le processus décisionnel fondés sur des preuves dans le secteur de la santé et les changements ultérieurs. La sélection et la promotion des décideurs politiques devraient être basées sur leur performance en matière de la PDFDP et les efforts des universitaires doivent être comptés lors de leurs promotions personnelles et celles du rang de leur institution. Les attitudes et les capacités des décideurs et des chercheurs devraient être encouragés en leur donnant assez de pouvoir et d’habiliter dans les différentes étapes du cycle de décision. Cette étude a révélé que les gestionnaires n'ont pas suffisamment accès à la fois aux preuves nationales et internationales. Réduire l’écart qui sépare les chercheurs des décideurs est une étape cruciale qui doit être réalisée en favorisant la communication réciproque. Cette question est très importante étant donné que l'utilisation des connaissances ne peut être renforcée que par l'étroite collaboration entre les décideurs politiques et le secteur de la recherche. Dans ce but des programmes à long terme doivent être conçus ; la création des réseaux de chercheurs et de décideurs pour le choix du sujet de recherche, le classement des priorités, et le fait de renforcer la confiance réciproque entre les chercheurs et les décideurs politiques semblent être efficace.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.

Interacting software-agents to support a kind of expert supervision systems

Expert supervision systems are software applications specially designed to automate process monitoring. The goal is to reduce the dependency on human operators to assure the correct operation of a process including faulty situations. Construction of this kind of application involves an important task of design and development in order to represent and to manipulate process data and behaviour at different degrees of abstraction for interfacing with data acquisition systems connected to the process. This is an open problem that becomes more complex with the number of variables, parameters and relations to account for the complexity of the process. Multiple specialised modules tuned to solve simpler tasks that operate under a co-ordination provide a solution. A modular architecture based on concepts of software agents, taking advantage of the integration of diverse knowledge-based techniques, is proposed for this purpose. The components (software agents, communication mechanisms and perception/action mechanisms) are based on ICa (Intelligent Control architecture), software middleware supporting the build-up of applications with software agent features

An ontological approach to personalized medical knowledge recommendation

Knowledge recommendation has become a promising method in supporting the clinicians decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users requirements accurately and realize personalized recommendation. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge recommendations. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthermore, the personalized recommendation mechanism is illustrated.

An ontological approach to personalized medical knowledge matching

Knowledge management has become a promising method in supporting the clinicians′ decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users′ requirements accurately and realize personalized matching. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge matching. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthmore, the personalized matching mechanism and algorithm are illustrated.

Granularity and disaggregation in compositional modelling with applications to ecological systems

In the past decade, compositional modelling (CM) has established itself as the predominant knowledge-based approach to construct mathematical (simulation) models automatically. Although it is mainly applied to physical systems, there is a growing interest in applying CM to other domains, such as ecological and socio-economic systems. Inspired by this observation, this paper presents a method for extending the conventional CM techniques to suit systems that are fundamentally presented by interacting populations of individuals instead of physical components or processes. The work supports building model repositories for such systems, especially in addressing the most critical outstanding issues of granularity and disaggregation in ecological systems modelling.