Beyond the Knowledge-Based Theory of the Geographic Cluster

The knowledge-based theory of the geographic cluster represents a major attempt to re-conceptualize clusters, in essence arguing that the localization of firms in similar and related industries stimulates learning and innovation, giving a competitive advantage to clustered firms. This paper critically examines the knowledge-based theory the cluster, arguing that it has greatly overstated the advantages of co-location to firms and misidentified the mechanisms through which learning occurs in clusters. In particular, the theory is criticized on three points: the flexible, under-specified way that it defines its object of study; the focus on firms as an explanatory variable instead of more fundamental processes of resource accumulation; and the functionalist mode of theory that employs as an explanation. Ways to address of each of these issues are discussed. In a final section I suggest that the rather static notions of learning put forward in the knowledge-based theory of the cluster be replaced by a developmental theory of regional dynamics that focuses on both learning and structural transformation.geographic cluster, localization, relatedness, knowledge-based theory

A Framework for the Automatic Physical Configuration and Tuning of a Mysql Community Server

Manual physical configuration and tuning of database servers, is a complicated task requiring a high level of expertise. Database administrators must consider numerous possibilities, to determine a candidate configuration for implementation. In recent times database vendors have responded to this problem, providing solutions which can automatically configure and tune their products. Poor configuration choices, resulting in performance degradation commonplace in manual configurations, have been significantly reduced in these solutions. However, no such solution exists for MySQL Community Server. This thesis, proposes a novel framework for automatically tuning a MySQL Community Server. A first iteration of the framework has been built and is presented in this paper together with its performance measurements

Outliers in rules - the comparision of LOF, COF and KMEANS algorithms

bases. The subject of outlier mining is very important nowadays. Outliers in rules mean unusual rules which are rare in comparison to others and should be explored further by the domain expert. In the research the authors use the outlier detection methods to find a given (1%, 5%, 10%) number of outliers in rules. Then, they analyze which of seven various quality indices, that they used for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage the authors used six different knowledge bases. The results show that the optimal results were achieved for COF outlier detection algorithm as the one for which, among all analyzed quality indices, the cluster quality improved most frequently

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Role based behavior analysis

Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso são trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de Informacão/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nível da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. Além disso, se não houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiências noutras. Finalmente, incentivar a proactividade não implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese é desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo é iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com características semelhantes são agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais são comprados com os perfis de grupo, e os que diferem significativamente são marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda métodos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisão comportamento semelhante. Além disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos são assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it

Neurobehavioral Strategies of Skill Acquisition in Left and Right Hand Dominant Individuals

The brain consists of vast networks of connected pathways communicating through synchronized electrochemical activity propagated along fiber tracts. The current understanding is that the brain has a modular organization where regions of specialized processes are dynamically coupled through long-range projections of dense axonal networks connecting spatially distinct regions enabling signal transfer necessary for all complex thought and behavior, including regulation of movement. The central objective of the dissertation was to understand how sensorimotor information is integrated, allowing for adaptable motor behavior and skill acquisition in the left-and right-hand dominant populations. To this end participants, of both left- and right-hand dominance, repeatedly completed a visually guided, force matching task while neurobiological and neurobehavioral outcome measurements were continuously recorded via EEG and EMG. Functional connectivity and graph theoretical measurements were derived from EEG. Cortico-cortical coherence patterns were used to infer neurostrategic discrepancies employed in the execution of a motor task for each population. EEG activity was also correlated with neuromuscular activity from EMG to calculate cortico-muscular connectivity. Neurological patterns and corresponding behavioral changes were used to express how hand dominance influenced the developing motor plan, thereby increasing understanding of the sensorimotor integration process. The cumulative findings indicated fundamental differences in how left- and right-hand dominant populations interact with the world. The right-hand dominant group was found to rely on visual information to inform motor behavior where the left-hand dominant group used visual information to update motor behavior. The left-hand group was found to have a more versatile motor plan, adaptable to both dominant, nondominant, and bimanual tasks. Compared to the right-hand group it might be said that they were more successful in encoding the task, however behaviorally they performed the same. The implications of the findings are relevant to both clinical and performance applications providing insight as to potential alternative methods of information integration. The inclusion of the left-hand dominant population in the growing conceptualization of the brain will generate a more complete, stable, and accurate understanding of our complex biology