Durante o ciclo de vida de um web service, diversas mudanças são feitas na sua interface, eventualmente causando incompatibilidades em relação aos seus clientes e ocasionando a quebra de suas aplicações. Os provedores precisam tomar decisões sobre mudanças em seus serviços frequentemente, muitas vezes sem um bom entendimento a respeito do efeito destas mudanças sobre seus clientes. Os trabalhos e ferramentas existentes não fornecem ao provedor um conhecimento adequado a respeito do uso real das funcionalidades da interface de um serviço, considerando os diferentes tipos de consumidores, o que impossibilita avaliar o impacto das mudanças. Este trabalho apresenta um framework para a descoberta de perfis de uso de serviços web, os quais constituem um modelo descritivo dos padrões de uso dos diferentes grupos de clientes do serviço, com relação ao uso das funcionalidades em sua interface. O framework auxilia no processo de descoberta de conhecimento através de tarefas semiautomáticas e parametrizáveis para a preparação e análise de dados de uso, minimizando a necessidade de intervenção do usuário. O framework engloba o monitoramento de interações de web services, a carga de dados de uso pré-processados em uma base de dados unificada, e a geração de perfis de uso. Técnicas de mineração de dados são utilizadas para agrupar clientes de acordo com seus padrões de uso de funcionalidades, e esses grupos são utilizados na construção de perfis de uso de serviços. Todo o processo é configurado através de parâmetros, permitindo que o usuário determine o nível de detalhe das informações sobre o uso incluídas nos perfis e os critérios para avaliar a similaridade entre clientes. A proposta é validada por meio de experimentos com dados sintéticos, simulados de acordo com características esperadas no comportamento de clientes de um serviço real. Os resultados dos experimentos demonstram que o framework proposto permite a descoberta de perfis de uso de serviço úteis, e fornecem evidências a respeito da parametrização adequada do framework. / During the life cycle of a web service, several changes are made in its interface, which possibly are incompatible with regard to current usage and may break client applications. Providers must make decisions about changes on their services, most often without insight on the effect these changes will have over their customers. Existing research and tools fail to input provider with proper knowledge about the actual usage of the service interface’s features, considering the distinct types of customers, making it impossible to assess the actual impact of changes. This work presents a framework for the discovery of web service usage profiles, which constitute a descriptive model of the usage patterns found in distinct groups of clients, concerning the usage of service interface features. The framework supports a user in the process of knowledge discovery over service usage data through semi-automatic and configurable tasks, which assist the preparation and analysis of usage data with the minimum user intervention possible. The framework performs the monitoring of web services interactions, loads pre-processed usage data into a unified database, and supports the generation of usage profiles. Data mining techniques are used to group clients according to their usage patterns of features, and these groups are used to build service usage profiles. The entire process is configured via parameters, which allows the user to determine the level of detail of the usage information included in the profiles, and the criteria for evaluating the similarity between client applications. The proposal is validated through experiments with synthetic data, simulated according to features expected in the use of a real service. The experimental results demonstrate that the proposed framework allows the discovery of useful service usage profiles, and provide evidences about the proper parameterization of the framework.

Durante o ciclo de vida de um web service, diversas mudanças são feitas na sua interface, eventualmente causando incompatibilidades em relação aos seus clientes e ocasionando a quebra de suas aplicações. Os provedores precisam tomar decisões sobre mudanças em seus serviços frequentemente, muitas vezes sem um bom entendimento a respeito do efeito destas mudanças sobre seus clientes. Os trabalhos e ferramentas existentes não fornecem ao provedor um conhecimento adequado a respeito do uso real das funcionalidades da interface de um serviço, considerando os diferentes tipos de consumidores, o que impossibilita avaliar o impacto das mudanças. Este trabalho apresenta um framework para a descoberta de perfis de uso de serviços web, os quais constituem um modelo descritivo dos padrões de uso dos diferentes grupos de clientes do serviço, com relação ao uso das funcionalidades em sua interface. O framework auxilia no processo de descoberta de conhecimento através de tarefas semiautomáticas e parametrizáveis para a preparação e análise de dados de uso, minimizando a necessidade de intervenção do usuário. O framework engloba o monitoramento de interações de web services, a carga de dados de uso pré-processados em uma base de dados unificada, e a geração de perfis de uso. Técnicas de mineração de dados são utilizadas para agrupar clientes de acordo com seus padrões de uso de funcionalidades, e esses grupos são utilizados na construção de perfis de uso de serviços. Todo o processo é configurado através de parâmetros, permitindo que o usuário determine o nível de detalhe das informações sobre o uso incluídas nos perfis e os critérios para avaliar a similaridade entre clientes. A proposta é validada por meio de experimentos com dados sintéticos, simulados de acordo com características esperadas no comportamento de clientes de um serviço real. Os resultados dos experimentos demonstram que o framework proposto permite a descoberta de perfis de uso de serviço úteis, e fornecem evidências a respeito da parametrização adequada do framework. / During the life cycle of a web service, several changes are made in its interface, which possibly are incompatible with regard to current usage and may break client applications. Providers must make decisions about changes on their services, most often without insight on the effect these changes will have over their customers. Existing research and tools fail to input provider with proper knowledge about the actual usage of the service interface’s features, considering the distinct types of customers, making it impossible to assess the actual impact of changes. This work presents a framework for the discovery of web service usage profiles, which constitute a descriptive model of the usage patterns found in distinct groups of clients, concerning the usage of service interface features. The framework supports a user in the process of knowledge discovery over service usage data through semi-automatic and configurable tasks, which assist the preparation and analysis of usage data with the minimum user intervention possible. The framework performs the monitoring of web services interactions, loads pre-processed usage data into a unified database, and supports the generation of usage profiles. Data mining techniques are used to group clients according to their usage patterns of features, and these groups are used to build service usage profiles. The entire process is configured via parameters, which allows the user to determine the level of detail of the usage information included in the profiles, and the criteria for evaluating the similarity between client applications. The proposal is validated through experiments with synthetic data, simulated according to features expected in the use of a real service. The experimental results demonstrate that the proposed framework allows the discovery of useful service usage profiles, and provide evidences about the proper parameterization of the framework.

Durante o ciclo de vida de um web service, diversas mudanças são feitas na sua interface, eventualmente causando incompatibilidades em relação aos seus clientes e ocasionando a quebra de suas aplicações. Os provedores precisam tomar decisões sobre mudanças em seus serviços frequentemente, muitas vezes sem um bom entendimento a respeito do efeito destas mudanças sobre seus clientes. Os trabalhos e ferramentas existentes não fornecem ao provedor um conhecimento adequado a respeito do uso real das funcionalidades da interface de um serviço, considerando os diferentes tipos de consumidores, o que impossibilita avaliar o impacto das mudanças. Este trabalho apresenta um framework para a descoberta de perfis de uso de serviços web, os quais constituem um modelo descritivo dos padrões de uso dos diferentes grupos de clientes do serviço, com relação ao uso das funcionalidades em sua interface. O framework auxilia no processo de descoberta de conhecimento através de tarefas semiautomáticas e parametrizáveis para a preparação e análise de dados de uso, minimizando a necessidade de intervenção do usuário. O framework engloba o monitoramento de interações de web services, a carga de dados de uso pré-processados em uma base de dados unificada, e a geração de perfis de uso. Técnicas de mineração de dados são utilizadas para agrupar clientes de acordo com seus padrões de uso de funcionalidades, e esses grupos são utilizados na construção de perfis de uso de serviços. Todo o processo é configurado através de parâmetros, permitindo que o usuário determine o nível de detalhe das informações sobre o uso incluídas nos perfis e os critérios para avaliar a similaridade entre clientes. A proposta é validada por meio de experimentos com dados sintéticos, simulados de acordo com características esperadas no comportamento de clientes de um serviço real. Os resultados dos experimentos demonstram que o framework proposto permite a descoberta de perfis de uso de serviço úteis, e fornecem evidências a respeito da parametrização adequada do framework. / During the life cycle of a web service, several changes are made in its interface, which possibly are incompatible with regard to current usage and may break client applications. Providers must make decisions about changes on their services, most often without insight on the effect these changes will have over their customers. Existing research and tools fail to input provider with proper knowledge about the actual usage of the service interface’s features, considering the distinct types of customers, making it impossible to assess the actual impact of changes. This work presents a framework for the discovery of web service usage profiles, which constitute a descriptive model of the usage patterns found in distinct groups of clients, concerning the usage of service interface features. The framework supports a user in the process of knowledge discovery over service usage data through semi-automatic and configurable tasks, which assist the preparation and analysis of usage data with the minimum user intervention possible. The framework performs the monitoring of web services interactions, loads pre-processed usage data into a unified database, and supports the generation of usage profiles. Data mining techniques are used to group clients according to their usage patterns of features, and these groups are used to build service usage profiles. The entire process is configured via parameters, which allows the user to determine the level of detail of the usage information included in the profiles, and the criteria for evaluating the similarity between client applications. The proposal is validated through experiments with synthetic data, simulated according to features expected in the use of a real service. The experimental results demonstrate that the proposed framework allows the discovery of useful service usage profiles, and provide evidences about the proper parameterization of the framework.

Security awareness is becoming an increasingly valuable characteristic due to the increased digitization of society. The commonality of constantly connected devices, such as smartphones and tablets, along with the threat of malware and cyber-attacks has sparked an interest in creating a system with the purpose of training people in security awareness. This thesis aims to show the presence of patterns in mobile device usage, and explore the possibility of using pattern detection as a means to predict riskful actions on mobile devices as a step to evaluate the prediction approach for use in the training system.A survey has been conducted by gathering usage data from a number of participants through the use of a logging application. This data was then analyzed using artificial neural networks provided by the open source FANN library in search for patterns preluding certain events. The results lend support to the claim that patterns exist in the way mobile devices are used, but the usefulness of FANN as a tool for finding these patterns was shown to be questionable.

Method forms the basis of scientific research, enabling criticism, selection and extension of current knowledge. However, methods are usually confined to the literature, where they are often difficult to find, understand, compare, or repeat. Bioinformatics and computational biology provide a rich opportunity for resource creation and discovery, with a rapidly expanding "resourceome". Many of these resources are difficult to find due to the large choice available, and there are only a limited number of sufficiently populated lists that can help inform resource selection. Text mining has enabled large scale data analysis and extraction from within the scientific literature, and as such can provide a way to help explore the vast wealth of resources available, which form the basis of bioinformatics methods. As such, this thesis aims to survey the computational biology literature, using text mining to extract database and software resource name mentions. By evaluating the common pairs and patterns of usage of these resources within such articles, an abstract approximation of the in silico methods employed within the target domain is developed. Specifically, this thesis provides an analysis of the difficulties of resource name extraction from the literature, then using this knowledge to develop bioNerDS - a rule-based system that can detect database and software name mentions within full-text documents (with a final F-score of 67%). bioNerDS is then applied to the full-text document corpus from PubMed Central, the results of which are then explored to identify the differences in resource usage between different domains (bioinformatics, biology and medicine) through time, different journals and different document sections. In particular, the well established resources (e.g., BLAST, GO and GenBank) remain pervasive throughout the domains, although they are seeing a slight decline in usage. Statistical programs see high levels of usage, with R in bioinformatics and SPSS in medicine being frequently mentioned throughout the literature. An overview of the common resource pairs has been generated by pairing database and software names which directly co-occur after one another in text. Combining and aggregating these resource pairs together across the literature enables the generation of a network of common resource patterns within computational biology, which provides an abstract representation of the common in silico methods used. For example, sequence alignment tools remain an important part of several computational biology analysis pipelines, and GO is a strong network sink (primarily used for data annotation). The networks also show the emergence of proteomics and next generation sequencing resources, and provide a specialised overview of a typical phylogenetics method. This work performs an analysis of common resource usage patterns, and thus provides an important first step towards in silico method extraction using text-mining. This should have future implications in community best practice, both for resource and method selection.

De senaste åren har trenden att använda en second screen i samband med tv-tittande ökat till följd av den ökade användningen av smartphones och surfplattor. Det ökade second screen-användandet medför att innehållet som visas på tv får mindre uppmärksamhet av tittarna. En möjlig lösning för att motverka detta är så kallade programspecifika second screen-applikationer som syftar till att komplettera innehållet som visas på tv och ge tittaren ett mervärde. Denna rapport syftar till att identifiera användningsmönster för en programspecifik second screen-applikation som används under livesända ishockeymatcher samt vilka faktorer som påverkar mönstren. Applikationen består av ett flöde som innehåller inlägg relaterade till matchen, t.ex. kommentarer och videoklipp från sportprofiler samt frågor som tittaren kan besvara och därmed se andra tittares åsikter om olika situationer i matchen. Rapporten diskuterar även förslag på hur resultaten kan användas vid utformandet av flödesbaserade second screen-applikationer samt hanteringen av innehållet i flödet. För att identifiera användningsmönstren utfördes användarobservationer, direkt följda av kompletterade intervjuer, under fem direktsända ishockeymatcher med fem olika användare som ensamma tittade på en match. Resultaten av studien visar att användningsmönstren huvudsakligen påverkas av de olika delarna i sändningen (speltid, studioanalys och reklamavbrott) samt spänningen i matchen. De olika funktionerna i applikationen samt användandet av andra applikationer är också faktorer som påverkar användandet. Resultaten visar även att användarna värdesätter att känna samhörighet med andra som tittar på matchen när de tittar på en match ensam. / The trend of interacting with a second screen while watching TV has evolved over the past years with the increased usage of smartphones and tablets. One program genre in which second screen usage is common is sports. The increasing second screen usage has made it challenging to keep viewers engaged in the content being shown on the TV. A possible solution to this problem is program-specific second screen applications that serve to complement the TV content and give an added value to the viewer. This paper aims to identify usage patterns of a program-specific second screen application, used during broadcasts of live hockey games. The application consists of a feed that contains posts related to the game such as comments and videos posted by sport profiles and editors, and polls that the viewer can answer to see other viewers’ thoughts about different situations in the game. Moreover, the paper analyzes what factors affect the usage patterns and discusses how the findings could be used when developing and managing the content of a feed-based second screen application during a sports game. To identify the usage patterns of the application a triangulation approach was used. Five user observations, directly followed by complementing semi-structured interviews were conducted during five separate live hockey games broadcast on TV. The results show that the usage patterns of the application are mainly affected by the excitement and the different parts of the broadcast (game time, studio analysis and commercial breaks), the different features of the application and the usage of other applications. Moreover, the results highlight the importance of feeling connected to other remote viewers of the game through the application when watching a game alone.

In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately. / Ph. D. / In today’s highly competitive and deregulated electricity market, companies in the electricity power generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers’ electricity consumption behavior. This strategic information is vital in forecasting and managing the future electricity demand. This information is also very important in utility company’s long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of electric load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: I) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. II) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for categorizing the load profile data. III) The frequency domain load profile statistics and forecasting models. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately.

Submitted by Maicon Juliano Schmidt (maicons) on 2015-05-29T13:32:27Z No. of bitstreams: 1 Júlio Cezar Santos Pires_.pdf: 1421846 bytes, checksum: 20f1800d942c84a72b6cb04d163cc92f (MD5) / Made available in DSpace on 2015-05-29T13:32:27Z (GMT). No. of bitstreams: 1 Júlio Cezar Santos Pires_.pdf: 1421846 bytes, checksum: 20f1800d942c84a72b6cb04d163cc92f (MD5) Previous issue date: 2014-04-29 / CTIC/RNP - Centro de Pesquisa e Desenvolvimento em Tecnologias Digitais para informação e Comunicação / Com os constantes avanços tecnológicos, surgem novas tendências para prover uma base de serviços para a nova era da tecnologia da informação. Com isso, surgem novos paradigmas para sistemas distribuídos, como, por exemplo, a Computação em Nuvem (ou Cloud Computing), que possui como ideia base a disponibilização de recursos computacionais sob demanda por meio da Internet, permitindo, assim, a sua utilização em qualquer lugar e pelos mais diversos tipos de aplicações. Entre as principais características da computação em nuvem, tem-se a elasticidade, provisionamento de serviço e cobrança baseada na utilização efetiva dos recursos. Visando tornar estas características, na prática, possíveis, torna-se indispensável que a infraestrutura disponha de um sistema de monitoramento. Neste contexto, este trabalho apresenta o BSPMon, um sistema de monitoramento de recursos preditivo para aplicações paralelas em Cloud Computing. Com o objetivo de ter um controle fino sobre os recursos computacionais, o BSPMon coletará métricas de desempenho nos três níveis da infraestrutura: máquina física, máquina virtual e aplicação, efetuando, desta forma, um monitoramento hierárquico multinível dos recursos. De posse destas métricas de desempenho, o BSPMon efetuará predições sobre as demandas, visando melhores resultados para a tomada de decisão em situações de migração, previsão, controle sobre o SLA, provisionamento e consolidação dos recursos. O sistema proposto atuará no nível de middleware, de forma transparente para a aplicação. A partir das avaliações obtidas na predição, os resultados apontam baixa intrusividade na infraestrutura, eficiência energética e predições com taxa de acerto superior a 90%. / Due to constant technological advances, there are new trends to provide a service base for the new era of information technology. Thus, there are new paradigms for distributed systems, for example, Cloud Computing, which has as basic idea of the provision of computational resources on demand via the Internet, thus allowing their use anywhere and for many different types of applications. Among the main features of cloud computing, there is elasticity, service provisioning and billing based on the effective use of resources. In order to make these features in practice possible, it is essential that the infrastructure to have a monitoring system. In this context, this work presents the BSPMon, a monitoring system of predictive features for parallel applications in Cloud Computing. In order to have fine control over computing resources, the BSPMon collect performance metrics in three levels of infrastructure: physical machine, virtual machine and application, making thus a multilevel hierarchical resource monitoring. With such performance metrics, the BSPMon shall make predictions about the demands, to obtain better results for decision making in migration scenarios, prediction, control over the SLA, provisioning and consolidation of resources. The proposed system will operate in the middleware level, transparently to the application. From the evaluations obtained in the prediction, the results indicate low intrusiveness in infrastructure, energy efficiency and predictions accuracy rate above 90 %.

In South Africa, significant changes in health care have taken place since the first democratic elections in 1994. The change had lead to a position of integrated service delivery with specific reference to primary health care. Increasingly in developing countries, the private sector impacts significantly on the rights to education and the highest attainable standard of health. Inappropriate prescribing e.g. prescribing a drug without an acceptable indication, specifying an incorrect dosage, schedule or duration of treatment, duplicating therapeutic agents and prescribing drugs without adequate regard to potential interactions, can cause adverse outcomes, deplete health care resources, compromise the quality of care and possible increase in health costs. One approach monitoring prescribing practices is drug utilisation review. The general objective of this study was to review and interpret aspects of drug usage patterns in a private primary health care setting, with special reference to the top ten diagnoses made and the top twenty medicine items prescribed as well as the associated costs. A quantitative, retrospective drug utilisation review as well as certain aspects of managed and primary health care, pharmacoeconomics, pharmacoepidemiology, medicine formularies and standard treatment guidelines were reviewed in the literature as a base for the study. The results of the empirical study showed that 83648 patients consulted at the nine medicentres during the study period (1 January to 31 December 2001). A total number of 132591 patient visits (consultations) were made, 140723 medical conditions (diagnoses) performed and 516177 medicine items prescribed during the study period. Analysis of medicine usage patterns and associated costs of the top ten diagnoses made and top twenty medicine items prescribed in the study population, revealed the following: The top ten diagnoses determined accounted for 29.07% of the total number of diagnoses made, . a total medicine treatment cost accounting for 32.11% in the study population, . the top twenty medicine items determined accounted for 56.23% of the total medicine items prescribed and . a total medicine treatment cost accounting for 28.63% in the study population. The highest prevalence of diagnoses made and medicine items prescribed was found in age groups 4 and 5 (Le. patients between the ages of 19 to 40 years) and was also found to be more prevalent in the female than in the male population. In completion of the research, recommendations to review the medicentres medicine treatment protocols and on provision of primary health care education were made. Reference to the investigation of environmental factors is also made. / Thesis (M.Pharm.)--North-West University, Potchefstroom Campus, 2004.

In South Africa, significant changes in health care have taken place since the first democratic elections in 1994. The change had lead to a position of integrated service delivery with specific reference to primary health care. Increasingly in developing countries, the private sector impacts significantly on the rights to education and the highest attainable standard of health. Inappropriate prescribing e.g. prescribing a drug without an acceptable indication, specifying an incorrect dosage, schedule or duration of treatment, duplicating therapeutic agents and prescribing drugs without adequate regard to potential interactions, can cause adverse outcomes, deplete health care resources, compromise the quality of care and possible increase in health costs. One approach monitoring prescribing practices is drug utilisation review. The general objective of this study was to review and interpret aspects of drug usage patterns in a private primary health care setting, with special reference to the top ten diagnoses made and the top twenty medicine items prescribed as well as the associated costs. A quantitative, retrospective drug utilisation review as well as certain aspects of managed and primary health care, pharmacoeconomics, pharmacoepidemiology, medicine formularies and standard treatment guidelines were reviewed in the literature as a base for the study. The results of the empirical study showed that 83648 patients consulted at the nine medicentres during the study period (1 January to 31 December 2001). A total number of 132591 patient visits (consultations) were made, 140723 medical conditions (diagnoses) performed and 516177 medicine items prescribed during the study period. Analysis of medicine usage patterns and associated costs of the top ten diagnoses made and top twenty medicine items prescribed in the study population, revealed the following: The top ten diagnoses determined accounted for 29.07% of the total number of diagnoses made, . a total medicine treatment cost accounting for 32.11% in the study population, . the top twenty medicine items determined accounted for 56.23% of the total medicine items prescribed and . a total medicine treatment cost accounting for 28.63% in the study population. The highest prevalence of diagnoses made and medicine items prescribed was found in age groups 4 and 5 (Le. patients between the ages of 19 to 40 years) and was also found to be more prevalent in the female than in the male population. In completion of the research, recommendations to review the medicentres medicine treatment protocols and on provision of primary health care education were made. Reference to the investigation of environmental factors is also made. / Thesis (M.Pharm.)--North-West University, Potchefstroom Campus, 2004.