To support the continuously increasing number of mobile telephone users around the world, mobile communication systems have become more advanced and sophisticated in their designs. As a result of the great success with the second generation mobile radio networks, deployment of the third and development of fourth generations, the demand for higher data rates to support available services, such as internet connection, video telephony and personal navigation systems, is ever growing. To be able to meet the requirements regarding bandwidth and number of users, enhancements of existing systems and introductions of conceptually new technologies and techniques have been researched and developed. Although new proposed technologies in theory provide increased network capacity, the backbone of a successful roll-out of a mobile telephone system is inevitably the planning of the network’s cellular structure. Hence, the fundamental aspect to a reliable cellular planning is the knowledge about the physical radio channel for wide sets of different propagation scenarios. Therefore, to study radio wave propagation in typical Australian environments, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Australian Telecommunications Cooperative Research Centre (ATcrc) in collaboration developed a cellular code division multiple access (CDMA) pilot scanner. The pilot scanner measurement equipment enables for radio wave propagation measurements in available commercial CDMA mobile radio networks, which in Australia are usually deployed for extensive rural areas. Over time, the collected measurement data has been used to characterise many different types of mobile radio environments and some of the results are presented in this thesis. The thesis is divided into an introduction section and four parts based on peer-reviewed international research publications. The introduction section presents the reader with some relevant background on channel and propagation modelling. Also, the CDMA scanner measurement system that was developed in parallel with the research results founding this thesis is presented. The first part presents work on the evaluation and development of the different revisions of the Recommendation ITU-R P.1546 point-to-area radio wave propagation prediction model. In particular, the modified application of the terrain clearance angle (TCA) and the calculation method of the effective antenna height are scrutinized. In the second part, the correlation between the smallscale fading characteristics, described by the Ricean K-factor, and the vegetation density in the vicinity of the mobile receiving antenna is investigated. The third part presents an artificial neural network (ANN) based technique incorporated to predict path loss in rural macrocell environments. Obtained results, such as prediction accuracy and training time, are presented for different sized ANNs and different training approaches. Finally, the fourth part proposes an extension of the path loss ANN enabling the model to also predict small-scale fading characteristics.

隨著全球資訊網的發展，人們享受了資訊快速流通的便利，也造就了搜尋引擎的發展。針對學術文獻，ACM, IEEE等學術組織也將學術文獻數位化，並提供關鍵字查詢文獻的功能。此外，Google也發展了Google Scholar搜尋全球資訊網上的學術文獻。Google在回傳查詢結果時，除了考慮文獻內容與查詢關鍵字的相似度之外，也利用PageRank技術來考量文獻間的引用關係。但是，有時後使用者想查詢的是與查詢相關的重要參考文獻。這些文獻的內容與查詢未必有很高的相似度。 因此本論文的研究目的在研究並發展推薦重要參考文獻的技術。我們先利用蜘蛛程式( spider)與剖析程式( parser)擷取分析ACM Digital Library上所收錄的論文後設資料，並解析出論文篇名、作者、摘要、關鍵字、分類、參考文獻等論文的重要組成要素。接著利用Mixed Media Graph（MMG）以描述關鍵字與參考文獻間關係的MMG 模型。當使用者輸入關鍵字，利用MMG做random walk因此可以找出與輸入關鍵字相關性最高的參考文獻。 / The rapid development of the Internet, people enjoy the rapid flow of information to facilitate, but also created a search engine of development. ACM and IEEE have developed the digital libraries to provide literature search. Moreover, there exist some search engines for academic literature, such as Google Scholar. Google Scholar collects academic literatures from WWW and provides users the capability to query literatures by keywords. However, sometimes what users need is to search for important citations specified by authors, such as seminal survey papers or books. The aim of this thesis is to investigate and develop the mechanism for search for important citations. In the developed mechanism, first the spider crawls and collects the literature from ACM Digital Library. Then the parser parse and extract the meta information for each literature. The Mixed Media Graph is employed to capture the relationships between keywords and citations. Given a set of query keywords, the important citations are generated by random walk over the constructed Mixed Media Graph. Performance analysis shows that the proposed mechanism performs well.

Ce mémoire tente de répondre à une problématique très importante dans le domaine de recrutement : l’appariement entre offre d’emploi et candidats. Dans notre cas nous disposons de milliers d’offres d’emploi et de millions de profils ramassés sur les sites dédiés et fournis par un industriel spécialisé dans le recrutement. Les offres d’emploi et les profils de candidats sur les réseaux sociaux professionnels sont généralement destinés à des lecteurs humains qui sont les recruteurs et les chercheurs d’emploi. Chercher à effectuer une sélection automatique de profils pour une offre d’emploi se heurte donc à certaines difficultés que nous avons cherché à résoudre dans le présent mémoire. Nous avons utilisé des techniques de traitement automatique de la langue naturelle pour extraire automatiquement les informations pertinentes dans une offre d’emploi afin de construite une requête qui nous permettrait d’interroger notre base de données de profils. Pour valider notre modèle d’extraction de métier, de compétences et de d’expérience, nous avons évalué ces trois différentes tâches séparément en nous basant sur une référence cent offres d’emploi canadiennes que nous avons manuellement annotée. Et pour valider notre outil d’appariement nous avons fait évaluer le résultat de l’appariement de dix offres d’emploi canadiennes par un expert en recrutement. / Our work seeks to address a very important issue in the recruitment field: matching jobs postings and candidates. We have thousands of jobs postings and millions of profiles collected from internet provided by a specialized firm in recruitment. Job postings and candidate profiles on professional social networks are generally intended for human readers who are recruiters and job seekers. We use natural language processing (NLP) techniques to automatically extract relevant information in a job offer. We use the extracted information to build automatically a query on our database. To validate our information retrieval model of occupation, skills and experience, we use hundred Canadian jobs postings manually annotated. And to validate our matching tool we evaluate the result of the matching of ten Canadian jobs by a recruitment expert.

Cette recherche propose la création d’un premier modèle expliquant les relations entre les communautés virtuelles de consommation, leurs membres et leur écosystème virtuel de marques. À la différence des nombreux travaux sur le marketing tribal, concentrés sur les communautés de marque, l’auteur choisit d’axer son travail sur l’étude des communautés virtuelles non centrées autour d’une marque spécifique, permettant ainsi d’explorer pour la première fois divers cas de relations marque-communauté modulées par différentes variables telles que le niveau de congruence entre les valeurs d’une marque et celles de la communauté, le niveau d’intrusion et le niveau de contribution d’une marque dans la communauté. Outre l’identification de ces variables explicatives de la relation marque-communauté, cette recherche caractérise également l’ensemble des relations possibles entre marques et communautés tout en évaluant leur impact sur la relation individuelle marque-membre. Les résultats de l’étude de deux couples de marques testés dans deux communautés différentes montrent notamment que la recommandation d’une communauté à l’égard d’une marque a un impact positif sur la confiance d’un membre auprès de cette marque et que le dénigrement produit l’effet inverse. En revanche, contrairement à ce qui est observé dans les communautés de marque, le phénomène de co-création entre une marque et une communauté non marquée peut conduire à une érosion de la confiance d’un membre à l’égard de cette marque. La distinction entre communauté de marque et communauté « non marquée » est donc fondamentale dans l’étude de ces nouvelles structures sociétales. / This research explains the creation of a first model of relationships between virtual communities, their members and their brands virtual ecosystem. Unlike the many papers on tribal marketing, converging on brand communities, the author chooses to focus his work on the study of virtual communities not centered on a specific brand, allowing for the first time, the exploration of various cases of community-brand relationships modulated by several variables such as the congruence level between the values of the brand and those of the community, the contribution and the intrusion levels of the brand in the community. Besides the identification of these explanatory variables of the community-brand relationship, this research also describes all the possible relationships between brands and communities while assessing their impact on the individual relationship consumer-brand. The results of the study on two couples of brands tested on two different communities especially show that recommendation of a community about a brand has a positive impact on the member trust in this brand and that the denigration has the opposite effect. However, unlike what we observed in brand communities, the co-creation phenomenon between a brand and an “unbranded community” can lead to an erosion of the member trust in this brand. The distinction between brand community and “unbranded community” is therefore essential for the study of these new societal structures.

Dans notre société de plus en plus numérique, les systèmes de recommandation ont fait leur apparition dans le but de résoudre le problème bien connu de surcharge d'information. L'adoption des réseaux sociaux a permis l'émergence de systèmes intégrant les relations sociales dans leurs recommandations. Dans cette thèse, nous proposons un système de recommandation adapté aux architectures décentralisées pouvant être déployé sur des réseaux sociaux existants. L'utilisateur conserve son profil en local et ne communique qu'avec un ensemble restreint d'utilisateurs de confiance, avec qui il accepte de partager ses données. Nous prenons en compte le réseau social de l'utilisateur afin de construire le réseau de pairs. La similarité des amis est prise en compte pour pondérer les liens. Les recommandations sont propagées dans le réseau, passant d'amis en amis jusqu'à atteindre l'utilisateur désiré. Ainsi seuls les amis directs communiquent entre eux. À partir de cette propagation, nous proposons plusieurs techniques. Tout d'abord, nous délivrons à l'utilisateur final une confiance du système dans la fiabilité de la recommandation. Ceci lui permet de choisir parmi les produits sélectionnés, lesquels semblent effectivement les plus pertinents pour lui. Cette confiance est calculée sur plusieurs critères, tels que la variation des recommandations des amis, leur nombre, la similarité et la fraîcheur de la recommandation. Ensuite, nous définissons des heuristiques adaptant notre approche aux systèmes pair-à-pair. Dans de telles architectures, le réseau est une ressource critique et ne doit pas être constamment surchargé. Ces heuristiques limitent la consommation réseau de notre approche tout en fournissant des recommandations pertinentes à l'utilisateur. Enfin, nous proposons plusieurs stratégies de score par défaut, dans le cas où aucun score n'est calculable, prenant en compte les contraintes en terme d'accès à l'information par le système. Nous comparons notre approche avec des approches classiques de recommandation, de filtrage collaboratif ou basées sur la confiance, en utilisant plusieurs jeux de données existants, tels qu'Epinions et Flixster, ainsi que deux jeux de données que nous avons construits nous-même. Nous montrons qu'une approche purement locale, associée à des stratégies de score par défaut, offre de meilleurs résultats que la plupart des autres approches, notamment en ce qui concerne les "cold start users". / Recommender systems are widely used to achieve a constantly growing variety of services. Alongside with social networks, recommender systems that take into account friendship or trust between users have emerged. In this thesis, we propose an evolution of trust-based recommender systems adapted to decentralized architectures that can be deployed on top of existing social networks. Users profiles are stored locally and are exchanged with a limited, user-defined, list of trusted users. Our approach takes into account friends' similarity and propagates recommendation to direct friends in the social network in order to prevent ratings from being globally known. Moreover, the computational complexity is reduced since calculations are performed on a limited dataset, restricted to the user's neighborhood. On top of this propagation, our approach investigates several aspects. Our system computes and returns to the final user a confidence on the recommendation. It allows the user to tune his/her choice from the recommended products. Confidence takes into account friends' recommendations variance, their number, similarity and freshness of the recommendations. We also propose several heuristics that take into account peer-to-peer constraints, especially regarding network flooding. We show that those heuristics decrease network resources consumption without sacrificing accuracy and coverage. We propose default scoring strategies that are compatible with our constraints. We have implemented and compared our approach with existing ones, using multiple datasets, such as Epinions and Flixster. We show that local information with default scoring strategies are sufficient to cover more users than classical collaborative filtering and trust-based recommender systems. Regarding accuracy, our approach performs better than others, especially for cold start users, even if using less information.

Dans de nombreux domaines, les nouvelles technologies d'acquisition de l'information ou encore de mesure (e.g. serres de phénotypage robotisées) ont engendré une création phénoménale de données. Nous nous appuyons en particulier sur deux cas d'application réels: les observations de plantes en botanique et les données de phénotypage en biologie. Cependant, nos contributions peuvent être généralisées aux données du Web. Par ailleurs, s'ajoute à la quantité des données leur distribution. Chaque utilisateur stocke en effet ses données sur divers sites hétérogènes (e.g. ordinateurs personnels, serveurs, cloud), données qu'il souhaite partager. Que ce soit pour les observations de botanique ou pour les données de phénotypage en biologie, des solutions collaboratives, comprenant des outils de recherche et de recommandation distribués, bénéficieraient aux utilisateurs. L'objectif général de ce travail est donc de définir un ensemble de techniques permettant le partage et la découverte de données, via l'application d'approches de recherche et de recommandation, dans un environnement distribué (e.g. sites hétérogènes).Pour cela, la recherche et la recommandation permettent aux utilisateurs de se voir présenter des résultats, ou des recommandations, à la fois pertinents par rapport à une requête qu'ils auraient soumise et par rapport à leur profil. Les techniques de diversification permettent de présenter aux utilisateurs des résultats offrant une meilleure nouveauté tout en évitant de les lasser par des contenus redondants et répétitifs. Grâce à la diversité, une distance entre toutes les recommandations est en effet introduite afin que celles-ci soient les plus représentatives possibles de l'ensemble des résultats pertinents. Peu de travaux exploitent la diversité des profils des utilisateurs partageant les données. Dans ce travail de thèse, nous montrons notamment que dans certains scénarios, diversifier les profils des utilisateurs apporte une nette amélioration en ce qui concerne la qualité des résultats~: des sondages montrent que dans plus de 75% des cas, les utilisateurs préfèrent la diversité des profils à celle des contenus. Par ailleurs, afin d'aborder les problèmes de distribution des données sur des sites hétérogènes, deux approches sont possibles. La première, les réseaux P2P, consiste à établir des liens entre chaque pair (noeud du réseau): étant donné un pair p, ceux avec lesquels il a établi un lien représentent son voisinage. Celui-ci est utilisé lorsque p soumet une requête q, pour y répondre. Cependant, dans les solutions de l'état de l'art, la redondance des profils des pairs présents dans les différents voisinages limitent la capacité du système à retrouver des résultats pertinents sur le réseau, étant donné les requêtes soumises par les utilisateurs. Nous montrons, dans ce travail, qu'introduire de la diversité dans le calcul du voisinage, en augmentant la couverture, permet un net gain en termes de qualité. En effet, en tenant compte de la diversité, chaque pair du voisinage a une plus forte probabilité de retourner des résultats nouveaux à l'utilisateur courant: lorsqu'une requête est soumise par un pair, notre approche permet de retrouver jusqu'à trois fois plus de bons résultats sur le réseau. La seconde approche de la distribution est le multisite. Généralement, dans les solutions de l'état de l'art, les sites sont homogènes et représentés par de gros centres de données. Dans notre contexte, nous proposons une approche permettant la collaboration de sites hétérogènes, tels que de petits serveurs d'équipe, des ordinateurs personnels ou de gros sites dans le cloud. Un prototype est issu de cette contribution. Deux versions du prototype ont été réalisées afin de répondre aux deux cas d'application, en s'adaptant notamment aux types des données. / In many fields, novel technologies employed in information acquisition and measurement (e.g. phenotyping automated greenhouses) are at the basis of a phenomenal creation of data. In particular, we focus on two real use cases: plants observations in botany and phenotyping data in biology. Our contributions can be, however, generalized to Web data. In addition to their huge volume, data are also distributed. Indeed, each user stores their data in many heterogeneous sites (e.g. personal computers, servers, cloud); yet he wants to be able to share them. In both use cases, collaborative solutions, including distributed search and recommendation techniques, could benefit to the user.Thus, the global objective of this work is to define a set of techniques enabling sharing and discovery of data in heterogeneous distributed environment, through the use of search and recommendation approaches.For this purpose, search and recommendation allow users to be presented sets of results, or recommendations, that are both relevant to the queries submitted by the users and with respect to their profiles. Diversification techniques allow users to receive results with better novelty while avoiding redundant and repetitive content. By introducing a distance between each result presented to the user, diversity enables to return a broader set of relevant items.However, few works exploit profile diversity, which takes into account the users that share each item. In this work, we show that in some scenarios, considering profile diversity enables a consequent increase in results quality: surveys show that in more than 75% of the cases, users would prefer profile diversity to content diversity.Additionally, in order to address the problems related to data distribution among heterogeneous sites, two approaches are possible. First, P2P networks aim at establishing links between peers (nodes of the network): creating in this way an overlay network, where peers directly connected to a given peer p are known as his neighbors. This overlay is used to process queries submitted by each peer. However, in state of the art solutions, the redundancy of the peers in the various neighborhoods limits the capacity of the system to retrieve relevant items on the network, given the queries submitted by the users. In this work, we show that introducing diversity in the computation of the neighborhood, by increasing the coverage, enables a huge gain in terms of quality. By taking into account diversity, each peer in a given neighborhood has indeed, a higher probability to return different results given a keywords query compared to the other peers in the neighborhood. Whenever a query is submitted by a peer, our approach can retrieve up to three times more relevant items than state of the art solutions.The second category of approaches is called multi-site. Generally, in state of the art multi-sites solutions, the sites are homogeneous and consist in big data centers. In our context, we propose an approach enabling sharing among heterogeneous sites, such as small research teams servers, personal computers or big sites in the cloud. A prototype regrouping all contributions have been developed, with two versions addressing each of the use cases considered in this thesis.

Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications. / Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real.

Made available in DSpace on 2016-04-04T18:31:34Z (GMT). No. of bitstreams: 1 Alan Menk dos Santos.pdf: 6433244 bytes, checksum: d8118b5fa4198a1f3792738316afd65a (MD5) Previous issue date: 2012-12-03 / With the implementation of the Brazilian Digital TV System (SBTVD) comes a range of new opportunities and possibilities both for viewer and TV stations. For the viewers, they will have an immense amount of channels, programs and interactive advertisements. For TV stations, it increases the possibility of advertising in new media. In this context, the opportunity arises for a recommendation system for applications and interactivity portals. This dissertation presents a proposal of advertising personalization into applications and portals of digital TV environment in order to bring a better experience to the viewer, a new form of income for the broadcasters and also a greater acceptance of specialized products for use. This work develops an application for interactive Digital TV called Smart Marketing capable of capturing viewer navigation data through both implicit and explicit means by performing customized advertising from the process of knowledge discovery. Developed from AstroTV middleware, compatible with the Brazilian specification, its application was evaluated by means of experiment that used varied user profiles, applying into the generated database the process of knowledge discovery, which used tasks of classification and grouping. The results indicated the quality of the recommendation generated by Smart Marketing. / Com a implanta??o do Sistema Brasileiro de TV Digital (SBTVD), inicia-se uma gama de novas oportunidades e possibilidades tanto para o telespectador quanto as emissoras de TV. Para os Telespectadores, eles ter?o uma imensa quantidade de canais, programas e propagandas interativas. Para as emissoras de TV, aumenta a possibilidade de propagandas em novos meios de comunica??o. Neste contexto, surge a oportunidade de um sistema de recomenda??o para os aplicativos e portais de interatividade. Esta disserta??o apresenta uma proposta de personaliza??o de propaganda em aplicativos e portais do ambiente de TV Digital com o objetivo de trazer uma melhor experi?ncia ao telespectador, uma nova forma de obten??o de recursos por parte das teledifusoras e tamb?m uma maior aceita??o de produtos especializados, para uso. Este trabalho desenvolve um aplicativo para a TV Digital interativa denominado Smart Marketing capaz de capturar os dados de navega??o do telespectador tanto por meio impl?cito quanto explicito, realizando a apresenta??o de publicidades personalizadas a partir do processo de descoberta do conhecimento. Elaborado a partir do middleware AstroTV, compat?vel com a especifica??o brasileira, sua aplica??o foi avaliada por meio do experimento que se utilizou, de usu?rios com perfis variados, aplicando na base de dados gerada o processo de descoberta de conhecimento, o qual utilizou-se das tarefas de classifica??o e agrupamento. Os resultados obtidos indicaram a qualidade da recomenda??o gerada pelo Smart Marketing.

Submitted by William Justo Figueiro (williamjf) on 2015-06-18T23:11:45Z No. of bitstreams: 1 33.pdf: 1765508 bytes, checksum: d921fbdef8015531446e414c52c66bf9 (MD5) / Made available in DSpace on 2015-06-18T23:11:45Z (GMT). No. of bitstreams: 1 33.pdf: 1765508 bytes, checksum: d921fbdef8015531446e414c52c66bf9 (MD5) Previous issue date: 2012 / Nenhuma / Este trabalho elabora de uma arquitetura de software que contempla dentro de dispositivos móveis na plataforma Android, a coleta de informações de contexto físico de localização (informações que são apenas coletadas em ambientes externos) e geração de contexto lógico de localização (informações que precisam de um processamento dos dados para ser encontradas em ambientes internos), estas informações são armazenadas em uma estrutura Web Semântica a qual sofrerá inferências para gerar mais um contexto lógico de recomendação de uso de recursos disponíveis no dispositivo móvel e anteriormente utilizados pelo usuário em um dado instante e local. A funcionalidade desta arquitetura será verificada com a construção de um protótipo na plataforma Android. Um dos desafios deste trabalho será coletar o contexto lógico de localização do dispositivo em locais internos, como prédios e casas, onde a intensidade do sinal do sistema de posicionamento global (GPS) é insuficiente para ser identificada, portanto neste trabalho será utilizado sensores acelerômetro e giroscópio presentes nos dispositivos móveis para calcular seu deslocamento. A localização interna será integrada a localização externa, formando um percurso contínuo. As informações coletadas no contexto físico são armazenadas em uma ontologia dentro do dispositivo móvel e sincronizadas com um servidor remoto. Outro desafio deste trabalho é o desenvolvimento de um agente de software que através dos dados armazenados na ontologia local, faz inferências nos dados armazenados na forma de Web Semântica e disponibiliza recomendações de uso de um determinado recurso, fundamentado apenas nos dados históricos de utilização destes recursos, relacionando a aproximação em determinado local com a frequência no tempo em relação ao mesmo horário do dia ou ao mesmo dia da semana e ao mesmo dia do mês. O armazenamento do contexto coletado, em uma estrutura Web Semântica, possibilita a união destas informações com demais informações coletadas de outros dispositivos contendo contextos que caracterizem um equipamento, um indivíduo ou uma sociedade. O resultado esperado da arquitetura apresentada neste trabalho, será o maior grau possível de precisão na posição geográfica identificada e a coerência das recomendações de uso de recursos disponíveis no dispositivo móvel em um dado instante e local. / This paper elaborates a software architecture that addresses within mobile devices on the Android platform, collecting information from the physical context of location (only information that is collected outdoors) and generation of logical context of location (information they need processing of the data to be found indoors) and stores this information in a Semantic Web structure which suffer inferences to generate a context logical of recommendation to use resources available on the mobile device and used previously by the user at a given time and local. The functionality of this architecture will be test by construction a prototype on the Android platform. One of the challenges of this work will be to collect the context of logical device location in indoor locations such as buildings and houses where the signal strength of the Global Positioning System (GPS) is insufficient to be identified, so this work will be used and accelerometer sensors gyroscope present in mobile devices to calculate your speed and direction. The location will be integrated inside the external location, forming a continuous path. The information collected in the physical context is stored in the ontology within the mobile device and synchronized with a remote server. Another challenge of this work is the development of a software agent that through data stored in the ontology on device, makes inferences on the data stored in the form of Web Semantic and provides recommendations for use of a given resource, based only on historical data of these resources by relating the approach in a certain place with the frequency in time over the same time of day or the same day of the week and the same day of the month. The architecture of this work is being called and Context Manager is integrated with the other two studies did not present this work: a Semantic Desktop with the task of identifying a resource that is being used to send and manager context; and Context's Federation, serving as a remote server, with the task of receiving context data collected by the context manager. The storage of context collected in a Web Semantic structure enables the union of this information with other context information that characterize a device, an individual or a society. The expected outcome of the architecture presented here will be the greatest possible degree of accuracy in the identified geographical position and consistency of recommendations for the use of resources available on the mobile device at a given time and place.

Submitted by William Justo Figueiro (williamjf) on 2015-07-02T23:41:55Z No. of bitstreams: 1 19.pdf: 3284339 bytes, checksum: 699295cfa668a175206d937e3cc57d7a (MD5) / Made available in DSpace on 2015-07-02T23:41:55Z (GMT). No. of bitstreams: 1 19.pdf: 3284339 bytes, checksum: 699295cfa668a175206d937e3cc57d7a (MD5) Previous issue date: 2014-03-31 / Nenhuma / A necessidade do professor buscar e selecionar materiais educacionais adequados para sua turma é um fato comum no meio educacional. Entretanto, a grande disponibilidade de materiais, a heterogeneidade dos perfis dos alunos e a diversidade de atividades pedagógicas que podem ser realizadas, tornam esta tarefa bastante trabalhosa. Neste cenário, este trabalho apresenta um modelo de recomendação ubíqua de conteúdo educacional para grupo de aprendizes, que visa auxiliar o professor no processo de busca e seleção de materiais educacionais levando em conta os perfis dos alunos e o contexto onde eles estão inseridos. A estratégia adotada neste trabalho se diferencia dos trabalhos relacionados por efetuar a recomendação de materiais educacionais considerando de forma conjunta os perfis de um grupo de aprendizes e o contexto no qual eles se encontram. Com base em uma validação por cenários foi possível verificar a viabilidade do modelo, além de propor uma solução para o problema de pesquisa / The necessity of teachers to search and to select appropriate educational materials for their classes is a common fact in the educational environment. However, the wide availability of materials, the heterogeneity of the students’ profiles and the diversity of pedagogical activities that can be conducted, make this task laborious. In this scenario, this work presents a model for ubiquitous recommendation of educational content for groups of learners dynamically created, which aims to help teachers to search and to select educational materials taking into consideration the profile of the group and the teaching context. The strategy adopted in this work differs from related work by making the recommendation of educational content considering jointly the profiles of a group of learners and context in which they find themselves. Based on validation scenarios, it was possible to verify the feasibility of the model, and it was proposed a solution to the research problem.