Universiẗat, Diss.--Bremen, 2006.

Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009. / Committee Chair: Ramachandran, Umakishore; Committee Member: Ahamad, Mustaque; Committee Member: Edwards, Keith; Committee Member: Liu, Ling; Committee Member: Suh, Sang-bum

The rapid development of mobile technologies has facilitated users to generate and store files on mobile devices. However, it has become a challenging issue for users to search efficiently and effectively for files of interest in a mobile environment that involves a large number of mobile nodes. In this thesis, file management and retrieval alternatives have been investigated to propose a feasible framework that can be employed on resource-limited devices without altering their operating systems. The file annotation and retrieval framework (FARM) proposed in the thesis automatically annotates the files with their basic file attributes by extracting them from the underlying operating system of the device. The framework is implemented in the JME platform as a case study. This framework provides a variety of features for managing the metadata and file search features on the device itself and on other devices in a networked environment. FARM not only automates the file-search process but also provides accurate results as demonstrated by the experimental analysis. In order to facilitate a file search and take advantage of the Semantic Web Technologies, the SemFARM framework is proposed which utilizes the knowledge of a generic ontology. The generic ontology defines the most common keywords that can be used as the metadata of stored files. This provides semantic-based file search capabilities on low-end devices where the search keywords are enriched with additional knowledge extracted from the defined ontology. The existing frameworks annotate image files only, while SemFARM can be used to annotate all types of files. Semantic heterogeneity is a challenging issue and necessitates extensive research to accomplish the aim of a semantic web. For this reason, significant research efforts have been made in recent years by proposing an enormous number of ontology alignment systems to deal with ontology heterogeneities. In the process of aligning different ontologies, it is essential to encompass their semantic, structural or any system-specific measures in mapping decisions to produce more accurate alignments. The proposed solution, in this thesis, for ontology alignment presents a structural matcher, which computes the similarity between the super-classes, sub-classes and properties of two entities from different ontologies that require aligning. The proposed alignment system (OARS) uses Rough Sets to aggregate the results obtained from various matchers in order to deal with uncertainties during the mapping process of entities. The OARS uses a combinational approach by using a string-based and linguistic-based matcher, in addition to structural-matcher for computing the overall similarity between two entities. The performance of the OARS is evaluated in comparison with existing state of the art alignment systems in terms of precision and recall. The performance tests are performed by using benchmark ontologies and the results show significant improvements, specifically in terms of recall on all groups of test ontologies. There is no such existing framework, which can use alignments for file search on mobile devices. The ontology alignment paradigm is integrated in the SemFARM to further enhance the file search features of the framework as it utilises the knowledge of more than one ontology in order to perform a search query. The experimental evaluations show that it performs better in terms of precision and recall where more than one ontology is available when searching for a required file.

St. Gallen, Univ., Diss., 2007.

Zugl.: Saarbrücken, Univ., Diss., 2006

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 149-157).

Thesis (M.Sc.)--Victoria University of Wellington, 2009. / Includes bibliographical references.

Made available in DSpace on 2015-03-05T14:01:22Z (GMT). No. of bitstreams: 0 Previous issue date: 19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O uso crescente de dispositivos móveis e a ampla difusão de redes sem fio vêm estimulando as pesquisas relacionadas com computação móvel e ubíqua. Neste contexto, a educação vem sendo considerada uma das principais áreas de aplicação. O acompanhamento da mobilidade do aprendiz, através de sistemas de localização e o suporte à ciência de contexto, possibilitam a descoberta de novas oportunidades educacionais. Atualmente existem diversas propostas para a organização de ambientes de educação ubíqua, embora a maioria utilize uma arquitetura centralizada. Essa abordagem, apesar de trazer benefícios como a facilidade de acesso aos dados dos aprendizes, tem desvantagens intrínsecas ao seu modelo, como a preocupação com a escalabilidade e a separação física entre o aprendiz e os seus dados de perfil. Como alternativa às abordagens centralizadas, este trabalho propõe um modelo de colaboração para ambientes descentralizados, que, baseado no modelo sócio-interacionista e na abordagem de agentes, auxilia no processo de a / The recent use of mobile devices and the widespread adoption of wireless computer networks have stimulated mobile and ubiquitous computing research. In this context, education is being considered one of the main application areas. The ability to accurately track the learner’s position through a location system and the context awareness support allow the discovery of new pedagogical opportunities. Nowadays, there are several approaches to the organization of ubiquitous computing environments, even if the majority of them employ a centralized architecture. This approach brings some benefits, like easing the access to the learner profiles, but it also has some disadvantages intrinsic to the model, like scalability concerns and the physical separation between the learners and their profile data. This work proposes a collaboration model created for decentralized environments, capable of improving the learning process through the stimulus to the interaction between learners. This approach is modeled using agents, e

Zugl.: Saarbrücken, Univ., Diss., 2006

Context-aware computing aims at making mobile devices sensitive to the social and physical settings in which they are used. A necessary requirement to achieve this goal is to enable those devices to establish a shared understanding of the desired settings. Establishing a shared understanding entails the need to manipulate sensed data in order to capture a real world situation wholly, conceptually, and meaningfully. Quite often, however, the data acquired from sensors can be inexact, incomplete, and/or uncertain. Inexact sensing arises mostly due to the inherent limitation of sensors to capture a real world phenomenon precisely. Incompleteness is caused by the absence of a mechanism to capture certain real-world aspects; and uncertainty stems from the lack of knowledge about the reliability of the sensing sources, such as their sensing range, accuracy, and resolution. The thesis identifies a set of criteria for a context-aware system to capture dynamic real-world situations. On the basis of these criteria, a distributed architecture is designed, implemented and tested. The architecture consists of Primitive Context Servers, which abstract the acquisition of primitive contexts from physical sensors; Aggregators, to minimise error caused by inconsistent sensing, and to gather correlated primitive contexts pertaining to a particular entity or situation; a Knowledge Base and an Empirical Ambient Knowledge Component, to model dynamic properties of entities with facts and beliefs; and a Composer, to reason about dynamic real-world situations on the basis of sensed data. Two additional components, namely, the Event Handler and the Rule Organiser, are responsible for dynamically generating context rules by associating decision events ? signifying a user?s activity ? with the context in which those decision events are produced. Context-rules are essential elements with which the behaviour of mobile devices can be controlled and useful services can be provided. Four estimation and recognition schemes, namely, Fuzzy Logic, Hidden Markov Models, Dempster-Schafer Theory of Evidence, and Bayesian Networks, are investigated, and their suitability for the implementation of the components of the architecture of the thesis is studied. Subsequently, fuzzy sets are chosen to model dynamic properties of entities. Dempster-Schafer?s combination theory is chosen for aggregating primitive contexts; and Bayesian Networks are chosen to reason about a higher-level context, which is an abstraction of a real-world situation. A Bayesian Composer is implemented to demonstrate the capability of the architecture in dealing with uncertainty, in revising the belief of the Empirical Ambient Knowledge Component, in dealing with the dynamics of primitive contexts and in dynamically defining contextual states. The Composer could be able to reason about the whereabouts of a person in the absence of any localisation sensor. Thermal, relative humidity, light intensity properties of a place as well as time information were employed to model and reason about a place. Consequently, depending on the variety and reliability of the sensors employed, the Composer could be able to discriminate between rooms, corridors, a building, or an outdoor place with different degrees of uncertainty. The Context-Aware E-Pad (CAEP) application is designed and implemented to demonstrate how applications can employ a higher-level context without the need to directly deal with its composition, and how a context rule can be generated by associating the activities (decision events) of a mobile user with the context in which the decision events are produced.