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Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 60-62). / ix, 62 leaves, bound ill. 29 cm

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Smart-phones, wearables and mobile devices in general are the sensors of our modern world. Their sensing capabilities offer the means to analyze and interpret our behaviour and surroundings. When it comes to human behaviour, perhaps the most informative feature is our location and mobility habits. Insights from human mobility are useful in a number of everyday practical applications, such as the improvement of transportation and road network infrastructure, ride-sharing services, activity recognition, mobile data pre-fetching, analysis of the social behaviour of humans, etc. In this dissertation, we develop algorithms for processing mobility data. The analysis of mobility data is a non trivial task as it involves managing large quantities of location information, usually spread out spatially and temporally across many tracking sensors. An additional challenge in processing mobility information is to publish the data and the results of its analysis without jeopardizing the privacy of the involved individuals or the quality of the data. We look into a series of problems on processing mobility data from individuals and from a population. Our mission is to design algorithms with provable properties that allow for the fast and reliable extraction of insights. We present efficient solutions - in terms of storage and computation requirements - , with a focus on distributed computation, online processing and privacy preservation.

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The thesis deals with the theoretical and practical aspects of implementation of Business Intelligence in a field of geo-location data with emphasis on the design and implementation of data architecture and the development of dynamic dashboards. The whole issue is described on the development of a tool for the analysis of geo-location data, on which the author participated with other members of the Competence Center Retail Analytics - Ondrej Funiok, Vilem Kalus and Martin Matejka. The first part of the thesis describes the theoretical bases of Business and Location Intelligence, which is followed by a description of the basic tasks of working with geo-location data - data storage in relational database management systems, data processing by map servers and data presentation. Individual tasks are described with practical examples and use software tools to support these tasks. The next section presents the project of development tool for the analysis of geo-location data that is created in the Retail Analytics Competence Center at the University of Economics, Prague. The second part of the thesis describes the design and implementation of the data architecture of developed tool, which is followed by a description of the frontend portion of the tool consisting of the implementation of a library for creating dynamic Dashboards to present geo-location data.