Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaf 104). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 364-373). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 103-108). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2003. / Typescript. Vita. Includes bibliographical references (leaves 103-108). Also available on the Internet.

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010. / Committee Chair: Dellaert, Frank; Committee Co-Chair: Rehg, James; Committee Member: Bobick, Aaron; Committee Member: Essa, Irfan; Committee Member: Smyth, Padhraic. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Concepción is the most active composite volcano in Nicaragua, and is located on Ometepe Island, within Lake Nicaragua. Moderate to small volcanic explosions with a volcanic explosivity index (VEI) of 1-2 have been characteristic of this volcano during the last four decades. Although its current activity is not violent, its volcanic deposits reveal stages of violent activity involving Plinian and sub-Plinian eruptions that deposited vast amounts of volcanic tephra in the Atlantic Ocean. These observations, together with the 31,000 people living on the island, make Concepción volcano an important target for volcanological research. My research focuses on the investigation of the stability of the volcano edifice of Concepción, using geophysical data such as gravity, geodetic global positioning system (GPS), sulphur dioxide (SO2) flux, real-time seismic amplitude (RSAM), and satellite remotely-sensed data. The integration of these data sets provides information about the short-term behavior of Concepción, and some insights into the volcano's long-term behavior. This study has provided, for the first time, information about the shallow dynamics of Concepción on time scales of days to weeks. I furnish evidence that this volcano is not gravitationally spreading in a continuous fashion as previously thought, that its bulk average density is comparable to that of a pile of gravel, that the volcano edifice is composed of two major distinctive lithologies, that the deformation field around the volcano is recoverable in a matter of days, and that the deformation source is located in the shallow crust. This source is also degassing through the relatively open magmatic conduit. There are, however, several remaining questions. Although the volcano is not spreading continuously there is the possibility that gravitational spreading may be taking place in a stick-slip fashion. This has important implications for slope stability of the volcano, and the associated hazards. The factors influencing the long term slope stability of the volcano are still not fully resolved, but internal volcanic processes and anthropogenic disturbances appear to be the major factors.

Nowadays we are living in an era of rich data, not only in the sense of the amount of data, but also in the sense of various sources and content of data. Efficient search, management, and exploitation of data have, over decades, been a major direction of database research. In this thesis, three challenging problems are proposed and studied, targeting (i) time series data, (ii) user preference data, and (iii) location-based social network data, respectively, providing efficient solutions to corresponding real-life applications. First, durability queries are studied in historical time series databases, which identify objects that have durable quality over time. For example, a sociologist may be interested in the top 10 web search terms during the period of some historical events; the police may seek for vehicles that move close to a suspect 70% of the time during a certain time, etc. Such durable top-k (DTop-k) and durable k-nearest neighbor (DkNN) queries can be viewed as natural extensions of the standard snapshot top-k and NN queries to timestamped sequences of values or locations. Although their snapshot counterparts have been studied extensively, there is little prior work that addresses this new class of durability queries. Efficient and scalable algorithms are proposed based on novel indexing techniques. Next, an efficient solution to k-nearest neighbor search over top-m lists is investigated. A top-m list is a ranking of m items, typically representing some user’s preference over these items. For example, a user may have a list of her 10 most favourite books; the result from a search engine is typically a list of webpages ranked according to their relevance to some keywords. The search problem aims at extracting k top-m lists from the database that are the “closest” to some query list where the closeness is evaluated using commonly used measures such as the Fagin’s intersection metric, Spearman’s footrule, Kendall’s tau, etc. Despite of the importance of such queries, there’s little prior work suggesting any efficient solution. In this thesis, a unified framework is proposed to answer such queries efficiently. Finally, the problem of top-N venue recommendation in location-based social networks (LBSNs) is studied, which recommends new venues to users. As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model. Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this thesis, effective and efficient algorithms that create recommendations are proposed based on such properties. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

published_or_final_version / abstract / toc / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Statistics / Master / Master of Philosophy