No description available.

Thesis (M.S.)--Mississippi State University. Department of Mechanical Engineering. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2007. / Includes bibliographical references.

Includes bibliographical references (p. 147-159).

published_or_final_version / Computer Science / Master / Master of Philosophy

No description available.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 112-121).

Thesis (M.S.) in Spatial Information Science and Engineering--University of Maine, 2002. / Includes vita. Includes bibliographical references (leaves 101-106).

Includes bibliographical references (p. 185-196).

Nearest Neighbor (NN in short) queries are important in emerging applications, such as wireless networks, location-based services, and data stream applications, where the data obtained are often imprecise. The imprecision or imperfection of the data sources is modeled by uncertain data in recent research works. Handling uncertainty is important because this issue affects the quality of query answers. Although queries on uncertain data are useful, evaluating the queries on them can be costly, in terms of I/O or computational efficiency. In this thesis, we study how to efficiently evaluate NN queries on uncertain data. Given a query point q and a set of uncertain objects O, the possible nearest neighbor query returns a set of candidates which have non-zero probabilities to be the query answer. It is also interesting to ask \which region has the same set of possible nearest neighbors", and \which region has one specific object as its possible nearest neighbor". To reveal the relationship between the query space and nearest neighbor answers, we propose the UV-diagram, where the query space is split into disjoint partitions, such that each partition is associated with a set of objects. If a query point is located inside the partition, its possible nearest neighbors could be directly retrieved. However, the number of such partitions is exponential and the construction effort can be expensive. To tackle this problem, we propose an alternative concept, called UV-cell, and efficient algorithms for constructing it. The UV-cell has an irregular shape, which incurs difficulties in storage, maintenance, and query evaluation. We design an index structure, called UV-index, which is an approximated version of the UV-diagram. Extensive experiments show that the UV-index could efficiently answer different variants of NN queries, such as Probabilistic Nearest Neighbor Queries, Continuous Probabilistic Nearest Neighbor Queries. Another problem studied in this thesis is the trajectory nearest neighbor query. Here the query point is restricted to a pre-known trajectory. In applications (e.g. monitoring potential threats along a flight/vessel's trajectory), it is useful to derive nearest neighbors for all points on the query trajectory. Simple solutions, such as sampling or approximating the locations of uncertain objects as points, fails to achieve a good query quality. To handle this problem, we design efficient algorithms and optimization methods for this query. Experiments show that our solution can efficiently and accurately answer this query. Our solution is also scalable to large datasets and long trajectories. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy