This dissertation, "Advanced Query Processing on Spatial Networks" by Man-lung, Yiu, 姚文龍, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled "Advanced Query Processing on Spatial Networks" Submitted by Man Lung Yiu for the degree of Doctor of Philosophy at the University of Hong Kong in February 2006 Recent advances in GPS and mobile communication technologies have al- lowedapplicationstoemergethatcanaccessandexploitlocationinformation about (moving) objects on road networks. Location-based services enable car drivers to search for facilities such as restaurants, shops, and car-parks close to their route. Logistic services monitor the status of delivery vehi- cles and ensure the timely delivery of goods. In this class of applications, both the accessibility and location of objects (e.g., vehicles and facilities) are constrained by the underlying network. The actual distance between two objects is defined by their shortest path distance on the network rather than their Euclidean distance. These network constraints significantly increase the complexity of retrieving spatial query results. Thus, query processing on spatial networks (i.e., road networks) has received considerable attention from database researchers in recent years. In this thesis, we identify three interesting problems and study their eval- uation in the context of spatial networks: (i) aggregate nearest neighbor (ANN) query, (ii) reverse nearest neighbor (RNN) query, and (iii) cluster- ing. Our findings for (i) and (ii) provide meaningful results for end-users, while our results for (iii) provide effective data exploration tools for data analysts. Aggregate nearest neighbor (ANN) queries are generalized from the nearest neighbor problem, allowing a group of mobile users to express individual preferences for reaching the best overall facility (e.g., a restau- rant). Reverse nearest neighbor (RNN) queries are relevant to applications in decision support and resource allocation, enabling users to retrieve data objects locationally influenced by a query object. Clustering can be applied to discover dense collections of data objects, indicating regions of special interest. The process of computing results for these problems on spatial networks is complicated by the shortest path definition of the distance between two ob- jects. Naive evaluation methods may lead to numerous expensive network distance computations, and may not scale well for large networks and large datasets. Our main research objective is the design of appropriate opti- mization techniques for the proposed problems, that incur low I/O cost ofaccessing the spatial network. We also investigate several variants of these problems in order to expand the application scope of our proposed techniques. Variants of ANN queries include aggregate center queries and weighted queries. RNN queries have bichromatic and continuous variants. Clustering is also applicable with sev- eral grouping criteria. An abstract of exactly 388 words Signed Man Lung Yiu DOI: 10.5353/th_b3627936 Subjects: Nearest neighbor analysis (Statistics) Database management Cluster analysis