Thesis (Ph.D)--Stanford University, 1999. / Title from pdf t.p. (viewed May 9, 2002). "May 1999." "Adminitrivia V1/Prg/19990823"--Metadata.

Thesis (Ph.D.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 106-114)

Thesis (Ph.D.) -- University of Texas at Arlington, 2008.

Thesis (Ph. D.)--University of Queensland, 2002. / Includes bibliographical references.

Data replication is a key building block for large-scale distributed systems to improve availability, performance, and scalability. Because there is a fundamental trade-off between performance and consistency as well as between availability and consistency, systems must make trade-offs among these factors based on the demands and technologies of their target environments and workloads. Unfortunately, existing replication protocols and mechanisms are intrinsically entangled with specific policy assumptions. Therefore, to accommodate new trade-offs for new policy requirements, developers have to either build a new replication system from scratch or modify existing mechanisms. This dissertation presents a universal data replication architecture (URA) that cleanly separates mechanism and policy and supports Partial Replication (PR), Any Consistency (AC), and Topology Independence (TI) simultaneously. Our architecture yields two significant advantages. First, by providing a single set of mechanisms that capture the common underlying abstractions for data replication, URA can serve as a common substrate for building and deploying new replication systems. It therefore can significantly reduce the effort required to construct or modify a replication system. Second, by providing a set of general and flexible mechanisms independent of any specific policy, URA enables better trade-offs than any current system can provide. In particular, URA can simultaneously provide the three PRACTI properties while any existing system can provide at most two of them. Our experimental results and case-study systems confirm that universal data replication architecture is a way to build better replication systems and a better way to build replication systems. / text

Multidimensional data are ubiquitous and their efficient management and analysis is a core database research problem. There are lots of previous works focusing on indexing, analyzing and querying multidimensional data. In this dissertation, three challenging advanced analysis and join problems in multidimensional spaces are proposed and studied, providing efficient solutions to their related applications. First, the problem of generalized budget constrained optimization query (Gen-BOQ) is studied. In real life, it is often difficult for manufacturers to create new products dominating their competitors, due to some constraints. These constraints can be modeled by constraint functions, and the problem is then to decide the best possible regions in multidimensional spaces where the features of new products could be placed. Using the number of dominating and dominated objects, the profitability of these regions can be evaluated and the best areas are then returned. Although GenBOQ computation is challenging due to its high complexity, an efficient divide-and-conquer based framework is offered for this problem. In addition, an approximation method is proposed, making tradeoffs between the result quality and the query cost. Next, the efficient evaluation of all top-k queries (ATOPk) in multidimensional spaces is investigated, which compute the top ranked objects for a group of preference functions simultaneously. As an application of such a query, consider an online store, which needs to provide recommendations for a large number of users simultaneously. This problem is somewhat overlooked by past research; in this thesis, batch algorithms are proposed instead of naïvely evaluating top-k queries individually. Similar preferences are grouped together, and two algorithms are proposed, using block indexed nested loops and a view-based thresholding strategy. The optimized view-based threshold algorithm is demonstrated to be consistently the best. Moreover, an all top-k query helps to evaluate other queries relying on the results of multiple top-k queries, such as reverse top-k queries and top-m influential queries proposed in previous works. It is shown that applying the view-based approach to these queries can improve the performance of the current state-of-the-art by orders of magnitude. Finally, the problem of spatio-textual similarity joins (ST-SJOIN) on multidimensional data is considered. Given both spatial and textual information, ST-SJOIN retrieves pairs of objects which are both spatially close and textually similar. One possible application of this query is friendship recommendation, by matching people who not only live nearby but also share common interests. By combining the state-of-the-art strategies of spatial distance joins and set similarity joins, efficient query processing algorithms are proposed, taking both spatial and textual constraints into account. A batch processing strategy is also introduced to boost the performance, which is also effective for the original textual-only joins. Using synthetic and real datasets, it is shown that the proposed techniques outperform the baseline solutions. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

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