The typical workload in a database system consists of a mix of multiple queries of different types, running concurrently and interacting with each other. The same query may have different performance in different mixes. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this dissertation, we demonstrate how queries affect each other when they are executing concurrently in different mixes. We show the significant impact that query interactions can have on the end-to-end workload performance. A major hurdle in the understanding of query interactions in database systems is that there is a large spectrum of possible causes of interactions. For example, query interactions can happen because of any of the resource-related, data-related or configuration-related dependencies that exist in the system. This variation in underlying causes makes it very difficult to come up with robust analytical performance models to capture and model query interactions. We present a new approach for modeling performance in the presence of interactions, based on conducting experiments to measure the effect of query interactions and fitting statistical models to the data collected in these experiments to capture the impact of query interactions. The experiments collect samples of the different possible query mixes, and measure the performance metrics of interest for the different queries in these sample mixes. Statistical models such as simple regression and instance-based learning techniques are used to train models from these sample mixes. This approach requires no prior assumptions about the internal workings of the database system or the nature or cause of the interactions, making it portable across systems. We demonstrate the potential of capturing, modeling, and exploiting query interactions by developing techniques to help in two database performance related tasks: workload scheduling and estimating the completion time of a workload. These are important workload management problems that database administrators have to deal with routinely. We consider the problem of scheduling a workload of report-generation queries. Our scheduling algorithms employ statistical performance models to schedule appropriate query mixes for the given workload. Our experimental evaluation demonstrates that our interaction-aware scheduling algorithms outperform scheduling policies that are typically used in database systems. The problem of estimating the completion time of a workload is an important problem, and the state of the art does not offer any systematic solution. Typically database administrators rely on heuristics or observations of past behavior to solve this problem. We propose a more rigorous solution to this problem, based on a workload simulator that employs performance models to simulate the execution of the different mixes that make up a workload. This mix-based simulator provides a systematic tool that can help database administrators in estimating workload completion time. Our experimental evaluation shows that our approach can estimate the workload completion times with a high degree of accuracy. Overall, this dissertation demonstrates that reasoning about query interactions holds significant potential for realizing performance improvements in database systems. The techniques developed in this work can be viewed as initial steps in this interesting area of research, with lots of potential for future work.

A materialized view is a derived data collecton stored in a database. When the source data for a materialized view is updated, the materialized view also needs to be updated. The process of updating a materialized view in response to changes in the source data is called view maintenance. There are two methods for maintaining a materialized view - recomputation and incremental computation. Recomputation computes the new view instance from scratch using the updated sources data. Incremental computation on the other hand, computes the new view instance by using the update to the source data, the old view instance, and possibly some source data. Incremental computation is widely accepted as a less expensive mathod of maintaining a view when the size of the update to the source data is small in relation to the size of the source data. / Thesis (PhD)--University of South Australia, 2000

No description available.

With the increasing usage of storing textual data into relational databases, there is a demand for the databases to support keyword queries over textual data. Due to the normalization and the inherent connections among tuples in different tables, traditional IR-style ranking and query evaluation methods do not apply. A number of systems have been proposed to deal with this issue. In this thesis, I will give a detailed demonstration and description to our SPARK project. In the project, we study both the effectiveness and the efficiency issues of answering top-k keyword query on a relational database system. We propose a new ranking formula by adapting existing IR techniques on a natural notion of ???virtual document???. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human being???s perception better. We also study efficient query processing methods based on the new ranking method, and propose algorithms that have minimal accesses to the database. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of both retrieval effectiveness and efficiency. We build a prototype of SPARK system on top of popular RDBMS based on these new techniques to satisfy different kinds of users and to support various query modes.

Thesis (M.S.)--Rochester Institute of Technology, 1989. / Includes bibliographical references.

Thesis (Ph. D.)--University of New South Wales, 1997. / Also available online.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 122-135). Also available in electronic version.

Report (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 54-56). Also available via the Internet.

Thesis (M.C.S.)--Carleton University, 2003. / Includes bibliographical references (p. 150-156). Also available in electronic format on the Internet.

Thesis (M.S.)--University of Wyoming, 2007. / Title from PDF title page (viewed on Feb. 11, 2009). Includes bibliographical references (p. 58-59).