This article describes query processing in the DBO database system. Like other database systems designed for ad hoc analytic processing, DBO is able to compute the exact answers to queries over a large relational database in a scalable fashion. Unlike any other system designed for analytic processing, DBO can constantly maintain a guess as to the final answer to an aggregate query throughout execution, along with statistically meaningful bounds for the guess's accuracy. As DBO gathers more and more information, the guess gets more and more accurate, until it is 100% accurate as the query is completed. This allows users to stop the execution as soon as they are happy with the query accuracy, and thus encourages exploratory data analysis.

This paper describes query processing in the DBO database system. Like other database systems designed for ad-hoc, analytic processing, DBO is able to compute the exact answer to queries over a large relational database in a scalable fashion. Unlike any other system designed for analytic processing, DBO can constantly maintain a guess as to the final answer to an aggregate query throughout execution, along with statistically meaningful bounds for the guess's accuracy. As DBO gathers more and more information, the guess gets more and more accurate, until it is 100% accurate as the query is completed. This allows users to stop the execution at any time that they are happy with the query accuracy, and encourages exploratory data analysis.

One of the most common operations in analytic query processing is the application of an aggregate function to the result of a relational join. We describe an algorithm called the Sort-Merge-Shrink (SMS) Join for computing the answer to such a query over large, disk-based input tables. The key innovation of the SMS join is that if the input data are clustered in a statistically random fashion on disk, then at all times, the join provides an online, statistical estimator for the eventual answer to the query as well as probabilistic confidence bounds. Thus, a user can monitor the progress of the join throughout its execution and stop the join when satisfied with the estimate's accuracy or run the algorithm to completion with a total time requirement that is not much longer than that of other common join algorithms. This contrasts with other online join algorithms, which either do not offer such statistical guarantees or can only offer guarantees so long as the input data can fit into main memory.

We propose a method for finding seeds for the local alignment of two nucleotide sequences. Our method uses randomized algorithms to find approximate seeds. We present a dynamic index to store the fingerprints of k-grams and a highly scalable and accurate (HSA) algorithm to incorporate randomization into process of seed generation. Experimental results show that our method produces better quality seeds with improved running time and memory usage compared to traditional non-spaced and spaced seeds. The presented algorithm scales very well with higher seed lengths while maintaining the quality and performance.

One of the most common operations in analytic query processing is the application of an aggregate function to the result of a relational join. We describe an algorithm for computing the answer to such a query over large, disk-based input tables. The key innovation of our algorithm is that at all times, it provides an online, statistical estimator for the eventual answer to the query, as well as probabilistic confidence bounds. Thus, a user can monitor the progress of the join throughout its execution and stop the join when satisfied with the estimate's accuracy, or run the algorithm to completion with a total time requirement that is not much longer than other common join algorithms. This contrasts with other online join algorithms, which either do not offer such statistical guarantees or can only offer guarantees so long as the input data can fit into core memory.

Statistical estimation and approximate query processing have become increasingly prevalent applications for database systems. However, approximation is usually of little use without some sort of guarantee on estimation accuracy, or "confidence bound." Analytically deriving probabilistic guarantees for database queries over sampled data is a daunting task, not suitable for the faint of heart, and certainly beyond the expertise of the typical database system end-user. This paper considers the problem of incorporating into a database system a powerful "plug-in" method for computing confidence bounds on the answer to relational database queries over sampled or incomplete data. This statistical tool, called the bootstrap, is simple enough that it can be used by a data-base programmer with a rudimentary mathematical background, but general enough that it can be applied to almost any statistical inference problem. Given the power and ease-of-use of the bootstrap, we argue that the algorithms presented for supporting the bootstrap should be incorporated into any database system which is intended to support analytic processing.

Random sampling is one of the most fundamental data management tools available. However, most current research involving sampling considers the problem of how to use a sample, and not how to compute one. The implicit assumption is that a "sample" is a small data structure that is easily maintained as new data are encountered, even though simple statistical arguments demonstrate that very large samples of gigabytes or terabytes in size can be necessary to provide high accuracy. No existing work tackles the problem of maintaining very large, disk-based samples from a data management perspective, and no techniques now exist for maintaining very large samples in an online manner from streaming data. In this paper, we present online algorithms for maintaining on-disk samples that are gigabytes or terabytes in size. The algorithms are designed for streaming data, or for any environment where a large sample must be maintained online in a single pass through a data set. The algorithms meet the strict requirement that the sample always be a true, statistically random sample (without replacement) of all of the data processed thus far. Our algorithms are also suitable for biased or unequal probability sampling.