In this paper, we introduce dynamic histograms, which are constructed and maintained incrementally. We develop several dynamic histogram construction algorithms and show that they come close to static histograms in quality. Our experimental study covers a wide range of datasets and update patterns, including histogram maintenance in a shared-nothing environment. Building upon the insights offered by the dynamic algorithms, we also propose a new static histogram construction algorithm that is very fast and generates histograms that are close in quality to the highly accurate (but expensive to construct!) V-Optimal histograms. 1 Introduction The cost of executing a relational operator is a function of the sizes of the tuple streams that are input to the operator, which for intermediate operators are in turn determined by selectivities of the previous operators. The more complex a query is, the more important it is to have precise intermediate size estimates. Otherwise, errors in ...

A relation is an unordered collection of records. Often, however there is an underlying order (e.g., a sequence of stock prices), and users want to pose queries that reflect this order (e.g., find a weekly moving average). SQL provides no support for posing such queries. We show how a rich class of queries reflecting sort order can be naturally expressed and efficiently executed with simple extensions to SQL.

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentation of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework is being implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups.

DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentations of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework, implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups, makes several contributions. In particular, it combines support for extended relational queries with powerful data visualization features. Datasets much larger than available main memory can be handled—DEVise is currently being used to visualize datasets well in excess of 100MB—and data can be interactively examined at several levels of detail: all the way from meta-data summarizing the entire dataset, to large subsets of the actual data, to individual data records. Combining querying (in general, data processing) with visualizations gives us a very versatile tool, and presents several novel challenges.