Association rule mining is a valuable decision support technique that can be used to analyze customer preferences, buying patterns, and product correlations.Current systems are however handicapped by the long processing times required by mining algorithms that make them unsuitable for interactive use.In this paper, we propose the use of a knowledge cache that can reduce the response time by several orders of magnitude.Most of the performance gain comes from the idea of guaranteed support that allows us to completely eliminate database accesses in a large number of cases.Using this cache, the time taken to answer a query is proportional to just the size of the result, rather than to the size of the database.Cache replacement is best done by a benefit-metric based strategy that can easily adapt to changing query patterns.We show that our caching scheme is quite robust, providing good performance on a wide variety of data distributions even for small cache sizes.We also compare algorithms that use precomputation to those that use caching and show that the best performance is obtained by combining both these techniques.Finally, we illustrate how the idea of caching can be readily extended to a broader class of problems such as the mining of generalized association rules.

Complex decision support queries, as exemplified by the TPC-D benchmark, make heavy use of join, aggregation and sorting operations. These operations can be very memory intensive and it is unlikely (particularly in a multi-user environment) that a database system will always have enough memory to completely satisfy the demands of all concurrently running operators. In this paper we propose, analyze and evaluate four different algorithms that distribute available memory among operators in ways that satisfy all operator requirements and concurrent schedulability constraints. One of these algorithms, based on linear programming, is also optimal in that it is guaranteed to produce the best query response time. We also show how these algorithms can be extended to work on shared-nothing parallel database systems. All the proposed strategies are completely general and can be used with any extended relational operations that show a linear improvement in performance with increased memory availa...

This paper presents a number of new techniques for parallelizing geo-spatial database systems and discusses their implementation in the Paradise object-relational database system. The effectiveness of these techniques is demonstrated using a variety of complex geo-spatial queries over a 120 GB global geo-spatial data set.