For applications that involve rapidly changing textual data and also require traditional DBMS capabilities, current systems are unsatisfactory. We describe a hybrid IR-DB system that serves as the basis for the QUIQ-Connect product, a collaborative customer support application. We present a novel query paradigm and system architecture, along with performance results.

For a number of reasons, even the best query optimizers can very often produce sub-optimal query execution plans, lead-ing to a significant degradation of performance. This is es-pecially true in databases used for complex decision support queries and/or object-relational databases. In this paper, we describe an algorithm that detects sub-optimality of a query execution plan during query execution and attempts to correct the problem. The basic idea is to collect statis-tics at key points during the execution of a complex query. These statistics are then used to optimize the execution of the query, either by improving the resource allocation for that query, or by changing the execution plan for the re-mainder of the query. To ensure that this does not signifi-cantly slow down the normal execution of a query, the Query Optimizer carefully chooses what statistics to collect, when to collect them, and the circumstances under which to re-optimize the query. We describe an implementation of this algorithm in the Paradise Database System, and we report on performance studies, which indicate that this can result in significant improvements in the performance of complex queries. 1

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.