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Abstract

LogicaCMG developed SABRE, a spatial business rules server. Its purpose is to process queries for
predefined business rules based on a given location. SABRE was rapidly developed as a project for 'masterclass'
employees. Although a functional demo was created, SABRE remained a simple prototype not ready
for commercial purposes. LogicaCMG's goal is to redesign SABRE for commercial purposes, such as for
instance tracking and tracing. This project focusses on the optimization of the database part, which will be
the foundation of SABRE. If SABRE will be commercially deployed we can expect massive usage, and very
high performance requirements. Therefore it's essential to develop SABRE from the ground up in such a way
that maximum performance is achieved.
The SABRE architecture consists basically of a Service Provider (SP), Web service (WS) and a Database
(DB). The SP requests certain information from the DB through the WS. The aim of this project is to develop
a working version of the redesigned SABRE application fit for demonstration purposes. The focus will be on
the DB part, therefore the WS will support only one service (e.g. AREA-event). The objective of this
assignment is to study the performance and scalability of the DB. The two most important scalability aspects
are: how does the DB cope when the amount of requests increases and how does the DB cope when the
amount of data increases.
A scalable database design has been created of which a prototype has been implemented. The prototype was
used for testing the performance and scalability of SABRE. To improve the performance and scalability three
optimizations have been used, SQL Tuning, Materialized Views and Range Partitioning. The Materialized
View optimization showed the best result with a 60% performance improvement. As a result of the
optimization the SABRE performed well for four out of the five used datasets. The largest dataset was too
large for the database to handle in terms of response times. However, since the tests have pointed out that
SABRE is scalable, addressing the issue of the largest dataset should only be a matter of adding resources.
When the required resources have been added the SABRE application will meet its requirements for
commercial exploitation. Therefore it is expected to hear more from SABRE in the near future.

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