Can I use PostgreSQL to develop a self-organizing database system?

Hi,

at NC State University, my students and I are working on a project called
"self-organizing databases," please see description below. I would like to
use an open-source database system for implementation and would really
appreciate your opinion on whether PostgreSQL is suitable for the project.
In general, I am very impressed by the quality of PostgreSQL code and
documentation, as well as by the support of the developer community.

For the project, I need a cost-based query optimizer with exhaustive join
enumeration and use of statistics on stored relations; PostgreSQL has that.
I also need the ability to process SQL queries with aggregation, extensive
indexing capabilities, view mechanisms, and possibly integrity constraints;
it seems that PostgreSQL has all that. We will modify the query optimizer to
incorporate rewriting queries using views, and we will create
view-generating and view-manipulating modules.

Please let me know if you have comments.

Sincerely,

Rada Chirkova

======================
Self-Organizing Databases

The goal of this project is to develop new effective methods to improve the
performance of sets of frequent and important queries on large relational
databases at all times, which could improve the efficiency of user
interactions with data-management systems. Solving the problem will have the
most effect in query optimization, data warehousing, and information
integration, which are important research topics with direct practical
applications.

The project focuses on the methodology of evaluating queries using views;
views are relations that are defined by auxiliary queries and can be used to
rewrite and answer user queries. One way to improve query performance is
precompute and store (i.e., materialize) views.

To truly optimize query performance, it is critical to materialize the
"right" views. The current focus of the project is on demonstrating that, by
designing and materializing views, it is possible to ensure optimal or
near-optimal performance of frequent and important queries, for common and
important query types. We consider this problem in the broader context of
designing self-organizing databases: A self-organizing database periodically
determines, without human intervention, a representative set of frequent and
important queries on the data, and incrementally designs and precomputes the
optimal (or near-optimal) views for that representative query workload. As
the representative query workload and the stored data change over time,
self-organizing databases adapt to the changes by changing the set of
materialized views that are used to improve the query-answering performance
in the database.

For building self-organizing databases, we consider an end-to-end solution –
that is, we consider all aspects of handling and using views, including:
·    designing and materializing views and indexes to improve query
performance;
·    exploring the effects of materialized views on the process of query
optimization;
·    adapting view design to the changing query workload, including the process
of retiring views that are no longer useful;
·    developing methods for auomatically updating existing materialized views
over time, to reflect the changes in the stored data;
·    developing methods to collect database statistics to reliably estimate the
sizes of the views the system considers for materialization;
·    analyzing the use of system resources and allocating an appropriate amount
of resources to view management in the system.