Knowledge Management in Autonomic Database Performance Tuning

Databases are growing rapidly in scale and complexity. High performance, availability and further policy goals need to be satisfied under any circumstances to please customers. In order to tune the DBMS within their complex environments highly skilled database administrators are required. Unfortunately, they are becoming rarer and more and more expensive. Hence, improving performance analysis and moving towards automation of problem resolution requires a more intuitive and flexible source of decision making. This paper points out the importance of knowledge for autonomic database tuning, proposes a component-based knowledge model and briefly presents first prototypical evaluation results.