Foundations of an autonomic manager for maintaining quality of service in enterprise data warehouses

Abstract

Data stored in an Enterprise Data Warehouse (EDW) is an essential asset to enterprises. Through efficient access to data (where efficiency is quantitatively measured in terms of speed), SMEs can enhance their growth, productivity, and global competitiveness. This can in turn lead to a positive impact on a country's Gross Domestic Product. The purpose of this paper is to present the building blocks required to maximize the speed of data access from EDWs in a self-adaptive manner. Reinforcement Learning (RL) in a fully observable, stochastic environment is proposed. The subsequent solution to a Markov Decision Process is highlighted as the core part of the RL.