Foundations of an autonomic manager for maintaining quality of service in enterprise data warehouses
Date
2017
Authors
Omondi, Allan O.
Ateya, Ismail L.
Wanyembi, Gregory N.
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
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.
Description
The conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.
Keywords
Autonomic computing, Enterprise data warehouse, Markov Decision Process, Markov Reward Process, Reinforcement learning
Citation
Omondi, A. O., Ateya, I. L., & Wanyembi, G. N. (2017). Foundations of an autonomic manager for maintaining quality of service in enterprise data warehouses. In Pan African Conference on Science, Computing and Telecommunications (PACT). Nairobi: Strathmore University. Retrieved from https://su-plus.strathmore.edu