Start Date
14-12-2012 12:00 AM
Description
Organizations in today’s rapidly evolving digital economy are relying more than ever on their database systems for critical decision-making functions. As a result, speedy and timely availability of the information from these systems is one of key factors crucial to organizational survival. Operating these database systems at high performance levels under complex and dynamic environments is a knowledge-intensive error-prone human-driven task. Although, there have been several developments in the area of autonomous performance tuning, such approaches are of limited use because they largely ignore the impact and the extent of organization-specific environmental changes on the performance of its database systems. This research addresses these issues by: 1. Extending the existing tuning reference knowledge model by incorporating the organization-specific environmental change impact knowledge. 2. Proposing a framework called “DECIPHER” that predictively acquires this knowledge for unimplemented environmental changes and identifies its dependencies by mining the existing organizational change management databases.
Recommended Citation
Sharma, Monish and Sarnikar, Surendra, "DECIPHER: Database Environmental Change Impact Prediction for Human-Driven Tuning Efforts in Real-Time" (2012). ICIS 2012 Proceedings. 12.
https://aisel.aisnet.org/icis2012/proceedings/KnowledgeManagement/12
DECIPHER: Database Environmental Change Impact Prediction for Human-Driven Tuning Efforts in Real-Time
Organizations in today’s rapidly evolving digital economy are relying more than ever on their database systems for critical decision-making functions. As a result, speedy and timely availability of the information from these systems is one of key factors crucial to organizational survival. Operating these database systems at high performance levels under complex and dynamic environments is a knowledge-intensive error-prone human-driven task. Although, there have been several developments in the area of autonomous performance tuning, such approaches are of limited use because they largely ignore the impact and the extent of organization-specific environmental changes on the performance of its database systems. This research addresses these issues by: 1. Extending the existing tuning reference knowledge model by incorporating the organization-specific environmental change impact knowledge. 2. Proposing a framework called “DECIPHER” that predictively acquires this knowledge for unimplemented environmental changes and identifies its dependencies by mining the existing organizational change management databases.