Towards a Training-Oriented Adaptive Decision Guidance and Support System

Information systems today have become incredibly complex and span multiple organizational networks, database and applications servers and on to the external Internet cloud resources. Consequently strategic approaches are needed to troubleshoot system failures by first identifying the component causing the failure, and thereby, further investigating the cause of the failure to solve the problem. Information regarding past troubleshooting strategies can be used to provide guidance for solving similar problems. We present a framework, DSDAware (Decision Support for Database Administrators using Warehouse-as-a-service) for developing a Decision Guidance and Support System (DGSS). The framework dynamically extracts knowledge from various correlated data sources containing systems related data and from the problem solving procedures of the human experts. The knowledge is used in a strategic problem solving approach to train new administrators by guiding them through the troubleshooting process using an interactive interface, and to offer a decision support service to the Web community. Our work specifically focuses on z/OS Mainframe DB2 database (DB) problems where the inherent complexity of the system makes troubleshooting a challenging task. The diminishing population of mainframe DB administrators (DBA) asserts the need for a DGSS for the new DBAs. The research applies text and data mining techniques for knowledge extraction, a rule-based system for knowledge representation and problem categorization, and a case-based system for providing decision support.