http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Using Normalized Metrics to Improve Database Speed
Use an innovative approach that relies on big data and advanced analytical techniques to analyze and improve Oracle Database performance. The approach in this book is a step-change away from traditional methods. Instead of relying on a few hand-picked, favorite metrics, or wading through multiple specialized tables of information such as those found in an automatic workload repository (AWR) report, you will draw on all available data, applying big data methods and analytical techniques to draw impactful, focused performance improvement conclusions. This book reviews past and present practices, along with available tools, to help you pinpoint areas for improvement. The book then guides you through a step-by-step method that can be used to take advantage of all available metrics to identify problem areas and work toward improving them. The method presented simplifies the tuning process and solves the problem of metric overload. You will learn how to: collect and normalize data, generate deltas that are useful in performing statistical analysis, create and use a taxonomy to enhance your understanding of problem performance areas in your database and its applications, and create a root cause analysis report that enables understanding of a specific performance problem and its likely solutions. What You'll Learn: Collect and prepare metrics for analysis from a wide array of sources Apply statistical techniques to select relevant metrics Create a taxonomy to provide additional insight into problem areas Provide a metrics-based root cause analysis regarding the performance issue Generate an actionable tuning plan prioritized according to problem areas Monitor performance using database-specific normal ranges.
Part I. Performance Tuning Basics -- 1. Traditional Approaches -- Part II. The Dynamic Oracle Performance Analytics (DOPA) Process -- 2. Gathering Problem Information -- 3. Data Preparation -- 4. Statistical Analysis -- 5. Feature Selection -- 6. Taxonomy -- 7. Building the Model and Reporting -- Part III. Case Studies and Further Applications -- 8. Case Studies -- 9. Monitoring -- 10. Further Enhancements.