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Journal Dynamic Oracle Performance Analytics

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11 articles
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Roger Cornejo
Dynamic Oracle Performance Analytics pp 79-89; doi:10.1007/978-1-4842-4137-0_5

The publisher has not yet granted permission to display this abstract.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 3-16; doi:10.1007/978-1-4842-4137-0_1

The publisher has not yet granted permission to display this abstract.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 91-105; doi:10.1007/978-1-4842-4137-0_6

Abstract:Feature selection, which was discussed in the last chapter, is a powerful component of the DOPA process. It enables the tuning analyst to quickly identify areas of the database that are performing outside of normal. The metrics with a high incidence of flagged values are assumed to have a high predictive value of pointing to the problem area. And this is definitely true in my experience. While the feature selection/flagging process is sufficient by itself to solve many problems, I learned another analytics “trick” from my son that enabled me to take my analysis one step further. The concept I brought into the analysis is that of taxonomy.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 19-32; doi:10.1007/978-1-4842-4137-0_2

Abstract:Before I begin Chapter 2, let me introduce Part II of this book, which is made up of Chapters 2– 7.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 61-77; doi:10.1007/978-1-4842-4137-0_4

Abstract:This chapter addresses the statistical manipulation of the data that has been collected and formatted. I’ll start by reviewing some basic statistical concepts, including normal ranges, outliers, and variance, and then discuss how these concepts are applied as part of the DOPA process to establish normal ranges for the host of metrics that don’t have well-known norms. Don’t worry, you don’t have to break out the statistics books; I will show you how to use the embedded Oracle functions to easily accomplish this statistical analysis.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 141-173; doi:10.1007/978-1-4842-4137-0_8

Abstract:In the previous chapter, I provided a framework for making the many decisions necessary when implementing the DOPA process. The discussion focused upon how you would choose to run the analysis, the various parameters, and views you would choose. In this chapter, I’ll lead you through several real examples.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 33-59; doi:10.1007/978-1-4842-4137-0_3

Abstract:Information from the client related to the performance issue being experienced is an important element in determining root cause. This information can be viewed as a source of data for the analytic process. The other source of data that is invaluable to the analytic process is data obtained from the database itself. This chapter provides step-by-step instructions for how to collect and prepare the data from the database in preparation for the next step in the analysis process which is statistical analysis. The process of preparing data occurs in the following sequence:
Roger Cornejo
Dynamic Oracle Performance Analytics pp 191-217; doi:10.1007/978-1-4842-4137-0_10

Abstract:As I have stated repeatedly throughout this work, the DOPA process is something I have been developing over a period of time. I consider it an effective tool as is, but there are many enhancements and further applications which I would like to explore as time allows. In this chapter I discuss further enhancements/possible future work in a general way. I hope these brief comments will stimulate the reader to experiment with these ideas as well. If so, I would love to hear about the results of your exploration.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 107-138; doi:10.1007/978-1-4842-4137-0_7

Abstract:In the preceding chapters, I introduced the individual steps of the DOPA process. It is finally time to put it all together, build the model, and report the results. As I have repeated throughout the book, the DOPA process is dynamic. You essentially create a new, unique predictive model with each execution of the code by altering the model inputs as you refine your tuning efforts. It is also versatile because the data can be subset in such a way that it makes the analysis easy and clearly shows the metrics that enable you to discover the cause of the performance problem.
Roger Cornejo
Dynamic Oracle Performance Analytics pp 175-189; doi:10.1007/978-1-4842-4137-0_9

Abstract:Sadly, and all too often, we learn of database performance problems as a complaint from database users. Most DBAs and managers would like to stay ahead of performance problems by monitoring their databases and taking peremptory action to avoid the types of performance complaints to which most of us have been accustomed. Some level of automated monitoring is or should be an integral and important task for DBAs, especially in environments with hundreds if not thousands of databases, where the sheer volume makes it impossible to monitor manually.
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