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Thursday, March 14 • 10:55am - 11:45am
Self-diagnosis and Management of Performance and Availability for Autonomous Database

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Autonomous databases signal considerable advancement from manual processes previously required by legacy databases that have been prevalent in previous decades. Removal of the human operator and as such, associated human error, from the control loop is the basic premise of autonomous databases. Self-driving and virtually infallible software agents may now take control of management and administrative tasks relating to database function that were once completed manually. Modern databases can generate vast numbers of diverse streams of sensory data with important implications for database behavior and performance. Such sensory data range from expanded performance metrics to internal and external resource utilization to workload statistics and detailed process logs. Given that traditional hands-on inspection procedures are unable to match the expansive volume of this data, there now exists a unique opportunity to leverage this data and the application of machine learning techniques

avatar for Yaser Suleiman

Yaser Suleiman

CMTS, Oracle
Yaser Suleiman, CMTS at Oracle HQ, Database RAC organization, developer in CHA team (part of the Autonomous Health).

Thursday March 14, 2019 10:55am - 11:45am PDT
3-Rm 104