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Database tuning describes a group of activities used to optimize and homogenize the performance of a database.It usually overlaps with query tuning, but refers to design of the database files, selection of the database management system (DBMS) application, and configuration of the database's environment (operating system, CPU, etc.).
See the main article at Performance engineering. Performance engineering is the discipline encompassing roles, skills, activities, practices, tools, and deliverables used to meet the non-functional requirements of a designed system, such as increase business revenue, reduction of system failure, delayed projects, and avoidance of unnecessary usage of resources or work.
The initiative included a National Science Foundation "Expeditions in Computing" grant of $10 million over five years to the AMPLab [175] at the University of California, Berkeley. [176] The AMPLab also received funds from DARPA , and over a dozen industrial sponsors and uses big data to attack a wide range of problems from predicting traffic ...
Oracle Corporation is an American multinational computer technology company headquartered in Austin, Texas. [5] Co-founded in 1977 in Santa Clara, California, by Larry Ellison, who remains executive chairman, Oracle was the third-largest software company in the world in 2020 by revenue and market capitalization. [6]
Empirix eTest Suite was acquired by Oracle in June 2008 and was rebranded as Oracle Application Testing Suite. The Oracle Application Testing Suite is part of the Oracle Enterprise Manager product family and comprises the following tightly integrated products: [1] Oracle Load Testing for scalability, performance and load testing.
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Oracle Database (commonly referred to as Oracle DBMS, Oracle Autonomous Database, or simply as Oracle) is a proprietary multi-model [4] database management system produced and marketed by Oracle Corporation. It is a database commonly used for running online transaction processing (OLTP), data warehousing (DW) and mixed (OLTP & DW) database ...
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.