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A job scheduler is a computer application for controlling unattended background program execution of jobs. [1] This is commonly called batch scheduling, as execution of non-interactive jobs is often called batch processing, though traditional job and batch are distinguished and contrasted; see that page for details.
In computer programming, an iterator is an object that progressively provides access to each item of a collection, in order. [1] [2] [3]A collection may provide multiple iterators via its interface that provide items in different orders, such as forwards and backwards.
Salesforce management systems (also sales force automation systems (SFA)) are information systems used in customer relationship management (CRM) marketing and management that help automate some sales and sales force management functions. They are often combined with a marketing information system, in which case they are often called CRM systems
The main planning process within extreme programming is called the Planning Game. The game is a meeting that occurs once per iteration, typically once a week.
A simplified version of a typical iteration cycle in agile project management. The basic idea behind this method is to develop a system through repeated cycles (iterative) and in smaller portions at a time (incremental), allowing software developers to take advantage of what was learned during development of earlier parts or versions of the system.
The earliest known work (1989) on continuous integration was the Infuse environment developed by G. E. Kaiser, D. E. Perry, and W. M. Schell. [4]In 1994, Grady Booch used the phrase continuous integration in Object-Oriented Analysis and Design with Applications (2nd edition) [5] to explain how, when developing using micro processes, "internal releases represent a sort of continuous integration ...
In multivariable calculus, an iterated integral is the result of applying integrals to a function of more than one variable (for example (,) or (,,)) in such a way that each of the integrals considers some of the variables as given constants.
The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and computer vision.