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2. Optimal job scheduling - Wikipedia

   en.wikipedia.org/wiki/Optimal_job_scheduling

   Optimal job scheduling is a class of optimization problems related to ... All variants surveyed above are deterministic in that all data is known to the planner.

3. Job-shop scheduling - Wikipedia

   en.wikipedia.org/wiki/Job-shop_scheduling

   Job-shop scheduling, the job-shop problem (JSP) or job-shop scheduling problem (JSSP) is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling .

4. Data parallelism - Wikipedia

   en.wikipedia.org/wiki/Data_parallelism

   Sequential vs. data-parallel job execution. Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in ...

5. Data conditioning - Wikipedia

   en.wikipedia.org/wiki/Data_conditioning

   Data conditioning principles can be applied to any demanding computing environment to create significant cost, performance and system utilization efficiencies, and are typically deployed by data center managers, system integrators, and storage and server OEMs seeking to optimize hardware and software utilization, simplified, non-intrusive ...

6. Mathematical optimization - Wikipedia

   en.wikipedia.org/wiki/Mathematical_optimization

   Robust optimization is, like stochastic programming, an attempt to capture uncertainty in the data underlying the optimization problem. Robust optimization aims to find solutions that are valid under all possible realizations of the uncertainties defined by an uncertainty set.

7. Analytics - Wikipedia

   en.wikipedia.org/wiki/Analytics

   Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.