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Scheduling is critical part in most creation frameworks and information processing as sequencing of tasks or jobs framework executed on a grouping of processors. One of the NP-hard problem is “Job Shop Scheduling Problem”. In this work, a method of optimization proposed called “Fireworks Algorithm”. The solutions divided into fireworks and each one applied sparks to find the best solution. For some selected spark applied Gaussian mutation to find enhanced solution and find optimum solution. FWA tested on dataset to improve performance and it do well with respect to some other algorithm like Meerkat Clan Algorithm (MCA), Camel Herds Algorithm) CHA (, and Cukoo Search Algorithm (CSA).
component --- Metaheuristic --- Firework Algorithm --- Flexible Job-shop Scheduling --- Make-span Time
Big data refer to the large volume of data, it can be analyzed for strategic Developing and better decisions. Big Data applications exaggerate in near few years because a traditional data techniques be limited specification. Various types of distributions and technologies used to suffer the Big Data challenges are developed. A survey of recent technologies are review for Big Data. The main technologies features are studied enable to extract knowledge from Big Data. Such distributions have some limitations and may differ in offerings and capacities. The used technologies face the increasing multi-streams and Big Data challenges. In this work review the big data technologies and challenge.
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