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Abstract

Increasing web services day by day and huge volume of data is also increasing exponentially. Processing a large amount of data efficiently can be a substantial problem. Currently, the method for processing a large amount of data comprises adopting parallel computing. Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications. The challenges comprise analysis, capture, creation, search, sharing, storage, transfer, visualization, and privacy violations. With pervasive sensors continuously collecting and storing enormous amounts of information leads to data flood. Learning from these large volumes of data is expected to bring significant science and engineering advances along with improvements in quality of life. However, with such a big blessing come big challenges. Billions of Internet users and machine-to-machine connections are producing a huge volume of data growth. Utilizing big data requires transforming information infrastructure into a more flexible, distributed, and open environment. In this paper, a survey has been prepared about the techniques available for optimization in big data with the presence of swarm intelligence. Using mathematical model based algorithm for optimization (Swarm Intelligence) in big data will yield better performance while handling of dynamic data in the non-stationary environments and dynamic environments.