Big that are so huge and complex than

Topics: Technology


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Last updated: September 23, 2019

Big data is a collection of data sets that are sohuge and complex than usual data processing application software is not enoughto deal. Dataset is a collection of data. Big data challenges include capturingdata, data storage, data study, investigate, transfer, visualization, querying,and updating and information privacy.

There are three characteristics of bigdata known as Volume, Variety, and Velocity. The term “big data” was created todefine the collection of huge amounts of data in structured, semi-structured,or unstructured formats in big databases, file systems, or other types ofrepositories. The processing of this data in order to produce an analysis andcombination of the trends and actions in actual or almost real-time.

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Out of theabove amounts of data, the unstructured data needs more immediate analysis andbears more valuable information to be exposed, providing a more in-depthunderstanding of the researched subject. It is also the unstructured data whichincurs more challenges in collecting, storing, organizing, classifying,analyzing, as well as supervision. In addition to the big data computingability, the quick advances in using intellectual data analytics techniquesdrawn from the emerging areas of artificial intelligence (AI) and machinelearning (ML) provide the ability to process very big amounts of diverseunstructured data that is now being generated daily to extract valuableactionable information.

Machine learning explores the learning and structure ofalgorithms that can study from and make predictions on data. The properinformation extraction from the variety of resources needs mining, machinelearning, and natural languages processing techniques. Readily available arefour types of analytics specifically prescriptive, predictive, diagnostic, anddescriptive. According to Gartner, most of the association had used predictivecompares to other types.

Normally, Machine Learning Algorithms can be dividedinto several categories affording to their use and the key categories are thefollowing:•              Supervisedlearning•              UnsupervisedLearning•              Semi-supervisedLearning•              ReinforcementLearning•              EvolutionaryLearning•              Deeplearning

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