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big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, ident...
big data is expressed in terms of V’s (volume, velocity, and variety); but it includes many v’s such as veracities, vagueness variability, vulnerability, volatility visualization, etc. Big data application areas include business organization, operation, production, marketing, information...
Big data is a term utilized to refer to the increase in the volume of data that are difficult to store, process, and analyze through traditional database technologies. The nature of big data is indistinct and involves considerable processes to identify and translate the data into new insights....
Data Big and Small Is Paris a 15-minute city? A few years ago, when hopes to leverage technology to build a more humane “sharing” economy had not yet completely vanished, it was often believed that the most interesting policy experiments were to be found at the local level of cities, ...
More recently, in 2012, Gartner [6] updated the definition as follows: “Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization”. Both definitions refer to...
McKinsey Global Institute June 2011 Big data: The next frontier for innovation, competition, and productivity The McKinsey Global Institute The McKinsey Global Institute (MGI), established in 1990, is McKinsey & Company's business and econom...
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Big Data is still gaining attention as a fundamental building block of the Artificial Intelligence and Machine Learning world. Therefore, a lot of effort has been pushed into Big Data research in the last 15 years. The objective of this Systematic Literature Review is to summarize the current st...
Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied