viscosity in big data

To determine the value of data, size of data plays a very crucial role. Virality measures how quickly data is spread and shared to each unique node. Nowadays big data is often seen as integral to a company's data strategy. Q. You may have heard of the three Vs of big data, but I believe there are seven additional important characteristics you need to know. (You might consider a fifth V, value.) Consider the following statements: Statement 1: Volatility refers to the data velocity relative to timescale of event being studied. Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V's of Big Data: Volume, Velocity and Variety. Statement 2: Viscosity refers to the rate of data loss and stable lifetime of data In recent years, Big Data was defined by the “3Vs” but now there is “5Vs” of Big Data which are also termed as the characteristics of Big Data as follows: 1. Viscosity - Viscosity measures the resistance to flow in the volume of data. Volume The main characteristic that makes data “big” is … In data science, this is often referred to as data cleaning, this operation is frequently the most labor intensive as it involves all of the pre-work required to set-up the high-performance compute. Time is a determinant factor along with rate of spread. This is where the vast majority of errors and issues are found with data and this is the fundamental bottle neck in high-performance computing. Volume is a huge amount of data. In most big data circles, these are called the four V’s: volume, variety, velocity, and veracity. Let me first introduce 8 V’s of Big data (based on an interesting article from Elena), namely Volume, Value, Veracity, Visualization, Variety, Velocity, Viscosity, and Virality. Volume: The name ‘Big Data’ itself is related to a size which is enormous. Validity, Volatility, Viability, and Viscosity of Big Data . Big data has specific characteristics and properties that can help you understand both the challenges and advantages of big data initiatives. Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. IV. High-volume, high-velocity and high-variety of Big Data . If we closely look at the questions on individual V’s in Fig 1, they trigger interesting points for the researchers. The general consensus of the day is that there are specific attributes that define big data. Big data’s power does not erase the need for vision or human insight. with more detail . These characteristics highlight the importance and complexity required to solve context in big data. C HARACTERISTICS, I SSUES A ND C HALLENGES. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, … While I don’t feel like adding all the V-words to the 3 or 4 V’s definition of Big Data, these new two, viscosity and virality, sound intriguing.

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