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Big Data Analytics using IoT
1  Patliputra University, India


Development of big data and IoT is rapidly increasingly and affecting all the major technologies and the business by increasing the benefits for the individual and organisations. The increasing growth of data of IoT devices has played a major role for use of Big Data. Big data is categorize into three aspects (a) Variety (b) Volume (c) Velocity [1]. These categories are introduced by Gartner to describe the elements of big data challenges. Various opportunities are presented by the capability to analyze and utilize huge amounts of IoT data, including applications in smart cities, smart transport and grid systems, energy smart meters, and remote patient healthcare monitoring devices. The more popularity of Internet of Things day by day has made a big data analytics challenging because of the processing and collection of data through different sensors in the IoT network. The IoT data are totally different from normal big data collected through systems in terms of characteristics because of the various sensors and objects involved during data collection, which include heterogeneity, noise, variety, and rapid growth.

Keywords: The IoT, Data Analytics, Big Data, Cloud, Edge, Fog Computing
Comments on this paper
Humbert G. Díaz
REVIEWWWERS-07 workshop question. Dear author(s), thank your kind support to our conference. Now publication stage is closed and we opened our online post-publication review/discussion workshop. In case, you answer questions/make questions to other papers you are entitled to become a MOL2NET conference certified reviewer. In this context, we have some questions for you.

Have you noted that some authors defined Big Data not as a 3V problem but as a 5V+C problem involving (a) Variety (b) Volume (c) Velocity, (d) Veracity, (e) Variability and (f) Complexity? Where complexity refers to multi-collinearities and/or inter-dependences among input variables forming networks of interconnected terms. In my personal case, we have argued also in favor of considering (g) Multiple labelling of input variables interconnected to (h) multiple output variables at the same time as another possible Big Data features. What opinion deserves to you all this???

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