Abstract
Big Data is playing a key role in diverse areas worldwide as these contains vast amount of essential information. The security as well as privacy of the data has become an unfathomable provocation that requests more awareness so as to achieve provide well-organized way of transference with secrecy perspective as the information consists of huge amount of important data. From the past few years, Data achieved a lot of observation by investigation group. The data was developed in large scale in about each area which is unprocessed as well as unstructured. Discovering awareness on appropriate data through huge raw information is the vital confrontation, existing nowadays. Different soft computing techniques and computational intelligence have been suggested for systematic information examination. These are mostly used in Artificial Intelligence (AI) computing technique that take part in an essential part in present big information confrontation by pre-refining as well as restructuring data. The administration domains in which conventional fuzzy sets and higher order fuzzy sets have shown exceptional results. Even though, this investigation domain in “fuzzy techniques in Big Data” is getting a few observations, there is a powerful demand for an inspiration to uplift investigators to research a lot in this arena. This paper organized bibliometric learning on upcoming growth in this area of “fuzzy methods in big information”. Dilatory, a juxtapose examination is done on the fuzzy methods in information after examining the majority of effective works in this area.
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Basavesha, D., Bharathi, S., Pareek, P.K. (2021). Soft Computational Techniques to Discover Unique and Precise Knowledge from Big Data. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_24
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