Organization of Meta-knowledge in the Form of 23-Bit Templates for Big Data Processing

Abstract

We are living and facing an unprecedented growth of available large-scale structured and unstructured data both. From different broad range of online websites and applications, data is being collected at significantly exceptional rate. For instance, as described in [11] Facebook reports about 6 billion new photos every month and 72 hours of video are uploaded to YouTube every minute. Researchers and developers are faced with this large amount of data that needs to be processed, analyzed, and clustered. Analysis of Big Data essentially drives every aspect of our daily life, including and not limited to, retail services, mobile services, financial services, manufacturing, and life sciences. The existing and conventional data processing techniques and clustering algorithms were not initially designed to handle this large amount of data and we face challenges to analyze Big Data. This research paper attempts to enhance these existing clustering algorithms to process Big Data. This research introduces an unprecedented big data processing technique using a 23-bit question meta-knowledge template for Big Data clustering in a linear time complexity O(n).