23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management

摘要

The global influence of Big Data is not only growing but seemingly endless.The trend is leaning towards knowledge that is attained easily and quickly frommassive pools of Big Data. Today we are living in the technological world thatDr. Usama Fayyad and his distinguished research fellows discussed in theintroductory explanations of Knowledge Discovery in Databases (KDD) predictednearly two decades ago. Indeed, they were precise in their outlook on Big Dataanalytics. In fact, the continued improvement of the interoperability ofmachine learning, statistics, database building and querying fused to createthis increasingly popular science- Data Mining and Knowledge Discovery. Thenext generation computational theories are geared towards helping to extractinsightful knowledge from even larger volumes of data at higher rates of speed.As the trend increases in popularity, the need for a highly adaptive solutionfor knowledge discovery will be necessary. In this research paper, we areintroducing the investigation and development of 23 bit-questions for aMetaknowledge template for Big Data Processing and clustering purposes. Thisresearch aims to demonstrate the construction of this methodology and provesthe validity and the beneficial utilization that brings Knowledge Discoveryfrom Big Data.