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

TL;DR

This paper introduces a 23-bit Metaknowledge template designed to enhance Big Data processing and clustering, facilitating faster and more adaptive knowledge discovery from large datasets.

Contribution

It presents a novel 23-bit question-based Metaknowledge template for Big Data analysis, demonstrating its construction and practical utility in knowledge discovery.

Findings

Validated the 23-bit Metaknowledge template for Big Data clustering.

Showed improved efficiency in knowledge extraction from large datasets.

Proved the methodology's adaptability and usefulness in Big Data contexts.

Abstract

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 from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.