23-面向大数据知识发现和管理的位元知识模板
23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management
摘要：
大数据的全球影响力不仅在增长，而且似乎永无止境。趋势是倾向于从大量的大数据池中轻松快速地获得知识。今天，我们生活在乌萨马·法耶兹博士和他的杰出研究人员在介绍性解释数据库中的知识发现（KDD）中所讨论的技术世界中，预测了近20年前。事实上，他们对大数据分析的看法是准确的。事实上，机器学习、统计学、数据库构建和查询的互操作性的持续改进融合在一起，创造了这门日益流行的科学--数据采掘和知识发现。下一代计算理论旨在帮助以更高的速度从更大量的数据中提取有见地的知识。随着这一趋势的普及，需要一个高度适应性的知识发现解决方案将是必要的。在这篇研究论文中，我们介绍了大数据处理和聚类目的的元知识模板的23位问题的调查和开发。本研究的目的是展示这种方法的构建，并证明从大数据中发现知识的有效性和有益的利用。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
