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Organization of Knowledge Extraction from Big Data Systems

机译：大数据系统知识提取的组织

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Even though some of the present-day technologies provide a number of solutions for handling large amounts of data, the increasing accumulation of data -- also termed as Big Data -- from the Internet such as emails, videos, images, and text as well as the digital data in medicine, genetics, and sensors and wireless devices is demanding efficient organizational and engineering designs. Many forms of digital data such as maps and climate informatics, geospatial attributes such as global positioning coordinates, location information, and directions are represented by text, images, or interactive graphics-videos. A single source may produce various types of data (e.g. a geospatial data source may produce both image-and text-type data). This vast and rich data requires a generic processing mechanism that can adapt to various data types and classify them accordingly. In this paper, we propose a technique to optimize the information processing for on-the-fly clusterization of disorganized and unclassified data from vast number of sources. The technique is based on the fuzzy logic using fault-tolerant indexing with error-correction Golay coding. We present an information processing model and an optimized technique for clustering continuous and complex data streams. We show that this mechanism can efficiently retrieve the sensible information from the underlying data clusters. The main objective of this paper is to introduce a tool for this demanding Big Data processing -- on-the-fly clustering of amorphous data items in data stream mode. Finally, we introduce the parallels between computational models of Big Data processing as well as the information processing of human brain where the human brain can be considered as a Big Data machine.
机译：即使某些当今的技术提供了用于处理大量数据的许多解决方案，但来自Internet的电子邮件（如视频，图像，图像和文本）的数据积累（也称为大数据）的存储量也在不断增加由于医学，遗传学以及传感器和无线设备中的数字数据需要高效的组织和工程设计。许多形式的数字数据（例如地图和气候信息学），地理空间属性（例如全球定位坐标，位置信息和方向）均由文本，图像或交互式图形视频表示。单个源可以产生各种类型的数据（例如，地理空间数据源可以产生图像和文本类型的数据）。这种庞大而丰富的数据需要通用的处理机制，该机制可以适应各种数据类型并相应地对其进行分类。在本文中，我们提出了一种技术，可以对来自大量来源的无序和未分类数据进行动态聚类的信息处理进行优化。该技术基于使用容错索引和纠错Golay编码的模糊逻辑。我们提出了一种信息处理模型和一种用于对连续和复杂数据流进行聚类的优化技术。我们证明了这种机制可以有效地从基础数据集群中检索明智的信息。本文的主要目的是介绍一种用于进行这种苛刻的大数据处理的工具-在数据流模式下对非晶体数据项进行动态聚类。最后，我们介绍了大数据处理的计算模型与人脑的信息处理之间的相似之处，其中人脑可以被视为大数据机器。

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