面向大数据网络分析的图挖掘新设计与实现

D. Shravani
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引用次数: 0

摘要

“图论在大数据大网络中的应用”的研究是一个创新的想法,具有新颖的设计和各种案例研究的范例实现。根据文献综述,图挖掘策略可以应用于大数据网络分析。大数据涉及具有多个自治源的大容量、复杂、不断增长的数据集。随着网络、数据存储和数据采集能力的快速发展,大数据正在物理、生物、生物医学等所有科学和工程领域迅速扩展。本文提出了表征大数据革命特征的HACE定理,并从数据挖掘的角度提出了一个大数据处理模型。这个数据驱动的模型涉及需求驱动的信息源聚合、挖掘和分析、用户兴趣建模以及安全和隐私考虑。我们分析了数据驱动模型和大数据革命中具有挑战性的问题。
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Novel Design and Implementation of Graph Mining for Big Data Network Analysis
The Research entitled Application of Graph Theory to Big Networks for Big Data” is an innovative idea having novel design and exemplar implementations on various case studies. Graph Mining strategies can be applied for Big Data Networks Analysis as specified by Literature survey. Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
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