时间序列大数据:对数据流框架,分析和算法的调查
Ana Almeida1,2, Susana Brás2,3, Susana Sargento1,2
1Instituto de Telecomunicações, Aveiro, Portugal.
概括
本文探讨了为增强知识发现和决策构建实时大数据系统. 它检查了处理大量数据的当前方法,并应用算法来获得即时的见解和预测.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 信息系统信息系统信息系统
背景情况:
- 大数据的意义在过去十年里大幅增长.
- 主要优势包括知识提取,决策支持和优化资源利用.
- 大数据的实时应用扩大了其分析,预测和预测的潜力.
研究的目的:
- 提供关于构建实时大数据处理系统的观点.
- 概述对大型数据集进行分析和应用算法的方法.
- 探索当前实时操作和预测建模的方法.
主要方法:
- 处理大量数据的系统设计原则.
- 探索当前的大数据处理架构的探索.
- 整合分析算法以获得实时洞察力.
主要成果:
- 一个实时大数据分析和预测的框架.
- 从大型数据集中提取知识的有效策略的识别.
- 通过及时的数据洞察,增强决策能力.
结论:
- 实时大数据处理系统对于现代应用至关重要.
- 有效的系统设计可以发现有价值的知识和改进运营.
- 目前的方法为实现实时分析和预测提供了途径.
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