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Proposal of analytics software architecture with data preparation layer for fast event identification in wide-area situational awareness

机译：带有数据准备层的分析软件架构的建议，用于在广域态势感知中快速识别事件

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摘要

The number of PMUs (Phasor Measurement Units) has increased drastically in several countries. The collected data such as voltage, current, and frequency become over hundreds terabytes in a few years. It takes long time to retrieve specific similar patterns to a query for analysis. This paper defines data preparation layer in an analytical system. That enables enormous accumulation. The layer contains a function that creates index creation based on time-series patterns and another function that quickly retrieves user required patterns. Both functions process clusters as well as string. In the experiment, the time to retrieve similar patterns to a query is measured, and four architectures of time-series data process are compared; (1) conventional architecture with no data preparation layer (2) architecture with String (3) architecture with Cluster, and (4) architecture with String and Cluster. The experiment result shows that the best one with String and Cluster is 67.7 times faster than the conventional no-data preparation layer architecture. In conclusion, the proposed data preparation layer is effective to retrieve similar time-series data patterns to the query. It is also clarified that the layer is critical to analyze historical data for predicting wide-area power disturbance.
机译：在一些国家，PMU（相量测量单位）的数量急剧增加。几年之内，诸如电压，电流和频率之类的收集数据已超过数百TB。检索与查询相似的特定模式需要花费很长时间才能进行分析。本文定义了分析系统中的数据准备层。这样可以实现巨大的积累。该层包含一个根据时间序列模式创建索引创建的函数，以及另一个快速检索用户所需模式的函数。这两个函数都处理簇和字符串。在实验中，测量了检索与查询相似的模式的时间，并比较了时序数据处理的四种体系结构; （1）没有数据准备层的常规体系结构（2）具有String的体系结构（3）具有Cluster的体系结构，以及（4）具有String和Cluster的体系结构。实验结果表明，使用String和Cluster的最佳方法比传统的无数据准备层体系结构快67.7倍。总之，所提出的数据准备层可有效地检索与查询相似的时间序列数据模式。还阐明了该层对于分析历史数据以预测广域电源干扰至关重要。

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