水利云下的数据清洗策略研究与实现
发布时间：2018-11-01 20:30
【摘要】：随着水利信息化系统迁入云端之后,由设备或人为、环境等各种主客观原因造成采集到云数据中心的数据中含有大量的"脏数据"(如乱序、异常、相似重复、误报、不完整、逻辑错误等),这些大量的"脏数据"会给应用系统带来高额的处理费用,延长响应时间,甚至会导致数据分析异常,降低决策支持系统的准确率,严重影响系统服务质量,难以支撑上层应用。本文结合项目中的实际情况给出了清洗这些脏数据的流程和方法,并通过实际数据和实验方案验证了本数据清洗方案的有效性,大大改善了水利信息化系统预测预警的效率。
[Abstract]:As the water conservancy information system moves into the cloud, the data collected into the cloud data center contain a large amount of "dirty data" (such as disorder, anomaly, similar repetition, false alarm, incomplete) caused by various subjective and objective reasons, such as equipment, human beings, environment and so on. The large amount of "dirty data" will bring high processing cost to the application system, prolong the response time, even lead to abnormal data analysis, reduce the accuracy of decision support system, and seriously affect the quality of service of the system. It is difficult to support the upper application. According to the actual situation of the project, this paper gives the flow and method of cleaning these dirty data, and validates the validity of the data cleaning scheme through the actual data and experimental scheme, which greatly improves the efficiency of prediction and early warning of water conservancy information system.
【作者单位】： 四川信息职业技术学院;
【分类号】：TV21;TP311.13
，
本文编号：2305022
[Abstract]:As the water conservancy information system moves into the cloud, the data collected into the cloud data center contain a large amount of "dirty data" (such as disorder, anomaly, similar repetition, false alarm, incomplete) caused by various subjective and objective reasons, such as equipment, human beings, environment and so on. The large amount of "dirty data" will bring high processing cost to the application system, prolong the response time, even lead to abnormal data analysis, reduce the accuracy of decision support system, and seriously affect the quality of service of the system. It is difficult to support the upper application. According to the actual situation of the project, this paper gives the flow and method of cleaning these dirty data, and validates the validity of the data cleaning scheme through the actual data and experimental scheme, which greatly improves the efficiency of prediction and early warning of water conservancy information system.
【作者单位】： 四川信息职业技术学院;
【分类号】：TV21;TP311.13
，
本文编号：2305022
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