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用于主动决策的
大数据预测分析

将决策制定从被动变为主动

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概述

Past is prelude. Historical data provides signals that indicate what may happen in the future. By understanding signals coming from machines and sensors, server logs and other new data sources, organizations can use Hortonworks big data predictive analytics to predict future events and become more proactive.

Hadoop captures, stores and processes the large volumes of data streaming from connected devices and sensors that measure your business. A combination of Hadoop predictive analytics with a variety of data science and iterative machine-learning techniques can make confident real-time recommendations that reduce costs, improve safety, and inform investments.

预测分析开辟了实现价值的新途径

预防性维护

Sensor data rapidly streams into companies and quickly grows to terabytes. Use HDP® Hadoop predictive analytics capabilities to predict when a piece of equipment may fail and then fix it before it breaks. A leading oilfield services company uses HDP to collect and analyze huge volumes of sensor data to anticipate equipment issues and perform maintenance before pumps break and jeopardize production.

石油和天然气行业使用 Hadoop

资源优化

根据变化的条件来分析资源利用率并进行调整,公司可以最大限度利用其有限资源。HDP 将资源建模所需要的所有数据和处理引擎聚合到一起并实时调整。世界最大的电力公司之一使用 HDP 来捕获大量的智能电表数据,并应用预测分析以更加准确地预测消耗。这将帮助该公司将生产与需求匹配,避免不必要的发电,同时保持不断电。

制造业使用 Hadoop »

行为洞察

通过实时数据捕获和分析来识别历史模式,可以向您表明干预的时机和方式,以免错过机会。预测可能的结果并采取操作以最大限度获得积极(或最大限度减少负面)结果。加州大学尔湾医院使用 HDP 来预测重新住院的可能性,以进行主动干预。充血性心衰患者存在体液滞留的趋势,从而导致患者变胖。UCIH 将这些心脏病患者安排家中,并留有体重秤,指示患者每天称一次体重。体重数据无线传输到 Hadoop,在 Hadoop 中,一种算法确定哪些体重变化表明了重新住院的风险,并且系统会向临床医生通知这些病例。

UCIH 案例研究 »

预测分析用例

用例 1
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如何分析机器和传感器数据

Machines know things. Sensors stream low-cost, always-on data. Hadoop makes it easier for you to store and refine that data and identify meaningful patterns, providing you with the insight to make proactive business decisions using big data predictive analytics. See how Hadoop can be used to analyze heating, ventilation and air conditioning data to maintain ideal office temperatures and minimize expenses

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