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Statistical methods for complex event processing and real time decision making

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While there has been a lot of attention paid recently to big data, in which data is written to massive repositories for later analysis, there also is a rapidly increasing amount of data available in the form of data streams or events. Data streams typically represent very recent measurements or current system states. Events represent things that happen, often in the context of computer processing. When processing data streams or events, we often need to make decisions in real time. Complex event processing (CEP) is an important area of computer science that provides powerful tools for processing events and analyzing data streams. CEP deals with events that can be comprised of other events and can model complex phenomena like a user's interactions with a web site or a stock market crash. In the current literature, CEP is almost entirely deterministic, that is, it does not account for randomness or rely on statistical methods. However, statistics and machine learning have a critical role to play in the use of data streams and events. Also, understanding how CEP works is critical to analyzing data based on complex events. When processing data streams, a distinction must be made between analysis, the human activity in which we try to gain understanding of an underlying process, and decision making, in which we apply knowledge to data to decide what action to take. Useful statistical techniques for data streams include smoothing, generalized additive models, change point detection, and classification methods. WIREs Comput Stat 2016, 8:5–26. doi: 10.1002/wics.1372 This article is categorized under: Data: Types and Structure > Streaming Data Statistical Learning and Exploratory Methods of the Data Sciences > Streaming Data Mining Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization
Smoothed call arrival rates for a call center for one week, showing interday and intraday patterns.
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Actual call arrivals over time for a call center queue (jittered), with a smoothed estimate of the call arrival rate.
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Moving totals of calls handled and talk time, along with smoothed average talk time.
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Simple moving average (blue) and one‐sided LOESS (yellow) smoothes based on simulated data, plotted along with the actual mean function (red).
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Agents staffed plus smoothed values of several other call center metrics.
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GAM model fitted to the one week of call arrival data.
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Statistical Learning and Exploratory Methods of the Data Sciences > Streaming Data Mining
Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization
Data: Types and Structure > Streaming Data

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