Event Processing for Business: Organizing the Real-Time Enterprise reminds me the most important part of information technology is the information.
Unfortunately, information and deriving actionable business meaning from it does not get nearly the attention it deserves. In fact I would recommend it more for the non-IT manager so that they can develop a better understanding of what is currently possible. This book will shift your focus back onto the events, the attributes of those events and processing the attribute data into actionable information.
The continuous search occurs against the event cloud of the organization where events are those things that have occurred and those things that are expected to occur.
The notion of continuous search for the answers also requires that the search occur in Right Now Time aka Real Time. Non-IT managers will also appreciate the generous use of examples to draw out the distinctions which is essentially that BI is about monitoring and tracking usually from offline data whereas CEP is focused on Right Now to support interventions. This is the difference between using reactive vs proactive systems to run your business. This perspective should also help you understand why there are so many struggles with BI projects and business dashboards.
Event Processing for Business is full of practical illustrations and examples for getting started. Abundant sensors and smart devices deployed in the Internet of Things pose the potential for IoT applications to detect high-level meaningful events.
GitHub - hs/awesome-cep: Awesome sites about Complex Event Processing
Complex Event Processing technology offers solutions of event pattern complex event queries over streams in real time well timely. Yet when CEP is detecting complex events that are continuous for some time, it results in detecting out multiple reduplicate pattern matches, leading to the burden of high output throughput and unnecessary disturb to IoT applications.
The Event-Feedback Mechanism is shown to achieve over three orders of magnitude performance in relieving output throughput, and a range of tested scenarios compared to a significant algorithm proves it practical and effective. Unable to display preview. Download preview PDF.
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Agrawal, J. Mozafari, B. Fengjuan, W. In: CASE, pp. Zhang, H.
Jun, C. Govindarajan, N. Computer Society of India, pp. Wang, Y. Mayer, R.