Application of Complex Event Processing Techniques to Big Data Related to Healthcare: A Systematic Literature Review of Case Studies

Application of Complex Event Processing Techniques to Big Data Related to Healthcare: A Systematic Literature Review of Case Studies

Fehmida Mohamedali (University of West London, UK) and Samia Oussena (School of Computing and Technology, University of West London, UK)
Copyright: © 2016 |Pages: 20
DOI: 10.4018/978-1-5225-0293-7.ch012
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Abstract

Healthcare is a growth area for event processing applications. Computers and information systems have been used for collecting patient data in health care for over fifty years. However, progress towards a unified health care delivery system in the UK has been slow. Big Data, the Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with treatment areas of healthcare domain but also to redefine healthcare services. This study is intended to provide a broad overview of where in the health sector, the application of CEP is most used, the data sources that contribute to it and the types of event processing languages and techniques implemented. By systematic review of existing literature on the application of CEP techniques in Healthcare, a number of use cases have been identified to provide a detailed analysis of the most common used case(s), common data sources in use and highlight CEP query language types and techniques that have been considered.
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Complex Event Processing (Cep)

Complex event processing refers to the processing of representations of events possibly, thousands of events, in a form that is suitable for automated processing. An event is simply “something that happens” in real life. Event objects as these representations are called include data such as where and when the event happened, how long it took and if it was caused by other events.

Business enterprises are swamped by 100,000 to 100 million events per second, originating in their application systems, sensors, social applications, the Web and other sources. RFID readers, bar code scanners, and other devices detect the presence of objects and send events through Internet-based event-processing networks (EPNs) to CEP-enabled servers that maintain virtual representations for each object. Within healthcare, CEP engines can analyse events and related data which come from various sources (health sensors, environment sensors etc.) in real-time and provide insights for a better healthcare.

The CEP technology is aimed to provide applications with a flexible and scalable mechanism for constructing condensed, refined views of the data. It correlates the data (viewed as events streams) in order to detect and report meaningful predefined patterns, thus supplying the application with an effective view of the accumulated incoming data (events), and allowing the application to react to the detections by executing actions (Magid, Adi, Barnea, Botzer & Rabinovich, 2008).

CEP encompasses methods, techniques, and tools for processing events from a variety of sources in real time; while they occur in a continuous and timely manner. It derives valuable higher-level knowledge from lower-level events. This is referred as complex events; combination of several events.

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