The exponential expansion of real-time data streams across multiple domains needs the development of effective event detection, correlation, and decision-making systems. However, classic Complex Event Processing (CEP) systems struggle with semantic heterogeneity, data interoperability, and knowledge driven event reasoning in Big Data environments. To solve these challenges, this research work presents an Ontology based Complex Event Processing (OCEP) framework, which utilizes semantic reasoning and Big Data Analytics to improve event driven decision support. The proposed OCEP architecture utilizes ontologies to support reasoning to event streams. It ensures compatibility with different data sources and lets you find the events based on the context. The Resource Description Framework (RDF) organizes event data, and SPARQL query enables rapid event reasoning and retrieval. The approach is implemented within the Hadoop environment, which consists of Hadoop Distributed File System (HDFS) for scalable storage and Apache Kafka for real-time CEP based event execution. We perform a real-time healthcare analysis and case study to validate the model, utilizing IoT sensor data for illness monitoring and emergency responses. This OCEP framework successfully integrates several event streams, leading to improved early disease detection and aiding doctors in decision-making. The result shows that OCEP predicts event detection with an accuracy of 85%. This research work utilizes an OCEP to solve the problems with semantic interoperability and correlation of complex events in Big Data analytics. The proposed architecture presents an intelligent, scalable and knowledge driven event processing framework for healthcare based decision support.

翻译：跨多个领域的实时数据流呈指数级增长，迫切需要开发有效的事件检测、关联与决策系统。然而，传统复杂事件处理系统在大数据环境中面临语义异构性、数据互操作性及知识驱动事件推理的挑战。为应对这些挑战，本研究提出一种基于本体的复杂事件处理框架，该框架利用语义推理与大数据分析增强事件驱动的决策支持。所提出的OCEP架构运用本体支持对事件流的推理，确保与异构数据源的兼容性，并支持基于上下文的事件发现。资源描述框架用于组织事件数据，SPARQL查询则实现快速的事件推理与检索。该方案在Hadoop环境中实施，其中Hadoop分布式文件系统提供可扩展存储，Apache Kafka支持基于CEP的实时事件执行。我们通过实时医疗分析与案例研究验证模型，利用物联网传感器数据进行疾病监测与应急响应。该OCEP框架成功整合了多路事件流，显著提升了早期疾病检测能力，并为医生决策提供支持。实验结果表明OCEP事件检测预测准确率达到85%。本研究通过OCEP解决了大数据分析中语义互操作性与复杂事件关联的难题，所提架构为医疗决策支持提供了一个智能化、可扩展且知识驱动的事件处理框架。