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Introduction

Cognitive computing has emerged as a powerful tool for processing and analyzing complex events in various domains. Complex event processing (CEP) involves the identification, correlation, and analysis of events in real-time to derive meaningful insights. The integration of cognitive computing techniques with CEP can enhance the efficiency and accuracy of event processing tasks. This thesis aims to explore the potential of cognitive computing for complex event processing and develop a framework that combines both approaches for improved decision-making.

1.2 Background of Study

This section will provide an overview of cognitive computing and complex event processing, highlighting their individual strengths and limitations. It will also discuss previous research efforts in integrating cognitive computing with CEP and identify gaps in the existing literature.

1.3 Problem Statement

The growing volume and complexity of data generated in today’s digital age pose a significant challenge for traditional event processing systems. There is a need for advanced techniques that can handle the dynamic and unstructured nature of events in real-time. This thesis will address the limitations of current event processing systems by leveraging the capabilities of cognitive computing.

1.4 Objective of Study

The primary objective of this study is to investigate the potential of cognitive computing for enhancing complex event processing tasks. Specific objectives include developing a framework for integrating cognitive computing with CEP, evaluating the performance of the proposed framework, and identifying potential applications in various domains.

1.5 Limitation of Study

This study may be limited by the availability of resources and the complexity of implementing cognitive computing algorithms. The generalizability of the findings may also be constrained by the specific context in which the research is conducted.

1.6 Scope of Study

The scope of this study includes the development and implementation of a cognitive computing framework for complex event processing. The evaluation will focus on the performance of the framework in handling real-time events and deriving actionable insights for decision-making.

1.7 Significance of Study

The findings of this study will contribute to the existing body of knowledge on the integration of cognitive computing with complex event processing. The proposed framework has the potential to advance the field of event processing and enable organizations to make more informed decisions in real-time.

1.8 Structure of the Thesis

Chapter 2: Literature Review
Chapter 3: System Design and Methodology
Chapter 4: System Implementation
Chapter 5: Conclusion and Summary

1.9 Definition of Terms

Cognitive computing: A field of artificial intelligence that aims to mimic human thought processes using advanced algorithms and machine learning techniques.
Complex event processing: The analysis of events in real-time to detect patterns, correlations, and anomalies for decision-making.

Thesis Overview: Cognitive Computing for Complex Event Processing

The thesis investigates the integration of cognitive computing techniques with complex event processing to enhance the efficiency and accuracy of event processing tasks. The study aims to develop a framework for real-time event processing, leveraging the capabilities of cognitive computing algorithms. The research will contribute to the existing knowledge on event processing systems and provide insights into the potential applications of cognitive computing in various domains. Chapter 2 will review the relevant literature on cognitive computing, complex event processing, and their integration. Chapter 3 will detail the system design and methodology, outlining the steps involved in developing the cognitive computing framework. Chapter 4 will present the system implementation process, including the tools and technologies used. Finally, Chapter 5 will conclude the thesis and summarize the key findings and contributions of the research.

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