Challenges in Implementing Real-Time Stream Data in Bioinformatics

Creating efficient and adaptable methods for data analysis is crucial in bioinformatics as the amount of real-time stream data continues to grow. We describe an integrated approach that combines complex event processing (CEP), online machine learning (ML), and data stream clustering to solve the problems associated with real-time stream data processing in bioinformatics. This method brings together online machine learning, complex event processing (CEP), and clustering in data streams. Flexible, all-encompassing, and real-time research are just a few of the ways in which this innovative approach excels above more conventional ones. We use several key performance indicators to show how our approach improves upon the status quo. In this study, we looked at ten different strategies and compared their performance over a range of metrics. One of these features was the flexibility to grow or shrink without sacrificing data security, performance, latency, accuracy, or any combination thereof. According to the findings, the novel approach performs better than the conventional methods in almost all respects. Better scalability, resource usage, data integrity, throughput, latency, accuracy, and scalability were all benefits. The proposed method was proven to be a more efficient choice for real-time data processing in bioinformatics due to its increased flexibility and comprehensiveness. This allows us to surmount the obstacles given by the volatile character of biological data. Bio informaticists and other medical professionals may use its flexibility and real-time analytical abilities in a variety of contexts, from analyzing genetic data to monitoring patients' health.