Efficient Real-Time Data Processing Using Parallel Ant and Bee Colony Algorithms

Real-time data processing is critical in several recent popular systems, such as real-time monitoring and control systems, financial trading platforms, and social media analytics. These systems, which demand real-time functions, must act quickly and respond rapidly to what they see in massive data streams. Traditional means of data processing could not support customized functions as real-time applications required to process huge volumes of unprocessed raw sensor signals in such special processes that were ultrafast and able to work from endless streamlines, especially concurrent traffic across many different nodes. One solution to such scalability issues lies in the ability of parallel computing, which could rectify this by enabling data processing at a high computational speed. This paper proposes a new method for parallel Ant and Bee Colony humming with real-time data mining. Inspired by the swarm behavior of ant and bee colonies, these algorithms have successfully optimized problems within many fields. The algorithm leverages the parallel nature of these algorithms, which allows efficient processing of large data volumes in realtime. Our experiments show the effectiveness of our approach on real-world datasets. Results The results suggest that our approach outperforms traditional data processing algorithms regarding speed and resource utilization. It could disrupt the real-time data processing paradigm and improve many systems that need a ‘fast’ & efficient way of managing their consistently aggregated live feed.