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Big data streams are possibly one of the most essential underlying notions.However, data streams are often challenging to handle owing to their rapid paceand limited information lifetime. It is difficult to collect and communicatestream samples while storing, transmitting and computing a function across thewhole stream or even a large segment of it. In answer to this research issue,many streaming-specific solutions were developed. Stream techniques imply alimited capacity of one or more resources such as computing power and memory,as well as time or accuracy limits. Reservoir sampling algorithms choose andstore results that are probabilistically significant. A weighted randomsampling approach using a generalised sampling algorithmic framework to detectunique events is the key research goal of this work. Briefly, a graduallydeveloped estimate of the joint stream distribution across all feasiblecomponents keeps k stream elements judged representative for the full stream.Once estimate confidence is high, k samples are chosen evenly. The complexityis O(min(k,n-k)), where n is the number of items inspected. Due to the factthat events are usually considered outliers, it is sufficient to extractelement patterns and push them to an alternate version of k-means as proposedhere. The suggested technique calculates the sum of squared errors (SSE) foreach cluster, and this is utilised not only as a measure of convergence, butalso as a quantification and an indirect assessment of the elementdistribution's approximation accuracy. This clustering enables for thedetection of outliers in the stream based on their distance from the usualevent centroids. The findings reveal that weighted sampling and res-meansoutperform typical approaches for stream event identification. Detected eventsare shown as knowledge graphs, along with typical clusters of events.

Pattern Recognition and Event Detection on IoT Data-streams