Title: An efficient algorithm for reducing the flow of real-time data stream with least sampling error
Authors: Devesh Kumar Lal; Ugrasen Suman
Addresses: School of Computer Science and IT, Devi Ahilya University, Indore, India ' School of Computer Science and IT, Devi Ahilya University, Indore, India
Abstract: Nature of data stream is determined after complete scanning of whole data sets during real-time data processing. However, it becomes inconvenient to process entire data stream at once in real-time data stream processing. Thus, a sheer sized fixed window of data streams is processed at a particular time. The intensification of sheer sized fixed window at processing node is mitigated by reducing the flowing rate of data stream. Heuristic clustering windowing (HCW) approach and partial blind window (PBW) algorithms are proposed for reducing the flow of data stream with least sampling error. These approaches consist of the combination of systematic sampling and clustering mechanism. A clustering approach is applied on one fraction of data streams whereas systematic sampling handles other portion of streams. These approaches are helpful in reducing flow of data streams in minimum latency.
Keywords: clustering approach; data stream; data processing; data sampling; real-time big data; systematic sampling.
International Journal of Big Data Intelligence, 2020 Vol.7 No.4, pp.186 - 193
Accepted: 15 Sep 2020
Published online: 15 Mar 2021 *