Tree stream mining algorithm with Chernoff-bound and standard deviation approach for big data stream

Abstract

Data streams can be defined as data generated in the form of text, audio, or video continuously. The data stream can be categorized as structured and unstructured data. Some challenges in managing data streams include unlimited lengths, feature evolution, concept evolution, and concept drift. Chandak proposed an efficient methodology string for processing data streams and could be an alternative challenge to concept-drift, concept evolution, and infinite-length [1]. Big data is a growing trend; practical computing challenges created by data streams can be found in several types of applications. It is known that data streams are usually obtained from sensors and monitors which accumulatively can make data very large. This can result in the inability of real-time processing to be carried out. Some important points regarding the challenges and trends of big data are currently explained by Rao et al. In addition to the development of models and tools for managing big data, it is also mapping and presentation so that it can be applied to solve real-world problems [2]. One

Distance variable improvement of time-series big data stream evaluation

“…The first dataset is a traffic demand dataset, the second dataset is power system data, and the third dataset is water absorption in Chicago. We evaluate and compare our approach with the standard FIMT-DD algorithm [3], our previous improvement of the FIMT-DD Chernoff [19], and the current approach (Distance Improvement). According to the evaluation metrics (MAE, RMSE, and MAPE), our approach gives consistently lower errors compared to previous methods.…”

“…3 (Road Weather Dataset). The STD is used as the identifier for the results that are obtained by using the standard FIMT-DD algorithm, IM is used as the identifier for the Chernoff-Bound approach [19], and ED is the identifier for our new approach (distance value).…”

“…In this paper, we propose an improved method for big data stream problems. We decreased the Mean Absolute Percentage Error (MAPE) by 3% compared to our previous improvement [19], which used the Chernoff bound approach. Additionally, we decreased the MAPE by 12% compared to the standard method [3].…”