Data Clutter Reduction in Sampling Technique

Abstract

Visualization is a process of converting data into its visual form as such data patterns can be extracted from the data. Data patterns are knowledge hidden behind the data. However, when data is big, it tends to overlap and clutter on visualization which distorts the data patterns. Data is overly crowded on visualization thus, it has become a challenge to extract knowledge patterns. Besides, big data is costly to visualize because it requires expensive hardware facilities due to its size. Moreover, it is timely to plot the data since it takes time for data to render on visualizations. Due to those reasons, there is a need to reduce the size of big datasets and at the same time maintain the data patterns. There are many methods of data reduction, which are preprocessing operations, dimension reduction, compression, network theory, redundancy elimination, data mining, machine learning, data filtering and sampling techniques. However, the commonly used data reduction technique is sampling technique that derives samples from data populations. Thus, sampling technique is chosen as a study for data reduction in this paper. However, the studies are scattered and are not discussed in a single paper. Consequently, the objective of this paper is to collect them in a single paper for further analysis in order to understand them in great detail. To achieve the objective, three interdisciplinary databases which are ACM Digital Library, IEEE Explore and Science Direct have been selected. From the database, a total of 48 studies have been extracted and they are from the years 2017 to 2021. Other than sampling techniques, this paper also seeks information on big data, data visualization, data clutter, and data reduction.