Uniform Selection Algorithm for Big Data Random Process and Repeat Use Based on Topological Location
Jaehoon You, Cheong Youn · International Journal of Advanced Science and Technology · 2019
Since the information revolution, massive online content has been placed on the Internet, and it has become possible to use it indefinitely as long as it is in demand. In the past, such content was disposable, but products having higher frequency of use have been explosively increasing because of the development of technology. Existing algorithms are biased towards simple sorting and aggregation. In this paper, we define an algorithm based on topological location that guarantees uniform selection, weight selection, and fast processing speed for problems that occur from mass consumption and repeatable reuse of tangible and intangible products, and we attempt to demonstrate these features through experiments. Disadvantages of the simple random sampling method are that it cannot guarantee a uniform selection and that it does not offer a way to select using different weights. Hence, the proposed algorithm assigns priority to the uniform probability distribution without making an assumption about the distribution. Further, by narrowing the selection range by digitizing the selection frequency to the topographical position, uniformity of random selection is ensured, and the processing speed for mass selection can be dramatically increased.