Efficient Top-k Dominant Computing In Large Amounts of Data
In many applications, the top-k dominant query is an important operation.The operation returns a set of interesting points from a potentially huge data space.Its result size can be controlled manually.Meanwhile, its query results are affected by attributes.We analyze the existing three algorithms.Found that it requires repeated scanning when processing large amounts of data.The most of them require 3 repeated scans.And they did not terminate operations early.Therefore, the query time of the existing algorithm is not satisfactory.It is even more prominent when the amount of data is huge.Therefore, we propose a novel TDTS algorithm.It is a table scan-based algorithm.It is used to efficiently calculate top-k dominant results under mass data.This algorithm does not require repeated scans.And it also has early termination operations.Our experiment compares our algorithm with other algorithms.The experimental results show that our algorithm is very efficient.Therefore, we conclude that our algorithm successfully solves the above problems.Its query time is greatly reduced.The query efficiency is greatly improved.
component data TDTS algorithm Preliminary classification table Efficient
Zongmin Cui Yiwei Cheng Zhuolin Mei Bin Wu Guangyong Gao Zhiqiang Zhao
School of Information Science and Technology Jiujiang University,Jiujiang, Jiangxi, China
国际会议
郑州
英文
164-170
2018-09-21（万方平台首次上网日期，不代表论文的发表时间）