TY - EJOU
AU - Qi, Wang
TI - Research on K Maximum Dominant Skyline and E-GA Algorithm Based on Data Stream Environment
T2 - Computer Systems Science and Engineering
PY - 2018
VL - 33
IS - 5
SN -
AB - With the continuous development of database technology, the data volume that can be stored and processed by the database is increasing. How to dig out
information that people are interested in from the massive data is one of the important issues in the field of database research. This article starts from the
user demand analysis, and makes an in-depth study of various query expansion problems of skylines. Then, according to different application scenarios,
this paper proposes efficient and targeted solutions to effectively meet the actual needs of people. Based on k- representative skyline query problem in the
data stream environment, a k-representative skyline selection standard k-LDS is presented which is applicable for data stream environment. k-LDS hopes
to select the skyline subset with the largest dominant area (containing k skyline tuples only) as k- representative skyline set in data stream. And for the
3-dimensionalal and multidimensional k-LDS problems, this paper also proposes the approximation algorithm, namely GA algorithm. Finally, through the
experiment, it is proved that k-LDS is more suitable for the data stream environment, and the algorithm proposed can effectively solve k-LD problems under
the data stream environment.
KW - Skyline query
KW - k-maximum dominant skylines
KW - greedy algorithm
KW - ε-GA algorithm
DO - 10.32604/csse.2018.33.369