Categorical Data Skyline Using Classification Tree

This paper pioneer an entirely new domain for skyline query--namely, the categorical data--with which the corresponding ranking measures for the skyline queries are developed, and tested the proposed algorithm using the ACM Computing Classification System.

Abstract

Skyline query is an effective method to process large-sized multi-dimensional data sets as it can pinpoint the target data so that dominated data (say, 95% of data) can be efficiently excluded as unnecessary data objects. However, most of the conventional skyline algorithms were developed to handle numerical data. Thus, most of the text data were excluded from being processed by the algorithms. In this paper, we pioneer an entirely new domain for skyline query—namely, the categorical data—with which the corresponding ranking measures for the skyline queries are developed. We tested our proposed algorithm using the ACM Computing Classification System.