Research on Big Data Query Online Analysis and Processing Technologyon the Basis of Decision Tree Model

摘要

In this paper, we prompt a new big data query online analysis and processing method based on decision treemodel. The purpose of this dissertation is to realize the high-efficiency of multi-dimensional OLAP query by studying thekey approaches for improving the OLAP query efficiency. In order to bring the full play of data analysis, the concept ofon-line analytical mining (OLAM) is used for reference in this dissertation. Data mining techniques and statistical analysisapproaches are integrated to form an OLAP query frame. Then the key techniques are studied in the framework. A newkind of OLAP query which is different from traditional cube query is proposed, called muti-boid query. This query can beused in the moving objects network. And we implement graph cubes through the combination of moving objects networkcharacteristics and existing data cube technology.