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

ABSTRACT

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

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