基于层次序列OLAP-Cache在大规模监测数据查询优化中的应用

利用OLAP进行大规模监测数据查询时存在效率低和实时性差的问题,传统解决方法主要利用缩小数据集市数据的方法,虽然在一定程度上提高了查询效率,但是会出现丢失数据的现象。提出在基于聚类方法基础之上进行层次序列OLAP查询优化,并针对大规模海洋监测数据的特点提出OLAPCache多级查询方法。在该算法中,首先分析OLAP序列立方体的查询效率问题;其次以等价类的层次关系为基础,确定等价的上下界和聚类值;然后利用改进的各级查询层次的运算策略来判断OLAP查询的语义特性,提高其Cache性能;最后结合OLAP查询集的闭包中存在最小蕴含关系的特点,对OLAP查询的蕴含关系进行分组查找。通过大规模监测的海洋数据对该算法进行仿真验证,仿真表明,该算法有效地避免了对大规模海量数据的递归查询,具有较好的执行效率。

Hierarchical Sequence OLAP-Cache-Based Query Optimization in Large-Scale Monitoring Data

Abstract: The problem of low efficiency and poor real-time large-scale monitoring data query, the traditional solution using mainly narrow the data mart data using OLAP, although to some extent, improve query performance, but data loss phenomenon. In this paper, based clustering method based on the hierarchical sequence OLAP query optimization, and for the characteristics of the large-scale ocean monitoring data OLAP-Cache multi-level query methods. In this algorithm, the first analysis of the OLAP sequence cube query efficiency problems; followed equivalence class hierarchical relationship, to determine the equivalent of the upper and lower bounds and clustering value; then improved at all levels, and query-level computing strategy to judgment OLAP query semantic features, improve its Cache performance characteristics of the minimum implication relations; closure Finally OLAP query set, Find a group OLAP query contains relations. Through large-scale monitoring of marine data, the algorithm simulation, simulation results show that the algorithm is effective to avoid recursive queries on large-scale mass data with better execution efficiency.