针对传统的时空索引构建、维护困难且实时查询效率低等问题,提出基于HBase的时空索引构造方法.该方法采用HBase作为监测视频大数据时空特征索引结构,通过Z填充曲线对空间特征进行降维存储,并利用时间、空间与属性特征之间的关联及依赖规则来安排rowkey索引键,可有效解决传统的时空索引构建、维护困难的缺陷.针对传统的时空索引实时查询效率低的问题,提出了基于Z曲线的时空关联查询算法.该算法对查询空间计算Z值范围和建立空间划分子集,利用划分后的时空特征进行列索引查询得到候选数据集并反查HBase索引表完成关联查询.实验结果表明,与传统的R树索引算法相比,提出的基于HBase的时空索引构造方法索引插入效率更高,提出的基于Z曲线的时空关联查询算法能够快速高效地处理时空关联查询.%Aiming at solving problems like the difficulties in building and maintaining of the traditional spatio-temporal index,and the inefficiency of real-time query,this paper first proposed a new building method of spatio-temporal index based on HBase.By using HBase as the spatio-temporal features index structure of monitoring video big data,using Z-filling curve to fulfill the dimension reduction and storage of spatio-temporal features,and using the associated relations and the dependency rule between time,space and attributive characters to arrange the rowkey index key,these disadvantages like the difficulties of traditional spatio-temporal index's building and maintaining would be overcome efficiently.In addition,aiming at solving the traditional space-time index low query efficiency issues in real time,this paper further proposed spatio-temporal association query algorithm based on the Z curve,which could calculate Z value ranges and establish space to subset partion,and then the candidate data sets would be obtained by using the spatio-temporal features got from the first step to have indexed searching,and next the related query would be realized by pegging the HBase index table.Experimental results and analysis show that,compared with the conventional R-tree indexing algorithm,the spatio-temporal index building method based on HBase proposed above has higher insertion index efficiency and the association Z curve space-time query-based algorithm proposed can quickly and efficiently deal with spatio-temporal correlated queries.
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