对等网络下自适应层级的矢量数据时空索引构建方法

Adaptive hierarchical spatio-temporal index construction method for vector data under peer-to-peer networks

时空索引是时空数据存储和管理的关键技术之一,基于空间填充曲线(space filling curve,SFC)的索引方法近年来受到了广泛关注。然而对于矢量数据,现有索引方法多侧重于空间索引的实现,难以同时顾及时间查询和空间查询的效率,且对于非点要素(线要素与面要素),确定最优的索引级别一直是难点所在。为此,本文面向对等网络环境,提出一种自适应层级的时空索引构建方法。首先提出了基于分区键和分区内排序键组合策略的时空信息联合编码,然后据此设计了点要素、非点要素的时空表达结构,最后设计了多层级树结构以构建时空索引MLS3(multi-level sphere 3),并基于地理实体时间粒度及空间密度等特征自适应确定其最优索引层级。利用轨迹(点要素)、公路(线要素)和建筑物(面要素)实际数据进行了试验。试验结果表明,相比GeoMesa提出的XZ3时空索引,本文索引方法可有效解决非点要素的时空表达及层级划分问题,在避免存储热点的同时实现更为高效的时空检索。

Spatio-temporal index is one of the key technologies for storage and management of spatio-temporal data.Index methods based on spatial filling curve(SFC)have drawn wide attention in recent year.However,the existing methods for the vector data mostly focus on the implementation of spatial index,which is difficult to take into account both the efficiency of time query and spatial query.For non-point elements(line elements and polygon elements),it is always difficult to determine the optimal index level.Therefore,this paper proposes an adaptive hierarchical spatio-temporal index construction method for vector data under peer-to-peer networks.Firstly,a joint coding of spatio-temporal information based on the combination strategy of partition key and sort key is proposed.Then,the spatio-temporal expression structure of point elements and non-point elements are designed.Finally,an adaptive multi-level tree is proposed to realize the spatio-temporal index(multi-level sphere 3,MLS3)based on the spatio-temporal characteristics of geographical entities.Experiments are carried out using actual data of trajectory(point elements),highway(line elements)and building(surface elements)data.By comparing with the XZ3 indexing algorithm proposed by GeoMesa,it is proved that the indexing method in this paper can effectively solve the problems of hierarchical division and spatio-temporal expression of non-point elements,and can effectively avoid storage hotspots while achieving efficient spatio-temporal retrieval.