时空数据库索引方法研究

时空数据库作为数据库研究领域中的一个重要分支，经过近十年的发展，在时空数据模型、时空查询优化与索引和时空本体论等方面取得了许多成果。现实世界中的许多实体都具有空间特性和时态特性，需要数据库管理系统提供有效的时空数据管理能力，如地籍管理系统中的地块、交通管理系统中的车辆等。时空数据库用于管理形状和位置随时问变化的对象。为了快速访问其庞大的数据量，必须建立有效的时空索引以提高各类时空查询的效率。提出了一种新的时空索引方法（瓣索引），它综合了快照和事件这两种时空信息建模方法。不仅能够处理时间片查询和时间段查询，而且能够进行事件查询。SEST索引使用R-tree结构来存储快照，用一种日志数据结构来存储发生在两次相邻快照之间的事件。通过实验对比SEST索引和HR—tree，结果表明：当变化频率在1％到13％之间时，SEST索引比HR—tree需要的存储空间少；当变化频率在1％到7％之间时，在时间段查询方面，SEST索引比HR—tree要好。因为SEST索引是一种面向事件的结构，所以事件查询时效率很高。

Research of Spatio-Temporal Access Method

Spatiotemporal database is an important branch in the research field of database.After about ten year's development,researchers have made significant progress in areas of spatiotemporal ontology,spatiotemporal model,spatiotemporal query optimization and index.Many entities in real world have both spatial and temporal characteristics,such as parcels in land management systems,vehicles in traffic management systems and so on.As more complicated applications concerning these entities appear,it is necessary for modern database management systems to be capable of providing efficient means to manipulate spatiotemporal data.Spatiotemporal database is used to manage objects that change their locations and shapes as time passes.It is essential to build effective spatiotemporal indices in order to improve query performance because of the huge mount of spatiotemporal data in the applications.Describes a new spatiotemporal access method(SEST-Index) that combines two approaches for modeling spatiotemporal information: snapshots and events.This method makes it possible to not only process time slice and interval queries,but also queries about events.The SEST-Index implementation uses an R-tree structure for storing snapshots and a log data structure for storing events that occur between consecutive snapshots.Experimental results that compare SEST-Index and HR-tree show that,for a change frequency between 1% and 13%,SEST-Index requires less storage space than HR-tree,and for a change frequency between 1% and 7%,SEST-Index outperforms HR-tree for interval queries.In addition,as SEST-Index is an event-oriented structure,event queries are efficiently answered.