Efficient Indexing for Spatiotemporal Applications

时空数据的索引是许多应用（全球变化，交通，社会和多媒体应用）的一个重要问题。该项目的目标是为几何形状随时间变化的数据提供有效的访问方法。两个时变的空间属性被认为是，对象的位置和范围。基于这些空间属性的变化率，离散和连续的时空环境被识别。在离散环境中，时空数据以离散的步长变化。有效的方法来回答任何过去的状态，这样的时空数据的历史查询。特别是，选择，邻居，聚合，连接和相似性查询使用“部分持久性”的方法来解决。在连续的时空环境中，数据是不断变化的。代替在离散时间保持数据位置/范围（这将导致巨大的更新/存储需求），存储该数据变化的函数。这引入了索引函数的新问题。使用这种方法，选择，邻居和聚合查询移动对象的未来位置在一个和两个维度。该项目中使用的方法预计将比传统的访问方法至少提高30%。已完成的工作的适用性达到多个设置，包括地理信息系统，多媒体数据库和交通系统。通过项目网页http://www.cs.ucr.edu/~tsotras/spatio-temporal.html传播项目结果。

Indexing spatiotemporal data is an important problem for many applications (global change, transportation, social and multimedia applications). The goal of this project is to provide efficient access methods for data whose geometry changes over time. Two time-varying spatial attributes are considered, the object position and extent. Based on the rate by which these spatial attributes change, the discrete and continuous spatiotemporal environments are identified. In the discrete environment, spatiotemporal data changes in discrete steps. Efficient ways to answer historical queries on any past state of such spatiotemporal data are examined. In particular, selection, neighbor, aggregate, join and similarity queries are addressed using a "partial persistence" methodology. In the continuous spatiotemporal environment, data changes continuously. Instead of keeping the data position/extent at discrete times (which would result in enormous update/storage requirements) the functions by which this data changes are stored. This introduces the novel problem of indexing functions. Using this approach, selection, neighbor and aggregation queries about future locations of moving objects in one and two dimensions are addressed. The methods used in this project are expected to achieve at least 30% improvement over traditional access methods. The applicability of the completed work reaches multiple settings, including Geographic Information Systems, multimedia databases and transportation systems. Dissemination of project findings is provided through the project's web page: http://www.cs.ucr.edu/~tsotras/spatio-temporal.html.