Efficient Indexing for Spatiotemporal Applications

对时空数据进行索引是许多应用（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。考虑了两个时变的空间属性，即目标位置和范围。基于这些空间属性的变化速率，可以识别出离散和连续的时空环境。在离散环境中，时空数据以离散的步骤变化。对此类时空数据的任何过去状态回答历史查询的有效方法进行了检查。特别是，选择、邻居、聚合、连接和相似性查询是使用“部分持久性”方法处理的。在连续的时空环境中，数据是连续变化的。而不是保持数据的位置/范围在离散的时间（这将导致巨大的更新/存储需求），这个数据变化的函数存储。这就引入了索引函数的新问题。使用该方法，解决了一维和二维中移动对象未来位置的选择、邻居和聚合查询。该项目中使用的方法预计比传统的访问方法至少提高30%。完成的工作的适用性达到多种设置，包括地理信息系统，多媒体数据库和运输系统。项目结果通过项目网页：http://www.cs.ucr.edu/~tsotras/spatio-temporal.html发布。

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.