Data management and data mining in spatio-temporal databases

时空数据正在以越来越快的速度收集，例如，通过卫星图像，GPS系统，移动的通信网络，医学成像技术，传感器网络，或视频观测等。在这个建议中，我们认为时空数据在其“原始”形式的数据是（部分）描述的位置和/或对象的扩展在一个空间参考框架随着时间的推移。空间参照系可以是例如地球表面的部分、城市的街道网络、人类大脑的模型、建筑物、由远程操作的车辆探索的水下区域的部分等。人们带着GPS使能设备四处移动）或移动和演变的几何形状（例如，森林火灾）。这类数据的可用性增加为应用领域的高级服务和数据分析提供了巨大的机会，这些应用领域包括移动的计算和商业、车队控制、环境和社会经济现象的监测和分析、交通控制、动物跟踪、肿瘤放射治疗规划、流行病学分析等。针对大量时空数据的先进数据分析和查询技术仍处于早期阶段。一方面，地理信息系统虽然为空间数据提供了复杂的分析和显示方法，但还不能处理大量数据和探索性时空分析的复杂性。另一方面，对象关系数据库系统，它提供了有效的访问方法，为非空间和在某种程度上的空间数据，还没有支持时空数据在一个有效的和高效的方式（虽然近年来的研究已经取得了显着进步，在该领域的建模和索引轨迹数据的基本查询处理）。该研究计划的全球目标是弥合这一差距，并设计通用数据库技术，通过开发有效和高效的方法和算法来表示，存储，查询和挖掘不同尺度的时空数据，以支持广泛的高级应用程序，分析和服务。

Spatio-temporal data is being collected at an increasing rate, e.g., through satellite images, GPS systems, mobile communication networks, medical imaging technology, sensor networks, or video observations etc. In this proposal, we consider spatio-temporal data in its "raw" form to be data that is (partially) described by locations and/or extensions of objects in a spatial frame of reference over time. Spatial frames of reference can be, for instance, parts of the earth's surface, a city's street network, a model of the brain of humans, a building, parts of an underwater area explored by a remotely operated vehicle, etc. Spatio-temporal data often represents moving objects (e.g., people moving around with a GPS enabled device) or moving and evolving geometries (e.g., forest fires). The increased availability of this type of data presents a great opportunity for advanced services and data analyses in application areas such as mobile computing and commerce, fleet control, monitoring and analyzing of environmental and socioeconomic phenomena, traffic control, animal tracking, radiation treatment planning for tumours, epidemiological analyses, etc. However, advanced data analysis and querying techniques for large volumes of spatio-temporal data are still in its early stages. On the one hand, geographic information systems (GIS), although they provide sophisticated analysis and display methods for spatial data, are not yet ready to deal with large data volumes and the complexity of exploratory spatio-temporal analysis. On the other hand, object-relational database systems, which provide efficient access methods for non-spatial and to some degree spatial data, do not yet support spatio-temporal data in an effective and efficient way (although research in recent years has advanced significantly in the area of modeling and indexing trajectory data for basic query processing). The global objective of this research program is to bridge this gap, and to devise general database technology to support a wide range of advanced applications, analyses, and services by developing effective and efficient methods and algorithms for representing, storing, querying, and mining of spatio-temporal data at different scales.