There exist numerous indexing methods which handle either spatio-temporal or high-dimensional data well. However, those indexing methods which handle spatio-temporal data well have certain drawbacks when confronted with high-dimensional data. As the most efficient spatio-temporal indexing methods are based on the R-tree and its variants, they face the well known problems in high-dimensional spa...

The requirements for efficient access and management of massive multi-dimensional spatio-temporal data in geographical information system and its applications are well recognized and researched. The most popular spatio-temporal access method is the R-Tree and its variants. However, it is difficult to use them for parallel access to multi-dimensional spatio-temporal data because R-Trees, and var...

With the growing advancement of pervasive computing technologies, we are moving towards an era where spatio-temporal information will be necessary for access control. The use of such information can be used for enhancing the security of an application, and it can also be exploited to launch attacks. For critical applications, a formal model for spatio-temporal-based access control is needed tha...

Chickenpox is a highly contagious airborne disease caused by Varicella zoster, which affects nearly all non-immune children worldwide with an annual incidence estimated at 80-90 million cases. To analyze the spatiotemporal pattern of the chickenpox incidence in the city of Valencia, Spain two complementary statistical approaches were used. First, we evaluated the existence of clusters and spati...

The goal of data mining is to discover nuggets. Spatial data mining discovers collocation rules. Especially in spatial data mining, when spatial data is relatively represented with time series, a spatio-temporal significance is inferred. In this context the collocation rule that is a quintessence for the spatial data, obtains changes to its size and shape with temporal influence. Thus, the chan...

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forecast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the inte...

Temporal and spatio-temporal aggregations are important but costly operations for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). In this paper we examine the problem of computing such aggregates over data streams. The aggregates are maintained using multiple levels of temporal granularities: older data is aggregated using coarser granularities while m...

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effective...

Indexing and query processing is an emerging research field in spatio temporal data. Most of the real-time applications such as location based services, fleet management, traffic prediction and radio frequency identification and sensor networks are based on spatiotemporal indexing and query processing. All the indexing and query processing applications is any one of the forms, such as spatio in...

In the geometric data model for spatio-temporal data, introduced by Chomicki and Revesz [1], spatio-temporal data are modelled as a finite collection of triangles that are transformed by time-dependent affinities of the plane. To facilitate querying and animation of spatio-temporal data, we present a normal form for data in the geometric data model. We propose an algorithm for constructing this...