Showing papers by "Yufei Tao published in 2001"

MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries

Abstract: Among the various types of spatio-temporal queries, the most common ones involve window queries in time. In particular, timestamp (or timeslice) queries retrieve all objects that intersect a window at a specific timestamp. Interval queries include multiple (usually consecutive) timestamps. Although several indexes have been developed for either type, currently there does not exist a structure that can efficiently process both query types. This is a significant problem due to the fundamental importance of these queries in any spatio-temporal system that deals with historical information retrieval. Our paper addresses the problem by proposing the MV3R-tree, a structure that utilizes the concepts of multi-version B-trees and 3D-Rtrees. Extensive experimentation proves that MV3R-trees compare favorably with specialized structures aimed at timestamp and interval window queries, both in terms of time and space requirements.

Efficient OLAP Operations in Spatial Data Warehouses

Abstract: Spatial databases store information about the position of individual objects in space. In many applications however, such as traffic supervision or mobile communications, only summarized data, like the number of cars in an area or phones serviced by a cell, is required. Although this information can be obtained from transactional spatial databases, its computation is expensive, rendering online processing inapplicable. Driven by the non-spatial paradigm, spatial data warehouses can be constructed to accelerate spatial OLAP operations. In this paper we consider the star-schema and we focus on the spatial dimensions. Unlike the non-spatial case, the groupings and the hierarchies can be numerous and unknown at design time, therefore the well-known materialization techniques are not directly applicable. In order to address this problem, we construct an ad-hoc grouping hierarchy based on the spatial index at the finest spatial granularity. We incorporate this hierarchy in the lattice model and present efficient methods to process arbitrary aggregations. We finally extend our technique to moving objects by employing incremental update methods.

Efficient historical R-trees

Abstract: The historical R-tree (HR-tree) is a spatio-temporal access method aimed at the retrieval of window queries in the past. The concept behind the method is to keep an R-tree for each timestamp in history, but to allow consecutive trees to share branches when the underlying objects do not change. New branches are only created to accommodate updates from the previous timestamp. Although existing implementations of HR-trees process timestamp (window) queries very efficiently, they are hardly applicable in practice due to excessive space requirements and poor interval query performance. This paper addresses these problems by proposing the HR+-tree, which occupies a small fraction of the space required for the corresponding HR-tree (for typical conditions about 20%), while improving interval query performance several times. Our claims are supported by extensive experimental evaluation.