A Quadtree-Based Dynamic Attribute Indexing Method

Abstract

Dynamic attributes are attributes that change continuously over time making it impractical to issue explicit updates for every change. In this paper, we adapt a variant of the quadtree structure to solve the problem of indexing dynamic attributes. The approach is based on the key idea of using a linear function of time for each dynamic attribute that allows us to predict its value in the future. We contribute an algorithm for regenerating the quadtree-based index periodically that minimizes CPU and disk access cost. We also provide an experimental study of performance focusing on query processing and index update overheads.

Main Memory Evaluation of Monitoring Queries Over Moving Objects

“…Tayeb et al [27] use quadtrees to index the trajectories of one-dimensional moving points. Kollios [16] et al map moving objects and their velocities into points and store the points in a kD-tree.…”

The Geometry of Uncertainty in Moving Objects Databases

“…For the purpose of query processing, we assume an available 3D indexing scheme in the underlying DBMS, similar to the ones proposed in [17,28,34]. The insertion of a trajectory is done by enclosing, for each trajectory, each trajectory volume between t i and t i+1 in a Minimum Bounding Box (MBB).…”

“…A large body of work in moving objects databases has been concentrated on indexing in primal [17,22,20,21,28] or dual space [1,12,13]. [30,31] present specifications of what an indexing of moving objects needs to consider, and generation of spatial datasets for benchmarking data.…”

“…Largest efforts were made in the area of access methods. Aside from a purely spatial ( [8] surveys 50+ structures) and temporal databases [27], there are several recent results which tackle various problems of indexing spatio-temporal objects and dynamic attributes [1,12,13,17,22,28,30,31]. Representing and querying the location of moving objects as a function of time is introduced in [24], and the works in [36,37] address policies for updating and modeling imprecision and communication costs.…”

Buddy tracking — efficient proximity detection among mobile friends

“…A quadtree based algorithm for indexing is given by [25]. Their main idea is to use a linear function of time for each of the dynamic attributes of the object, and to provide methods to regenerate the quadtree.…”