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