  In this paper, we propose and study the problem of top-m rank aggregation of
spatial objects in streaming queries, where, given a set of objects O, a stream
of spatial queries (kNN or range), the goal is to report m objects with the
highest aggregate rank. The rank of an object w.r.t. an individual query is
computed based on its distance from the query location, and the aggregate rank
is computed from all of the individual rank orderings. Solutions to this
fundamental problem can be used to monitor the popularity of spatial objects,
which in turn can provide new analytical tools for spatial data. Our work draws
inspiration from three different domains: rank aggregation, continuous queries
and spatial databases. To the best of our knowledge, there is no prior work
that considers all three problem domains in a single context. Our problem is
different from the classical rank aggregation problem in the way that the rank
of spatial objects are dependent on streaming queries whose locations are not
known a priori, and is different from the problem of continuous spatial queries
because new query locations can arrive in any region, but do not move. In order
to solve this problem, we show how to upper and lower bound the rank of an
object for any unseen query. Then we propose an approximation solution to
continuously monitor the top-m objects efficiently, for which we design an
Inverted Rank File (IRF) index to guarantee the error bound of the solution. In
particular, we propose the notion of safe ranking to determine whether the
current result is still valid or not when new queries arrive, and propose the
notion of validation objects to limit the number of objects to update in the
top-m results. We also propose an exact solution for applications where an
approximate solution is not sufficient. Last, we conduct extensive experiments
to verify the efficiency and effectiveness of our solutions.
