An Efficient Approximation Algorithm for Multi-criteria Indoor Route
  Planning Queries
  A route planning query has many real-world applications and has been studied
extensively in outdoor spaces such as road networks or Euclidean space. Despite
its many applications in indoor venues (e.g., shopping centres, libraries,
airports), almost all existing studies are specifically designed for outdoor
spaces and do not take into account unique properties of the indoor spaces such
as hallways, stairs, escalators, rooms etc. We identify this research gap and
formally define the problem of category aware multi-criteria route planning
query, denoted by CAM, which returns the optimal route from an indoor source
point to an indoor target point that passes through at least one indoor point
from each given category while minimizing the total cost of the route in terms
of travel distance and other relevant attributes. We show that CAM query is
NP-hard. Based on a novel dominance-based pruning, we propose an efficient
algorithm which generates high-quality results. We provide an extensive
experimental study conducted on the largest shopping centre in Australia and
compare our algorithm with alternative approaches. The experiments demonstrate
that our algorithm is highly efficient and produces quality results.
