RELAXED REVERSE NEAREST NEIGHBORS QUERIES Arif Hidayat Muhammad Aamir Cheema David Taniar

 Motivation  Problem Definition  Technique  Experiment  Conclusion

1 km 1.5 km

 Compute regions on which users cannot be RRNN of q  New pruning rule  Six-regions and half-space pruning not applicable in RRNN problem

 Proof:

 The pruning rule is tight (proof is in the paper)

Prune users using defined pruning regions Straightforward approach:  Store pruning regions in a list  Check user against entries in the list  O(n) Our approach:  Define interval for each pruning region  Build interval tree for each partition  Check users against overlapped interval  O(log n + k) RRNN Candidates

More techniques:  Computing interval  Trimming

 Implemented in C++  Run on Intel Core I5 2.3GHzx4 PC with 8GB memory running on Debian Linux  Users and facilities are indexed with R*-Tree  Each experiment runs 100 queries ParameterValues Data size2K, 200K, 2M, 20M x factor1.1, 1.3, 1.5, 2, 4 Real data setNA, LA, CA 13

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 No previous method for RRNN problem  We compare with naïve range query and improved algorithms

Our algorithm is several orders of magnitude better than improved algorithm