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SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Approximate matching of polygonal shapes (extended abstract)
SCG '91 Proceedings of the seventh annual symposium on Computational geometry
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
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Matching shapes with a reference point
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Adaptive multi-stage distance join processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Approximate congruence in nearly linear time
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Topology-reducing surface simplification using a discrete solid representation
ACM Transactions on Graphics (TOG)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hausdorff distance under translation for points and balls
Proceedings of the nineteenth annual symposium on Computational geometry
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
An Extensible Index for Spatial Databases
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Exact indexing of dynamic time warping
Knowledge and Information Systems
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Gorder: an efficient method for KNN join processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
A revised r*-tree in comparison with related index structures
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Similarity search on a large collection of point sets
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Searching web documents as location sets
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hausdorff distance with k-nearest neighbors
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Fast and exact network trajectory similarity computation: a case-study on bicycle corridor planning
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
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The Hausdorff distance is commonly used as a similarity measure between two point sets. Using this measure, a set X is considered similar to Y iff every point in X is close to at least one point in Y. Formally, the Hausdorff distance HausDist(X, Y) can be computed as the Max-Min distance from X to Y, i.e., find the maximum of the distance from an element in X to its nearest neighbor (NN) in Y. Although this is similar to the closest pair and farthest pair problems, computing the Hausdorff distance is a more challenging problem since its Max-Min nature involves both maximization and minimization rather than just one or the other. A traditional approach to computing HausDist(X, Y) performs a linear scan over X and utilizes an index to help compute the NN in Y for each x in X. We present a pair of basic solutions that avoid scanning X by applying the concept of aggregate NN search to searching for the element in X that yields the Hausdorff distance. In addition, we propose a novel method which incrementally explores the indexes of the two sets X and Y simultaneously. As an example application of our techniques, we use the Hausdorff distance as a measure of similarity between two trajectories (represented as point sets). We also use this example application to compare the performance of our proposed method with the traditional approach and the basic solutions. Experimental results show that our proposed method outperforms all competitors by one order of magnitude in terms of the tree traversal cost and total response time.