Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Manipulating Interpolated Data is Easier than You Thought
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Shape-Based Similarity Query for Trajectory of Mobile Objects
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Spiders: a new user interface for rotation and visualization of n-dimensional point sets
VIS '94 Proceedings of the conference on Visualization '94
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Rotation invariant distance measures for trajectories
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Scaling and time warping in time series querying
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the VLDB Endowment
Location-Aware Query Processing and Optimization
MDM '07 Proceedings of the 2007 International Conference on Mobile Data Management
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With the drastic increase of object trajectory data, the analysis and exploration of trajectories has become a major research focus with many applications. In particular, several approaches have been proposed in the context of similarity-based trajectory retrieval. While these approaches try to be comprehensive by considering the different properties of object trajectories at different degrees, the distance functions are always pre-defined and therefore do not support different views on what users consider (dis)similar trajectories in a particular domain. In this paper, we introduce a novel approach to learning distance functions in support of similarity-based retrieval of multi-dimensional object trajectories. Our approach is more generic than existing approaches in that distance functions are determined based on constraints, which specify what object trajectory pairs the user considers similar or dissimilar. Thus, using a single approach, different distance functions can be determined for different users views. We present two learning techniques, transformed Euclidean distance and transformed Dynamic Time Warping . Both techniques determine a linear transformation of the attributes of multi-dimensional trajectories, based on the constraints specified by the user. We demonstrate the flexibility and efficiency of our approach with applications to clustering and classification on real and synthetic object trajectory datasets from different application domains.