Robust and fast similarity search for moving object trajectories

An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods.

[1]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[2]  Daphna Weinshall,et al.  Classification with Nonmetric Distances: Image Retrieval and Class Representation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Dieter Pfoser,et al.  Novel Approaches in Query Processing for Moving Object Trajectories , 2000, VLDB 2000.

[4]  Dimitrios Gunopulos,et al.  Indexing multi-dimensional time-series with support for multiple distance measures , 2003, KDD '03.

[5]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[6]  A. Tversky.  Features of Similarity , 1977 .

[7]  Margrit Betke,et al.  THE CAMERA MOUSE: PRELIMINARY INVESTIGATION OF AUTOMATED VISUAL TRACKING FOR COMPUTER ACCESS , 2000 .

[8]  Alexandr Andoni,et al.  Lower bounds for embedding edit distance into normed spaces , 2003, SODA '03.

[9]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[10]  Deok-Hwan Kim,et al.  Similarity search for multidimensional data sequences , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[11]  Eamonn J. Keogh,et al.  Scaling up dynamic time warping for datamining applications , 2000, KDD '00.

[12]  Joachim M. Buhmann,et al.  Going Metric: Denoising Pairwise Data , 2002, NIPS.

[13]  Edward Y. Chang,et al.  DynDex: a dynamic and non-metric space indexer , 2002, MULTIMEDIA '02.

[14]  Divyakant Agrawal,et al.  BFT: Bit Filtration Technique for Approximate String Join in Biological Databases , 2003, SPIRE.

[15]  Raymond T. Ng,et al.  Indexing spatio-temporal trajectories with Chebyshev polynomials , 2004, SIGMOD '04.

[16]  Lei Chen,et al.  On the Marriage of Edit Distance and Lp Norms , 2004, VLDB 2004.

[17]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[18]  Lei Chen.  Similarity search over time series and trajectory data , 2005 .

[19]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[20]  Ambuj K. Singh,et al.  Variable length queries for time series data , 2001, Proceedings 17th International Conference on Data Engineering.

[21]  Erkki Sutinen,et al.  Filtration with q-Samples in Approximate String Matching , 1996, CPM.

[22]  Christos Faloutsos,et al.  Efficiently supporting ad hoc queries in large datasets of time sequences , 1997, SIGMOD '97.

[23]  Dimitrios Gunopulos,et al.  Rotation invariant distance measures for trajectories , 2004, KDD.

[24]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[26]  Rangasami L. Kashyap,et al.  A Spatio-Temporal Semantic Model for Multimedia Database Systems and Multimedia Information Systems , 2001, IEEE Trans. Knowl. Data Eng..

[27]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[28]  James J. Little,et al.  Video retrieval by spatial and temporal structure of trajectories , 2001, IS&T/SPIE Electronic Imaging.

[29]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[30]  Hans-Jörg Schek,et al.  A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.

[31]  Eamonn J. Keogh,et al.  Making Time-Series Classification More Accurate Using Learned Constraints , 2004, SDM.

[32]  Dina Q. Goldin,et al.  On Similarity Queries for Time-Series Data: Constraint Specification and Implementation , 1995, CP.

[33]  Luis Gravano,et al.  Approximate String Joins in a Database (Almost) for Free , 2001, VLDB.

[34]  Tetsuji Satoh,et al.  Shape-Based Similarity Query for Trajectory of Mobile Objects , 2003, Mobile Data Management.

[35]  Christos Faloutsos,et al.  Fast Time Sequence Indexing for Arbitrary Lp Norms , 2000, VLDB.

[36]  Graham Cormode,et al.  The string edit distance matching problem with moves , 2007, TALG.

[37]  Esko Ukkonen,et al.  Two Algorithms for Approximate String Matching in Static Texts , 1991, MFCS.

[38]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[39]  Nasser Yazdani,et al.  Matching and indexing sequences of different lengths , 1997, CIKM '97.

[40]  Eamonn J. Keogh,et al.  Locally adaptive dimensionality reduction for indexing large time series databases , 2001, SIGMOD '01.

[41]  Dimitrios Gunopulos,et al.  A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series , 2003 .