Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Sequential pattern matching and retrieving is of real value.For example, finding stocks in the NASDAQ market whose closing prices are always about $β驴 higher than or β驴 times as that of a given company.The probelm reduces to linear pattern retrieval: given query X, find all sequence Y from database S so that Y = β驴 + β驴 with confidence C. In this paper, we novelly introduce SLR (Simple Linear Regression) model [5,7] to solve this problem.We extend 1-dimensional R^2 to ER^2 for multi-dimensional sequence matching, such as on-line handwritten signature.In addition, we develop SLR+FFT pruning techniques based on SLR to speed up retrieval without incurring any false dismissal. Experimental results show that the pruning ratio of SLR+FFT is efficient (can be above 99%).Experiments on real stocks discovered many interesting patterns.Preliminary test on on-line signature recognition using ER^2 as similarity measure also shows high accuracy.