The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 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
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Time-series similarity problems and well-separated geometric sets
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
A fast projection algorithm for sequence data searching
Data & Knowledge Engineering - Special issue: next generation information technologies and systems
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
HierarchyScan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
On Similarity-Based Queries for Time Series Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Searches for Similar Subsequences of Different Lengths in Sequence Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
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In this paper, we propose an indexing scheme for time sequences which supports the minimum distance of arbitrary Lp norms as a similarity measurement. In many applications where the shape of the time sequence is a major consideration, the minimum distance is a more suitable similarity measurement than the simple Lp norm. To support minimum distance queries, most of the previous work has the preprocessing step for vertical shifting which normalizes each sequence by its mean. The vertical shifting, however, has the additional overhead to get the mean of a sequence and to subtract it from each element of the sequence. The proposed method can match time series of similar shape without vertical shifting and guarantees no false dismissals. In addition, the proposed method needs only one index structure to support minimum distance queries in any arbitrary Lp norm. The experiments are performed on real data (stock price movement) to verify the performance of the proposed method.