Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Introduction to algorithms
General match: a subsequence matching method in time-series databases based on generalized windows
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Transforming sequential processes of net systems into concurrent ones
International Journal of Knowledge-based and Intelligent Engineering Systems
Parallel Dimensionality Reduction Transformation for Time-Series Data
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
An MBR-safe transform for high-dimensional MBRs in similar sequence matching
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
| Hi-index | 0.00 |
The subsequence matching in a large time-series database has been an interesting problem. Many methods have been proposed that cope with this problem in an adequate extent. One of the good ideas is reducing properly the dimensionality of time-series data. In this paper, we propose a new method to reduce the dimensionality of high-dimensional time-series data. The method is simpler than existing ones based on the discrete Fourier transform and the discrete cosine transform. Furthermore, our dimensionality reduction may be executed in parallel. The method is used to time-series data matching problem and it decreases drastically the complexity of the corresponding algorithm. The method preserves planar geometric blocks and it is also applied to minimum bounding rectangles as well.