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
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Variable Length Queries for Time Series Data
Proceedings of the 17th International Conference on Data Engineering
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd 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 Subsequence Matching in Time Series Databases Under Time and Amplitude Transformations
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Optimizing Similarity Search for Arbitrary Length Time Series Queries
IEEE Transactions on Knowledge and Data Engineering
A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases
Data Mining and Knowledge Discovery
Ranked subsequence matching in time-series databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast Normalization-Transformed Subsequence Matching in Time-Series Databases
IEICE - Transactions on Information and Systems
Distortion-free predictive streaming time-series matching
Information Sciences: an International Journal
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
Similar subsequence search in time series databases
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
| Hi-index | 0.00 |
Time-series subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We claim that index interpolation is a fairly effective tool to resolve this problem. Index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their distinct sizes. For index interpolation, we need to decide the sizes of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of pairs 〈length, frequency 〉 of query sequences to be performed in a target application and a set of window sizes for building multiple indexes, we devise a formula that estimates the overall cost of all the subsequence matchings. By using this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of experiments with a real-life stock data set and a large volume of a synthetic data set to show the superiority of our approach.