Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 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
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Threshold Similarity Queries in Large Time Series Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Data Mining and Knowledge Discovery
Time Series Analysis Using the Concept of Adaptable Threshold Similarity
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
T-Time: Threshold-Based Data Mining on Time Series
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Similarity search on time series based on threshold queries
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
| Hi-index | 12.05 |
In this paper, a novel kind of threshold similarity query is introduced. It reports a threshold if exceeding which the queried time series has the most similar time intervals compared to that of the given query time series above its query threshold, and the extent of the similarity between the two time interval sequences should be within a user-specified range. We present an efficient method composed by two steps to solve the query. The first step is to dramatically narrow the search space into a quite small subspace without false dismissals, and the second to search iteratively in the subspace. In more detail, a lower bounding distance function is described, which guarantees no false dismissals during the first step. Furthermore, we use binary search to quickly locate the solution within the subspace based on the continuity and monotone of the length function of time intervals, which are proved in this paper. We implemented our method on traffic data and discovered some useful knowledge. We also carried out experiments on diverse time series data to compare our method with brute force method. The results were excellent: our method accelerated the search time from 10 times up to 150 times.