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
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 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
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
Interval Sequences: An Object-Relational Approach to Manage Spatial Data
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Similarity search in time series databases usually deals with comparing entire time series objects or subsequence search. In this paper, we formalize the notion of interval-focused similarity queries which take a set of intervals specifying relevant time frames as additional parameter and compare the time series objects only within this user-defined time focus. We propose an original method to efficiently support interval-focused distance range and k-nearest neighbor queries implementing a filter/refinement architecture. In our broad experimental evaluation we show the superiority of our novel approach compared to existing approaches on several real-world data sets.