Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast approximate correlation for massive time-series data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
ACM Computing Surveys (CSUR)
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Similarity search over stream time series has a wide spectrum of applications. Most previous work in static time-series databases and stream time series aim at retrieving the exact answer to a similarity search. However, little work considers the approximate similarity search in stream time series. In this paper, we propose a weighted locality-sensitive hashing (WLSH) technique, which is adaptive to characteristics of stream data, to answer approximate similarity search over stream time series. Due to the unique requirement of stream processing, we present an efficient method to update hash functions adaptive to stream data and maintain hash files incrementally at a low cost. Extensive experiments demonstrate the effectiveness of WLSH, as well as the efficiency of approximate similarity search via hashing on stream time series.