Similarity Search on Time Series Based on Threshold Queries

Similarity search in time series data is required in many application fields. The most prominent work has focused on similarity search considering either complete time series or similarity according to subsequences of time series. For many domains like financial analysis, medicine, environmental met...

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Bibliographic Details
Published inAdvances in Database Technology - EDBT 2006 pp. 276 - 294
Main Authors Aßfalg, Johannes, Kriegel, Hans-Peter, Kröger, Peer, Kunath, Peter, Pryakhin, Alexey, Renz, Matthias
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Computer science; control theory; systems
Dynamic Time Warping
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Query Process
Query Time
Software
Time Series
Time Series Data
Database query
Similarity
Time series
Database
Search time
Information retrieval
Incomplete information
Real time
Medical application
Meteorology
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Summary:Similarity search in time series data is required in many application fields. The most prominent work has focused on similarity search considering either complete time series or similarity according to subsequences of time series. For many domains like financial analysis, medicine, environmental meteorology, or environmental observation, the detection of temporal dependencies between different time series is very important. In contrast to traditional approaches which consider the course of the time series for the purpose of matching, coarse trend information about the time series could be sufficient to solve the above mentioned problem. In particular, temporal dependencies in time series can be detected by determining the points of time at which the time series exceeds a specific threshold. In this paper, we introduce the novel concept of threshold queries in time series databases which report those time series exceeding a user-defined query threshold at similar time frames compared to the query time series. We present a new efficient access method which uses the fact that only partial information of the time series is required at query time. The performance of our solution is demonstrated by an extensive experimental evaluation on real world and artificial time series data.
ISBN:3540329609
9783540329602
ISSN:0302-9743
1611-3349
DOI:10.1007/11687238_19