Effective and efficient data mining in time series databases is essential in many application domains as for instance in financial analysis, medicine, meteorology, and environmental observation. In particular, temporal dependencies between time series are of capital importance for these applications. In this paper, we present TQuEST, a powerful query processor for time series databases. TQuEST supports a novel but very useful class of queries which we call threshold queries. Threshold queries enable searches for time series whose values are above a user defined threshold at certain time intervals. Example queries are "report all ozone curves which are above their daily mean value at the same time as a given temperature curve exceeds 28°C" or "report all blood value curves from patients whose values exceed a certain threshold one hour after the new medication was taken". TQuEST is based on a novel representation of time series which allows the query processor to access only the relevant parts of the time series. This enables an efficient execution of threshold queries. In particular, queries can be readjusted with interactive response times. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Aßfalg, J., Kriegel, H. P., Kröger, P., Kunath, P., Pryakhin, A., & Renz, M. (2006). TQuEST: Threshold query execution for large sets of time series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3896 LNCS, pp. 1147–1150). Springer Verlag. https://doi.org/10.1007/11687238_79
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