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000182527 005__ 20190316235531.0
000182527 020__ $$a978-1-4673-4909-3
000182527 0247_ $$2doi$$a10.1109/ICDE.2013.6544879
000182527 02470 $$2ISI$$a000326733500073
000182527 037__ $$aCONF
000182527 245__ $$aA FFINITY: Efficiently Querying Statistical Measures on Time-Series Data
000182527 269__ $$a2013
000182527 260__ $$bIEEE$$c2013$$aNew York
000182527 336__ $$aConference Papers
000182527 520__ $$aComputing statistical measures for large databases of time series is a fundamental primitive for querying and mining time-series data [1]–[6]. This primitive is gaining importance with the increasing number and rapid growth of time series databases. In this paper, we introduce a framework for efficient computation of statistical measures by exploiting the concept of affine relationships. Affine relationships can be used to infer statistical measures for time series, from other related time series, instead of computing them directly; thus, reducing the overall computational cost significantly. The resulting methods exhibit at least one order of magnitude improvement over the best known methods. To the best of our knowledge, this is the first work that presents an unified approach for computing and querying several statistical measures at once. Our approach exploits affine relationships using three key components. First, the AFCLST algorithm clusters the time-series data, such that high-quality affine relationships could be easily found. Second, the SYMEX algorithm uses the clustered time series and efficiently computes the desired affine relationships. Third, the SCAPE index structure produces a many-fold im- provement in the performance of processing several statistical queries by seamlessly indexing the affine relationships. Finally, we establish the effectiveness of our approaches by performing comprehensive experimental evaluation on real datasets.
000182527 700__ $$0242022$$g177954$$aSathe, Saket
000182527 700__ $$aAberer, Karl$$g134136$$0240941
000182527 7112_ $$dApril 8-12, 2013$$cBrisbane, Australia$$a29th International Conference on Data Engineering (ICDE)
000182527 773__ $$t2013 IEEE 29Tth International Conference On Data Engineering (ICDE)$$q841-852
000182527 8564_ $$uhttps://infoscience.epfl.ch/record/182527/files/icde13-affinity.pdf$$zPublisher's version$$s367975$$yPublisher's version
000182527 909C0 $$xU10405$$0252004$$pLSIR
000182527 909CO $$ooai:infoscience.tind.io:182527$$qGLOBAL_SET$$pconf$$pIC
000182527 917Z8 $$x177954
000182527 917Z8 $$x148230
000182527 937__ $$aEPFL-CONF-182527
000182527 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000182527 980__ $$aCONF