ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows
Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing thi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yagoubi, Djamel Edine [verfasserIn] Akbarinia, Reza [verfasserIn] Kolev, Boyan [verfasserIn] Levchenko, Oleksandra [verfasserIn] Masseglia, Florent [verfasserIn] Valduriez, Patrick [verfasserIn] Shasha, Dennis [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Data mining and knowledge discovery - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997, 32(2018), 5 vom: 07. Aug., Seite 1481-1507 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2018 ; number:5 ; day:07 ; month:08 ; pages:1481-1507 |
| Links: |
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| DOI / URN: |
10.1007/s10618-018-0580-z |
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| Katalog-ID: |
SPR011814233 |
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| 520 | |a Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). | ||
| 650 | 4 | |a Time series analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Data stream processing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Distributed computing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Data mining |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Akbarinia, Reza |e verfasserin |4 aut | |
| 700 | 1 | |a Kolev, Boyan |e verfasserin |4 aut | |
| 700 | 1 | |a Levchenko, Oleksandra |e verfasserin |4 aut | |
| 700 | 1 | |a Masseglia, Florent |e verfasserin |4 aut | |
| 700 | 1 | |a Valduriez, Patrick |e verfasserin |4 aut | |
| 700 | 1 | |a Shasha, Dennis |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Data mining and knowledge discovery |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 |g 32(2018), 5 vom: 07. Aug., Seite 1481-1507 |w (DE-627)271349999 |w (DE-600)1479890-6 |x 1573-756X |7 nnns |
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10.1007/s10618-018-0580-z doi (DE-627)SPR011814233 (SPR)s10618-018-0580-z-e DE-627 ger DE-627 rakwb eng 400 ASE 54.64 bkl 06.74 bkl 54.72 bkl 17.00 bkl Yagoubi, Djamel Edine verfasserin aut ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). Time series analysis (dpeaa)DE-He213 Data stream processing (dpeaa)DE-He213 Distributed computing (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Akbarinia, Reza verfasserin aut Kolev, Boyan verfasserin aut Levchenko, Oleksandra verfasserin aut Masseglia, Florent verfasserin aut Valduriez, Patrick verfasserin aut Shasha, Dennis verfasserin aut Enthalten in Data mining and knowledge discovery Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 32(2018), 5 vom: 07. Aug., Seite 1481-1507 (DE-627)271349999 (DE-600)1479890-6 1573-756X nnns volume:32 year:2018 number:5 day:07 month:08 pages:1481-1507 https://dx.doi.org/10.1007/s10618-018-0580-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 ASE 06.74 ASE 54.72 ASE 17.00 ASE AR 32 2018 5 07 08 1481-1507 |
| spelling |
10.1007/s10618-018-0580-z doi (DE-627)SPR011814233 (SPR)s10618-018-0580-z-e DE-627 ger DE-627 rakwb eng 400 ASE 54.64 bkl 06.74 bkl 54.72 bkl 17.00 bkl Yagoubi, Djamel Edine verfasserin aut ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). Time series analysis (dpeaa)DE-He213 Data stream processing (dpeaa)DE-He213 Distributed computing (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Akbarinia, Reza verfasserin aut Kolev, Boyan verfasserin aut Levchenko, Oleksandra verfasserin aut Masseglia, Florent verfasserin aut Valduriez, Patrick verfasserin aut Shasha, Dennis verfasserin aut Enthalten in Data mining and knowledge discovery Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 32(2018), 5 vom: 07. Aug., Seite 1481-1507 (DE-627)271349999 (DE-600)1479890-6 1573-756X nnns volume:32 year:2018 number:5 day:07 month:08 pages:1481-1507 https://dx.doi.org/10.1007/s10618-018-0580-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 ASE 06.74 ASE 54.72 ASE 17.00 ASE AR 32 2018 5 07 08 1481-1507 |
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10.1007/s10618-018-0580-z doi (DE-627)SPR011814233 (SPR)s10618-018-0580-z-e DE-627 ger DE-627 rakwb eng 400 ASE 54.64 bkl 06.74 bkl 54.72 bkl 17.00 bkl Yagoubi, Djamel Edine verfasserin aut ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). Time series analysis (dpeaa)DE-He213 Data stream processing (dpeaa)DE-He213 Distributed computing (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Akbarinia, Reza verfasserin aut Kolev, Boyan verfasserin aut Levchenko, Oleksandra verfasserin aut Masseglia, Florent verfasserin aut Valduriez, Patrick verfasserin aut Shasha, Dennis verfasserin aut Enthalten in Data mining and knowledge discovery Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 32(2018), 5 vom: 07. Aug., Seite 1481-1507 (DE-627)271349999 (DE-600)1479890-6 1573-756X nnns volume:32 year:2018 number:5 day:07 month:08 pages:1481-1507 https://dx.doi.org/10.1007/s10618-018-0580-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 ASE 06.74 ASE 54.72 ASE 17.00 ASE AR 32 2018 5 07 08 1481-1507 |
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10.1007/s10618-018-0580-z doi (DE-627)SPR011814233 (SPR)s10618-018-0580-z-e DE-627 ger DE-627 rakwb eng 400 ASE 54.64 bkl 06.74 bkl 54.72 bkl 17.00 bkl Yagoubi, Djamel Edine verfasserin aut ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). Time series analysis (dpeaa)DE-He213 Data stream processing (dpeaa)DE-He213 Distributed computing (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Akbarinia, Reza verfasserin aut Kolev, Boyan verfasserin aut Levchenko, Oleksandra verfasserin aut Masseglia, Florent verfasserin aut Valduriez, Patrick verfasserin aut Shasha, Dennis verfasserin aut Enthalten in Data mining and knowledge discovery Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 32(2018), 5 vom: 07. Aug., Seite 1481-1507 (DE-627)271349999 (DE-600)1479890-6 1573-756X nnns volume:32 year:2018 number:5 day:07 month:08 pages:1481-1507 https://dx.doi.org/10.1007/s10618-018-0580-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 ASE 06.74 ASE 54.72 ASE 17.00 ASE AR 32 2018 5 07 08 1481-1507 |
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10.1007/s10618-018-0580-z doi (DE-627)SPR011814233 (SPR)s10618-018-0580-z-e DE-627 ger DE-627 rakwb eng 400 ASE 54.64 bkl 06.74 bkl 54.72 bkl 17.00 bkl Yagoubi, Djamel Edine verfasserin aut ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). Time series analysis (dpeaa)DE-He213 Data stream processing (dpeaa)DE-He213 Distributed computing (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Akbarinia, Reza verfasserin aut Kolev, Boyan verfasserin aut Levchenko, Oleksandra verfasserin aut Masseglia, Florent verfasserin aut Valduriez, Patrick verfasserin aut Shasha, Dennis verfasserin aut Enthalten in Data mining and knowledge discovery Dordrecht [u.a.] : Springer Science + Business Media B.V, 1997 32(2018), 5 vom: 07. Aug., Seite 1481-1507 (DE-627)271349999 (DE-600)1479890-6 1573-756X nnns volume:32 year:2018 number:5 day:07 month:08 pages:1481-1507 https://dx.doi.org/10.1007/s10618-018-0580-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ANG SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.64 ASE 06.74 ASE 54.72 ASE 17.00 ASE AR 32 2018 5 07 08 1481-1507 |
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Yagoubi, Djamel Edine @@aut@@ Akbarinia, Reza @@aut@@ Kolev, Boyan @@aut@@ Levchenko, Oleksandra @@aut@@ Masseglia, Florent @@aut@@ Valduriez, Patrick @@aut@@ Shasha, Dennis @@aut@@ |
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Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. 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Yagoubi, Djamel Edine |
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Yagoubi, Djamel Edine ddc 400 bkl 54.64 bkl 06.74 bkl 54.72 bkl 17.00 misc Time series analysis misc Data stream processing misc Distributed computing misc Data mining ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows |
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Yagoubi, Djamel Edine Akbarinia, Reza Kolev, Boyan Levchenko, Oleksandra Masseglia, Florent Valduriez, Patrick Shasha, Dennis |
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parcorr: efficient parallel methods to identify similar time series pairs across sliding windows |
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ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows |
| abstract |
Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). |
| abstractGer |
Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). |
| abstract_unstemmed |
Abstract Consider the problem of finding the highly correlated pairs of time series over a time window and then sliding that window to find the highly correlated pairs over successive co-temporous windows such that each successive window starts only a little time after the previous window. Doing this efficiently and in parallel could help in applications such as sensor fusion, financial trading, or communications network monitoring, to name a few. We have developed a parallel incremental random vector/sketching approach to this problem and compared it with the state-of-the-art nearest neighbor method iSAX. Whereas iSAX achieves 100% recall and precision for Euclidean distance, the sketching approach is, empirically, at least 10 times faster and achieves 95% recall and 100% precision on real and simulated data. For many applications this speedup is worth the minor reduction in recall. Our method scales up to 100 million time series and scales linearly in its expensive steps (but quadratic in the less expensive ones). |
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ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows |
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| score |
7.401165 |