STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery
Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is con...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shi, Lei [verfasserIn] Gangopadhyay, Aryya [verfasserIn] Janeja, Vandana P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Knowledge and information systems - London : Springer, 1999, 43(2014), 2 vom: 27. Feb., Seite 333-353 |
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| Übergeordnetes Werk: |
volume:43 ; year:2014 ; number:2 ; day:27 ; month:02 ; pages:333-353 |
| Links: |
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| DOI / URN: |
10.1007/s10115-014-0733-3 |
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| Katalog-ID: |
SPR009151311 |
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| 520 | |a Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. | ||
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| 650 | 4 | |a Spatio-temporal |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tensor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a High-order |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Gangopadhyay, Aryya |e verfasserin |4 aut | |
| 700 | 1 | |a Janeja, Vandana P. |e verfasserin |4 aut | |
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10.1007/s10115-014-0733-3 doi (DE-627)SPR009151311 (SPR)s10115-014-0733-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Shi, Lei verfasserin aut STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. Anomaly detection (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Tensor (dpeaa)DE-He213 High-order (dpeaa)DE-He213 Gangopadhyay, Aryya verfasserin aut Janeja, Vandana P. verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 43(2014), 2 vom: 27. Feb., Seite 333-353 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:43 year:2014 number:2 day:27 month:02 pages:333-353 https://dx.doi.org/10.1007/s10115-014-0733-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI 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_101 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_267 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_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 06.74 ASE 54.64 ASE AR 43 2014 2 27 02 333-353 |
| spelling |
10.1007/s10115-014-0733-3 doi (DE-627)SPR009151311 (SPR)s10115-014-0733-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Shi, Lei verfasserin aut STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. Anomaly detection (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Tensor (dpeaa)DE-He213 High-order (dpeaa)DE-He213 Gangopadhyay, Aryya verfasserin aut Janeja, Vandana P. verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 43(2014), 2 vom: 27. Feb., Seite 333-353 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:43 year:2014 number:2 day:27 month:02 pages:333-353 https://dx.doi.org/10.1007/s10115-014-0733-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI 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_101 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_267 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_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 06.74 ASE 54.64 ASE AR 43 2014 2 27 02 333-353 |
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10.1007/s10115-014-0733-3 doi (DE-627)SPR009151311 (SPR)s10115-014-0733-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Shi, Lei verfasserin aut STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. Anomaly detection (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Tensor (dpeaa)DE-He213 High-order (dpeaa)DE-He213 Gangopadhyay, Aryya verfasserin aut Janeja, Vandana P. verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 43(2014), 2 vom: 27. Feb., Seite 333-353 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:43 year:2014 number:2 day:27 month:02 pages:333-353 https://dx.doi.org/10.1007/s10115-014-0733-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI 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_101 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_267 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_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 06.74 ASE 54.64 ASE AR 43 2014 2 27 02 333-353 |
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10.1007/s10115-014-0733-3 doi (DE-627)SPR009151311 (SPR)s10115-014-0733-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Shi, Lei verfasserin aut STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. Anomaly detection (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Tensor (dpeaa)DE-He213 High-order (dpeaa)DE-He213 Gangopadhyay, Aryya verfasserin aut Janeja, Vandana P. verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 43(2014), 2 vom: 27. Feb., Seite 333-353 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:43 year:2014 number:2 day:27 month:02 pages:333-353 https://dx.doi.org/10.1007/s10115-014-0733-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI 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_101 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_267 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_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 06.74 ASE 54.64 ASE AR 43 2014 2 27 02 333-353 |
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10.1007/s10115-014-0733-3 doi (DE-627)SPR009151311 (SPR)s10115-014-0733-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Shi, Lei verfasserin aut STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. Anomaly detection (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Tensor (dpeaa)DE-He213 High-order (dpeaa)DE-He213 Gangopadhyay, Aryya verfasserin aut Janeja, Vandana P. verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 43(2014), 2 vom: 27. Feb., Seite 333-353 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:43 year:2014 number:2 day:27 month:02 pages:333-353 https://dx.doi.org/10.1007/s10115-014-0733-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI 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_101 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_267 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_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 06.74 ASE 54.64 ASE AR 43 2014 2 27 02 333-353 |
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As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. 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stensr: spatio-temporal tensor streams for anomaly detection and pattern discovery |
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STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery |
| abstract |
Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. |
| abstractGer |
Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. |
| abstract_unstemmed |
Abstract The focus of this paper is anomaly detection and pattern discovery in spatio-temporal tensor streams. As an example, sensor networks comprising of multiple individual sensor streams generate spatio-temporal data, which can be captured in tensor streams. Anomaly detection in such data is considered challenging because of the potential complexity and high order of the tensor data from spatio-temporal sources such as sensor networks. In this paper, we propose an innovative approach for anomaly detection and pattern discovery in such tensor streams. We model the tensor stream itself as a single incremental tensor, for example representing the entire sensor network, instead of dealing with each individual tensor in the stream separately. Such a model provides a global view of the tensor stream and enables subsequent in-depth analysis of it. The proposed approach is designed for online analysis of tensor streams with fast runtime. We evaluate our approach for detecting anomalies under different conditions and for identifying complex data patterns. We also compare the proposed approach with the existing tensor stream analysis method (Sun et al. in ACM Trans Knowl Discov Data 2, 2008). Our evaluation uses synthetic data as well as real-world data showing the efficiency and effectiveness of the proposed approach. |
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STenSr: Spatio-temporal tensor streams for anomaly detection and pattern discovery |
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https://dx.doi.org/10.1007/s10115-014-0733-3 |
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Gangopadhyay, Aryya Janeja, Vandana P. |
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10.1007/s10115-014-0733-3 |
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2024-07-04T00:52:16.890Z |
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| score |
7.401045 |