A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data
Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not app...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Geng, Yang [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Übergeordnetes Werk: |
Enthalten in: Biosensors: A novel approach to and recent discovery in detection of cytokines - Mobed, Ahmad ELSEVIER, 2020, an international research journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:139 ; year:2022 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.autcon.2022.104303 |
|---|
| Katalog-ID: |
ELV057697396 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV057697396 | ||
| 003 | DE-627 | ||
| 005 | 20230626045601.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 220808s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.autcon.2022.104303 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica |
| 035 | |a (DE-627)ELV057697396 | ||
| 035 | |a (ELSEVIER)S0926-5805(22)00176-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 570 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 44.00 |2 bkl | ||
| 100 | 1 | |a Geng, Yang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| 264 | 1 | |c 2022transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. | ||
| 520 | |a Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. | ||
| 700 | 1 | |a Ji, Wenjie |4 oth | |
| 700 | 1 | |a Xie, Yongxin |4 oth | |
| 700 | 1 | |a Lin, Borong |4 oth | |
| 700 | 1 | |a Zhuang, Weimin |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science Publ |a Mobed, Ahmad ELSEVIER |t Biosensors: A novel approach to and recent discovery in detection of cytokines |d 2020 |d an international research journal |g Amsterdam [u.a.] |w (DE-627)ELV004774973 |
| 773 | 1 | 8 | |g volume:139 |g year:2022 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.autcon.2022.104303 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 44.00 |j Medizin: Allgemeines |q VZ |
| 951 | |a AR | ||
| 952 | |d 139 |j 2022 |h 0 | ||
| author_variant |
y g yg |
|---|---|
| matchkey_str |
gengyangjiwenjiexieyongxinlinborongzhuan:2022----:sbeunelseigehdoietfigalidoevrnetlatr |
| hierarchy_sort_str |
2022transfer abstract |
| bklnumber |
44.00 |
| publishDate |
2022 |
| allfields |
10.1016/j.autcon.2022.104303 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057697396 (ELSEVIER)S0926-5805(22)00176-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Geng, Yang verfasserin aut A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Ji, Wenjie oth Xie, Yongxin oth Lin, Borong oth Zhuang, Weimin oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:139 year:2022 pages:0 https://doi.org/10.1016/j.autcon.2022.104303 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 139 2022 0 |
| spelling |
10.1016/j.autcon.2022.104303 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057697396 (ELSEVIER)S0926-5805(22)00176-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Geng, Yang verfasserin aut A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Ji, Wenjie oth Xie, Yongxin oth Lin, Borong oth Zhuang, Weimin oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:139 year:2022 pages:0 https://doi.org/10.1016/j.autcon.2022.104303 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 139 2022 0 |
| allfields_unstemmed |
10.1016/j.autcon.2022.104303 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057697396 (ELSEVIER)S0926-5805(22)00176-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Geng, Yang verfasserin aut A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Ji, Wenjie oth Xie, Yongxin oth Lin, Borong oth Zhuang, Weimin oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:139 year:2022 pages:0 https://doi.org/10.1016/j.autcon.2022.104303 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 139 2022 0 |
| allfieldsGer |
10.1016/j.autcon.2022.104303 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057697396 (ELSEVIER)S0926-5805(22)00176-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Geng, Yang verfasserin aut A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Ji, Wenjie oth Xie, Yongxin oth Lin, Borong oth Zhuang, Weimin oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:139 year:2022 pages:0 https://doi.org/10.1016/j.autcon.2022.104303 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 139 2022 0 |
| allfieldsSound |
10.1016/j.autcon.2022.104303 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica (DE-627)ELV057697396 (ELSEVIER)S0926-5805(22)00176-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 44.00 bkl Geng, Yang verfasserin aut A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. Ji, Wenjie oth Xie, Yongxin oth Lin, Borong oth Zhuang, Weimin oth Enthalten in Elsevier Science Publ Mobed, Ahmad ELSEVIER Biosensors: A novel approach to and recent discovery in detection of cytokines 2020 an international research journal Amsterdam [u.a.] (DE-627)ELV004774973 volume:139 year:2022 pages:0 https://doi.org/10.1016/j.autcon.2022.104303 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 139 2022 0 |
| language |
English |
| source |
Enthalten in Biosensors: A novel approach to and recent discovery in detection of cytokines Amsterdam [u.a.] volume:139 year:2022 pages:0 |
| sourceStr |
Enthalten in Biosensors: A novel approach to and recent discovery in detection of cytokines Amsterdam [u.a.] volume:139 year:2022 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Medizin: Allgemeines |
| institution |
findex.gbv.de |
| dewey-raw |
570 |
| isfreeaccess_bool |
false |
| container_title |
Biosensors: A novel approach to and recent discovery in detection of cytokines |
| authorswithroles_txt_mv |
Geng, Yang @@aut@@ Ji, Wenjie @@oth@@ Xie, Yongxin @@oth@@ Lin, Borong @@oth@@ Zhuang, Weimin @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV004774973 |
| dewey-sort |
3570 |
| id |
ELV057697396 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV057697396</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626045601.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.autcon.2022.104303</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV057697396</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0926-5805(22)00176-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Geng, Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ji, Wenjie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Yongxin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Borong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhuang, Weimin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Publ</subfield><subfield code="a">Mobed, Ahmad ELSEVIER</subfield><subfield code="t">Biosensors: A novel approach to and recent discovery in detection of cytokines</subfield><subfield code="d">2020</subfield><subfield code="d">an international research journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV004774973</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:139</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.autcon.2022.104303</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">139</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Geng, Yang |
| spellingShingle |
Geng, Yang ddc 570 fid BIODIV bkl 44.00 A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| authorStr |
Geng, Yang |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV004774973 |
| format |
electronic Article |
| dewey-ones |
570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
570 VZ BIODIV DE-30 fid 44.00 bkl A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| topic |
ddc 570 fid BIODIV bkl 44.00 |
| topic_unstemmed |
ddc 570 fid BIODIV bkl 44.00 |
| topic_browse |
ddc 570 fid BIODIV bkl 44.00 |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
w j wj y x yx b l bl w z wz |
| hierarchy_parent_title |
Biosensors: A novel approach to and recent discovery in detection of cytokines |
| hierarchy_parent_id |
ELV004774973 |
| dewey-tens |
570 - Life sciences; biology |
| hierarchy_top_title |
Biosensors: A novel approach to and recent discovery in detection of cytokines |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV004774973 |
| title |
A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| ctrlnum |
(DE-627)ELV057697396 (ELSEVIER)S0926-5805(22)00176-5 |
| title_full |
A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| author_sort |
Geng, Yang |
| journal |
Biosensors: A novel approach to and recent discovery in detection of cytokines |
| journalStr |
Biosensors: A novel approach to and recent discovery in detection of cytokines |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Geng, Yang |
| container_volume |
139 |
| class |
570 VZ BIODIV DE-30 fid 44.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Geng, Yang |
| doi_str_mv |
10.1016/j.autcon.2022.104303 |
| dewey-full |
570 |
| title_sort |
a sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| title_auth |
A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| abstract |
Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. |
| abstractGer |
Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. |
| abstract_unstemmed |
Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
| title_short |
A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data |
| url |
https://doi.org/10.1016/j.autcon.2022.104303 |
| remote_bool |
true |
| author2 |
Ji, Wenjie Xie, Yongxin Lin, Borong Zhuang, Weimin |
| author2Str |
Ji, Wenjie Xie, Yongxin Lin, Borong Zhuang, Weimin |
| ppnlink |
ELV004774973 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth |
| doi_str |
10.1016/j.autcon.2022.104303 |
| up_date |
2024-07-06T16:54:00.937Z |
| _version_ |
1803849397373501440 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV057697396</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626045601.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220808s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.autcon.2022.104303</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001929.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV057697396</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0926-5805(22)00176-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Geng, Yang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Indoor Environmental Quality (IEQ) affects human comfort, productivity and health. The rich values behind massive IEQ data are urgently required for the improvement of building performance. Clustering is one common approach for data mining. However, current time-series clustering methods are not applicable to IEQ data, owing to its high dimension and complex pattern. This study proposes a sub-sequence clustering framework for the extraction of daily IEQ patterns. Two case studies were conducted: 1) three main daily patterns of air temperature were extracted from 734 curves in an office building, and 2) six typical daily patterns of CO2 concentration were identified from 1884 curves in a university building. Post-clustering analyses, including a classification tree, were also performed for knowledge discovery. Finally, the clustering performance of the proposed method was compared with that of previous methods. The results indicate that the sub-sequence clustering method is appropriate for identifying daily IEQ patterns.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ji, Wenjie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Yongxin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lin, Borong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhuang, Weimin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Publ</subfield><subfield code="a">Mobed, Ahmad ELSEVIER</subfield><subfield code="t">Biosensors: A novel approach to and recent discovery in detection of cytokines</subfield><subfield code="d">2020</subfield><subfield code="d">an international research journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV004774973</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:139</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.autcon.2022.104303</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.00</subfield><subfield code="j">Medizin: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">139</subfield><subfield code="j">2022</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.40158 |