Verfügbarkeit: Big Data | Technische Universität München

Big Data: 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings

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Bibliographische Detailangaben
Körperschaft:	BigData Wuhan (VerfasserIn)
Weitere beteiligte Personen:	Jin, Hai (HerausgeberIn), Lin, Xuemin (HerausgeberIn), Cheng, Xueqi (HerausgeberIn), Shi, Xuanhua (HerausgeberIn), Xiao, Nong (HerausgeberIn), Huang, Yihua (HerausgeberIn)
Format:	Elektronisch Tagungsbericht E-Book
Sprache:	Englisch
Veröffentlicht:	Singapore Springer [2019]
Schriftenreihe:	Communications in Computer and Information Science 1120
Schlagwörter:	Big Data Information Systems and Communication Service Machine Learning Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer System Implementation Big data Computers Machine learning Computer communication systems Optical data processing Architecture, Computer Konferenzschrift > 2019 > Wuhan
Links:	https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 https://doi.org/10.1007/978-981-15-1899-7 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031661509&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	1 Online-Ressource (xv, 432 Seiten) Illustrationen
ISBN:	9789811518997
ISSN:	1865-0929
DOI:	10.1007/978-981-15-1899-7

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Datensatz im Suchindex

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adam_text	INHALTSVERZEICHNIS DANKSAGUNG.............................................................................................................V INHALTSVERZEICHNIS...............................................................................................VII ABBILDUNGSVERZEICHNIS.........................................................................................XI FORMELVERZEICHNIS...............................................................................................XV T A BELLEN VERZEICHN IS......................................................................................... XVII ABKUERZUNGSVERZEICHNIS....................................................................................XIX 1 EINLEITUNG ........ . ................................................................................................ 1 1.1 MOTIVATION................................................................................................1 1.2 AUFGABENSTELLUNG....................................................................................2 1.3 HYPOTHESE.................................................................................................3 1.4 ARBEITSPAKETE.......................................................................................... 4 1.4.1 ERSTES ARBEITSPAKET .......................... 5 1.4.2 ZWEITES ARBEITSPAKET........................................................................ 6 1.4.3 DRITTES ARBEITSPAKET........................................................................ 8 1.5 GLIEDERUNG UND AUFBAU...................................................................... 10 2 STAND DER FORSCHUNG....................................................................................13 2.1 IMMOBILIENPORTALE................................................................................13 2.2 LITERATUR.... ............................ 14 2.2.1 LITERATUR AUS DEM BEREICH DER RAEUMLICHEN ANALYSE .................... 16 2.2.2 LITERATUR AUS DEM BEREICH DES MASCHINELLEN LERNENS................18 2.2.3 LITERATUR AUS DEM BEREICH DER GEOVISUALISIERUNG ...................... 18 2.3 TECHNOLOGISCHE RECHERCHE..................................................................18 2.4 STATUS QUO DER M ETHODEN...................................................................24 2.4.1 HEDONISCHE MODELLVERFAHREN.......................................................25 2.4.2 RAEUMLICHE BEZUGSEINHEITEN .......................... 27 2.4.3 NICHT DYNAMISCHE LEGENDEN............................... 30 2.4.4 PUNKTSIGNATUREN UND DAS ICON-CLUTTERING-PROBLEM ................ 32 2.4.5 INTEGRATION VON DISTANZ ............ .................................................... 33 2.4.6 INTEGRATION VON RAEUMLICHEN INDIKATOREN......................................34 2.4.7 FAZIT................................................................................................ 34 3 DATENBASIS......................................................................................................35 3.1 QUANTITATIVE DATENBASIS.................. 35 3.2 QUALITATIVE DATENBASIS........................................................................ 37 V III_____________________________________________INHALTSVERZEICHNIS 3.2.1 EXPERTENINTERVIEWS..................................................................... 37 3.2.2 DIE GEOINFORMATISCHE UND GEOVISUELLE PROZESSKETTE VON IMMOBILIENPORTALEN................................................................... 45 3.2.3 RAEUMLICHE OPTIMIERUNGSPOTENTIALE...........................................46 4 METHODEN........................................................................................................49 4.1 METHODENGRUPPE DER RAEUMLICH STATISTISCHEN METHODEN .............. 49 4.1.1 REGIONALISIERTE VARIABLEN.......................................................... 50 4.1.2 RAEUMLICHEN AUTOKORRELATION..................................................... 50 4.1.3 HAUPTKOMPONENTENANALYSE ......................................................54 4.1.4 GEOGRAPHISCH GEWICHTETE REGRESSION.........................................55 4.1.5 RAEUMLICHE SCHAETZVERFAHREN, INTERPOLATION................................56 4.2 METHODENGRUPPE DER VERFAHREN DES MASCHINELLEN LERNENS...... 68 4.2.1 RANDOM FOREST (RF).................................................................. 69 4.2.2 KUENSTLICHE NEURONALE NETZE.....................................................70 4.3 METHODENGRUPPE DER GEOVISUELLEN METHODEN.................................71 4.3.1 GRID-MAPPING............................................................................ 72 4.3.2 MAP APIS .................................................... 73 4.3.3 KARTENBASIS................................................................................ 74 4.3.4 CLOUDBASIERTE GEO VISUALISIERUNG............................................ 74 4.4 FAZIT......................................................................................................... 75 5 ANWENDUNG DER METHODEN..........................................................................77 5.1 ANWENDUNG RAEUMLICH-STATISTISCHER METHODEN................................79 5.1.1 TEILIMPLEMENTIERUNG DER HEDONISCHEN REGRESSION .................. 79 5.1.2 TEILIMPLEMENTIERUNG DER AUSWAHL VON SEKUNDAERVARIABLEN .... 81 5.1.3 TEILIMPLEMENTIERUNG DER MESSUNG DER RAEUMLICHEN AUTOKORRELATION ......................................................................... 89 5.1.4 TEILIMPLEMENTIERUNG DER GEOGRAPHISCH GEWICHTETEN REGRESSION IN KOMBINATION MIT LAGEMODELLIERUNGEN .............. 91 5.1.5 TEILIMPLEMENTIERUNG DER MODELLEVALUIERUNG FUER DIE ANWENDUNG FUER IMMOBILIENPORTALE...........................................98 5.2 ANWENDUNG DER METHODEN DES MASCHINELLEN LERNENS ............. 102 5.2.1 TEILIMPLEMENTIERUNG VON RF ZUR MIETPREISSCHAETZUNG .......... 103 5.2.2 TEILIMPLEMENTIERUNG VON ANN ZUR MIETPREISSCHAETZUNG ...... 107 5.3 T EILIMPLEMENTIERUNG GEOVISUALISIERUNGSMETHODEN .................... 108 5.3.1 VORUEBERLEGUNGEN ZU MAP APIS ....................................... 109 5.3.2 VORUEBERLEGUNG ZU POTENTIELLEN NUTZERGRUPPEN VON MIETPREISKARTEN ....................................................................... 109 5.3.3 ZONENLOSE PREISKARTE ............................................................... 110 5.3.4 PROTOTYPENENTWICKLUNG............................................................ 114 5.3.5 ANALYSE DER GEMEINSAMKEITEN DER MIETPREISKARTEN ................ 115 6 FAZIT UND UMSETZUNGSOPTIONEN ............................................................ 123 6.1 FAZIT.................................................................................................123 6.1.1 RAUMANALYTISCHE TEILKOMPONENTE DES FAZITS........................123 6.1 .2 GEOVISUELLE TEILKOMPONENTE DES FAZITS ................................. 125 6.2 UMSETZUNGSOPTIONEN ..................................................................... 126 6.2.1 SZENARIO MIETPREISSCHAETZUNG 1................................................. 127 6.2.2 SZENARIO MIETPREISSCHAETZUNG II ............................................... 127 6.2.3 SZENARIO MIETPREISKARTE .......................................................... 128 7 ZUSAMMENFASSUNG UND AUSBLICK......................................................... 131 7.1 ZUSAMMENFASSUNG......................................................................... 131 7.2 BEITRAG ZUM FORSCHUNGSSTAND UND AUSBLICK .............................. 133 7.2.1 AUSBLICK................................................................................... 133 LITERATURVERZEICHNIS ..................................................................................... 135 ANHANG............................................................................................................145 ANHANG 1: LEITFADEN EXPERTENBEFRAGUNGEN ............................................... 145 ANHANG 2: AUSWERTEMATRIX TECHNOLOGISCHE RECHERCHE ............................. 148 ANHANG 3: VARIABLEN DES ANGEBOTSDATENSAETZE ........................................... 150 ANHANG 4: ANGEBOTSDICHTEN UND DURSCHNITTSNETTOKALTMIETEN PRO JAHR IN POTSDAM................................................................................151 ANHANG 5: AUTOKORRELATION DER ANGEBOTSDATEN - POTSDAM ERSTES QUARTAL 2013............................................................................ 151 ANHANG 6: RANDOM FOREST SCORE FUER SECHS KUENSTLICHE ANGEBOTSWERTE AUS DEM ERSTEN QUARTAL 2013 ................................................... 151 ANHANG 7: FEHLERREPORT RF MODELL MIT 150 BAEUMEN................................152 ANHANG 8; ERGEBNISSE DER PCA: Q L/2013 ................................................. 153 ANHANG 9: MODELLIERTE EINFLUSSFAKTOREN AUF DIE NETTOKALTMIETE (POTSDAM 22,2012)............... 153
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spellingShingle	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings Communications in Computer and Information Science Big Data Information Systems and Communication Service Machine Learning Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer System Implementation Big data Computers Machine learning Computer communication systems Optical data processing Architecture, Computer
subject_GND	(DE-588)1071861417
title	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings
title_auth	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings
title_exact_search	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings
title_full	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings Hai Jin, Xuemin Lin, Xueqi Cheng, Xuanhua Shi, Nong Xiao, Yihua Huang (eds.)
title_fullStr	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings Hai Jin, Xuemin Lin, Xueqi Cheng, Xuanhua Shi, Nong Xiao, Yihua Huang (eds.)
title_full_unstemmed	Big Data 7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings Hai Jin, Xuemin Lin, Xueqi Cheng, Xuanhua Shi, Nong Xiao, Yihua Huang (eds.)
title_short	Big Data
title_sort	big data 7th ccf conference bigdata 2019 wuhan china september 26 28 2019 proceedings
title_sub	7th CCF Conference, BigData 2019, Wuhan, China, September 26–28, 2019 : proceedings
topic	Big Data Information Systems and Communication Service Machine Learning Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer System Implementation Big data Computers Machine learning Computer communication systems Optical data processing Architecture, Computer
topic_facet	Big Data Information Systems and Communication Service Machine Learning Computer Communication Networks Computer Imaging, Vision, Pattern Recognition and Graphics Computer System Implementation Big data Computers Machine learning Computer communication systems Optical data processing Architecture, Computer Konferenzschrift 2019 Wuhan
url	https://doi.org/10.1007/978-981-15-1899-7 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031661509&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV041227480
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