(So) Big Data and the transformation of the city
Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Conse...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Andrienko, Gennady [verfasserIn] Andrienko, Natalia [verfasserIn] Boldrini, Chiara [verfasserIn] Caldarelli, Guido [verfasserIn] Cintia, Paolo [verfasserIn] Cresci, Stefano [verfasserIn] Facchini, Angelo [verfasserIn] Giannotti, Fosca [verfasserIn] Gionis, Aristides [verfasserIn] Guidotti, Riccardo [verfasserIn] Mathioudakis, Michael [verfasserIn] Muntean, Cristina Ioana [verfasserIn] Pappalardo, Luca [verfasserIn] Pedreschi, Dino [verfasserIn] Pournaras, Evangelos [verfasserIn] Pratesi, Francesca [verfasserIn] Tesconi, Maurizio [verfasserIn] Trasarti, Roberto [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of data science and analytics - Cham, Switzerland : Springer International Publishing, 2016, 11(2020), 4 vom: 31. März, Seite 311-340 |
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| Übergeordnetes Werk: |
volume:11 ; year:2020 ; number:4 ; day:31 ; month:03 ; pages:311-340 |
| Links: |
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| DOI / URN: |
10.1007/s41060-020-00207-3 |
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| Katalog-ID: |
SPR043933351 |
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| 520 | |a Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. | ||
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| 650 | 4 | |a Urban data science |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Mobility datasets |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Andrienko, Natalia |e verfasserin |4 aut | |
| 700 | 1 | |a Boldrini, Chiara |e verfasserin |4 aut | |
| 700 | 1 | |a Caldarelli, Guido |e verfasserin |4 aut | |
| 700 | 1 | |a Cintia, Paolo |e verfasserin |4 aut | |
| 700 | 1 | |a Cresci, Stefano |e verfasserin |4 aut | |
| 700 | 1 | |a Facchini, Angelo |e verfasserin |4 aut | |
| 700 | 1 | |a Giannotti, Fosca |e verfasserin |4 aut | |
| 700 | 1 | |a Gionis, Aristides |e verfasserin |4 aut | |
| 700 | 1 | |a Guidotti, Riccardo |e verfasserin |4 aut | |
| 700 | 1 | |a Mathioudakis, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Muntean, Cristina Ioana |e verfasserin |4 aut | |
| 700 | 1 | |a Pappalardo, Luca |e verfasserin |4 aut | |
| 700 | 1 | |a Pedreschi, Dino |e verfasserin |4 aut | |
| 700 | 1 | |a Pournaras, Evangelos |e verfasserin |4 aut | |
| 700 | 1 | |a Pratesi, Francesca |e verfasserin |4 aut | |
| 700 | 1 | |a Tesconi, Maurizio |e verfasserin |4 aut | |
| 700 | 1 | |a Trasarti, Roberto |e verfasserin |4 aut | |
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10.1007/s41060-020-00207-3 doi (DE-627)SPR043933351 (DE-599)SPRs41060-020-00207-3-e (SPR)s41060-020-00207-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Andrienko, Gennady verfasserin aut (So) Big Data and the transformation of the city 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. Big data (dpeaa)DE-He213 Urban data science (dpeaa)DE-He213 SoBigData (dpeaa)DE-He213 Mobility datasets (dpeaa)DE-He213 Andrienko, Natalia verfasserin aut Boldrini, Chiara verfasserin aut Caldarelli, Guido verfasserin aut Cintia, Paolo verfasserin aut Cresci, Stefano verfasserin aut Facchini, Angelo verfasserin aut Giannotti, Fosca verfasserin aut Gionis, Aristides verfasserin aut Guidotti, Riccardo verfasserin aut Mathioudakis, Michael verfasserin aut Muntean, Cristina Ioana verfasserin aut Pappalardo, Luca verfasserin aut Pedreschi, Dino verfasserin aut Pournaras, Evangelos verfasserin aut Pratesi, Francesca verfasserin aut Tesconi, Maurizio verfasserin aut Trasarti, Roberto verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 4 vom: 31. März, Seite 311-340 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:4 day:31 month:03 pages:311-340 https://dx.doi.org/10.1007/s41060-020-00207-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 4 31 03 311-340 |
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10.1007/s41060-020-00207-3 doi (DE-627)SPR043933351 (DE-599)SPRs41060-020-00207-3-e (SPR)s41060-020-00207-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Andrienko, Gennady verfasserin aut (So) Big Data and the transformation of the city 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. Big data (dpeaa)DE-He213 Urban data science (dpeaa)DE-He213 SoBigData (dpeaa)DE-He213 Mobility datasets (dpeaa)DE-He213 Andrienko, Natalia verfasserin aut Boldrini, Chiara verfasserin aut Caldarelli, Guido verfasserin aut Cintia, Paolo verfasserin aut Cresci, Stefano verfasserin aut Facchini, Angelo verfasserin aut Giannotti, Fosca verfasserin aut Gionis, Aristides verfasserin aut Guidotti, Riccardo verfasserin aut Mathioudakis, Michael verfasserin aut Muntean, Cristina Ioana verfasserin aut Pappalardo, Luca verfasserin aut Pedreschi, Dino verfasserin aut Pournaras, Evangelos verfasserin aut Pratesi, Francesca verfasserin aut Tesconi, Maurizio verfasserin aut Trasarti, Roberto verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 4 vom: 31. März, Seite 311-340 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:4 day:31 month:03 pages:311-340 https://dx.doi.org/10.1007/s41060-020-00207-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 4 31 03 311-340 |
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10.1007/s41060-020-00207-3 doi (DE-627)SPR043933351 (DE-599)SPRs41060-020-00207-3-e (SPR)s41060-020-00207-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Andrienko, Gennady verfasserin aut (So) Big Data and the transformation of the city 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. Big data (dpeaa)DE-He213 Urban data science (dpeaa)DE-He213 SoBigData (dpeaa)DE-He213 Mobility datasets (dpeaa)DE-He213 Andrienko, Natalia verfasserin aut Boldrini, Chiara verfasserin aut Caldarelli, Guido verfasserin aut Cintia, Paolo verfasserin aut Cresci, Stefano verfasserin aut Facchini, Angelo verfasserin aut Giannotti, Fosca verfasserin aut Gionis, Aristides verfasserin aut Guidotti, Riccardo verfasserin aut Mathioudakis, Michael verfasserin aut Muntean, Cristina Ioana verfasserin aut Pappalardo, Luca verfasserin aut Pedreschi, Dino verfasserin aut Pournaras, Evangelos verfasserin aut Pratesi, Francesca verfasserin aut Tesconi, Maurizio verfasserin aut Trasarti, Roberto verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 4 vom: 31. März, Seite 311-340 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:4 day:31 month:03 pages:311-340 https://dx.doi.org/10.1007/s41060-020-00207-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 4 31 03 311-340 |
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10.1007/s41060-020-00207-3 doi (DE-627)SPR043933351 (DE-599)SPRs41060-020-00207-3-e (SPR)s41060-020-00207-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Andrienko, Gennady verfasserin aut (So) Big Data and the transformation of the city 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. Big data (dpeaa)DE-He213 Urban data science (dpeaa)DE-He213 SoBigData (dpeaa)DE-He213 Mobility datasets (dpeaa)DE-He213 Andrienko, Natalia verfasserin aut Boldrini, Chiara verfasserin aut Caldarelli, Guido verfasserin aut Cintia, Paolo verfasserin aut Cresci, Stefano verfasserin aut Facchini, Angelo verfasserin aut Giannotti, Fosca verfasserin aut Gionis, Aristides verfasserin aut Guidotti, Riccardo verfasserin aut Mathioudakis, Michael verfasserin aut Muntean, Cristina Ioana verfasserin aut Pappalardo, Luca verfasserin aut Pedreschi, Dino verfasserin aut Pournaras, Evangelos verfasserin aut Pratesi, Francesca verfasserin aut Tesconi, Maurizio verfasserin aut Trasarti, Roberto verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 4 vom: 31. März, Seite 311-340 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:4 day:31 month:03 pages:311-340 https://dx.doi.org/10.1007/s41060-020-00207-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 4 31 03 311-340 |
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10.1007/s41060-020-00207-3 doi (DE-627)SPR043933351 (DE-599)SPRs41060-020-00207-3-e (SPR)s41060-020-00207-3-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Andrienko, Gennady verfasserin aut (So) Big Data and the transformation of the city 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. Big data (dpeaa)DE-He213 Urban data science (dpeaa)DE-He213 SoBigData (dpeaa)DE-He213 Mobility datasets (dpeaa)DE-He213 Andrienko, Natalia verfasserin aut Boldrini, Chiara verfasserin aut Caldarelli, Guido verfasserin aut Cintia, Paolo verfasserin aut Cresci, Stefano verfasserin aut Facchini, Angelo verfasserin aut Giannotti, Fosca verfasserin aut Gionis, Aristides verfasserin aut Guidotti, Riccardo verfasserin aut Mathioudakis, Michael verfasserin aut Muntean, Cristina Ioana verfasserin aut Pappalardo, Luca verfasserin aut Pedreschi, Dino verfasserin aut Pournaras, Evangelos verfasserin aut Pratesi, Francesca verfasserin aut Tesconi, Maurizio verfasserin aut Trasarti, Roberto verfasserin aut Enthalten in International journal of data science and analytics Cham, Switzerland : Springer International Publishing, 2016 11(2020), 4 vom: 31. März, Seite 311-340 (DE-627)84425083X (DE-600)2843078-5 2364-4168 nnns volume:11 year:2020 number:4 day:31 month:03 pages:311-340 https://dx.doi.org/10.1007/s41060-020-00207-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_266 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2020 4 31 03 311-340 |
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Andrienko, Gennady @@aut@@ Andrienko, Natalia @@aut@@ Boldrini, Chiara @@aut@@ Caldarelli, Guido @@aut@@ Cintia, Paolo @@aut@@ Cresci, Stefano @@aut@@ Facchini, Angelo @@aut@@ Giannotti, Fosca @@aut@@ Gionis, Aristides @@aut@@ Guidotti, Riccardo @@aut@@ Mathioudakis, Michael @@aut@@ Muntean, Cristina Ioana @@aut@@ Pappalardo, Luca @@aut@@ Pedreschi, Dino @@aut@@ Pournaras, Evangelos @@aut@@ Pratesi, Francesca @@aut@@ Tesconi, Maurizio @@aut@@ Trasarti, Roberto @@aut@@ |
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The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. 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| author |
Andrienko, Gennady |
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Andrienko, Gennady ddc 004 misc Big data misc Urban data science misc SoBigData misc Mobility datasets (So) Big Data and the transformation of the city |
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Andrienko, Gennady |
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004 - Data processing & computer science |
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004 ASE (So) Big Data and the transformation of the city Big data (dpeaa)DE-He213 Urban data science (dpeaa)DE-He213 SoBigData (dpeaa)DE-He213 Mobility datasets (dpeaa)DE-He213 |
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ddc 004 misc Big data misc Urban data science misc SoBigData misc Mobility datasets |
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ddc 004 misc Big data misc Urban data science misc SoBigData misc Mobility datasets |
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International journal of data science and analytics |
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(So) Big Data and the transformation of the city |
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(So) Big Data and the transformation of the city |
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Andrienko, Gennady |
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International journal of data science and analytics |
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Andrienko, Gennady Andrienko, Natalia Boldrini, Chiara Caldarelli, Guido Cintia, Paolo Cresci, Stefano Facchini, Angelo Giannotti, Fosca Gionis, Aristides Guidotti, Riccardo Mathioudakis, Michael Muntean, Cristina Ioana Pappalardo, Luca Pedreschi, Dino Pournaras, Evangelos Pratesi, Francesca Tesconi, Maurizio Trasarti, Roberto |
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Elektronische Aufsätze |
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Andrienko, Gennady |
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10.1007/s41060-020-00207-3 |
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(so) big data and the transformation of the city |
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(So) Big Data and the transformation of the city |
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Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. |
| abstractGer |
Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. |
| abstract_unstemmed |
Abstract The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. |
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(So) Big Data and the transformation of the city |
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Andrienko, Natalia Boldrini, Chiara Caldarelli, Guido Cintia, Paolo Cresci, Stefano Facchini, Angelo Giannotti, Fosca Gionis, Aristides Guidotti, Riccardo Mathioudakis, Michael Muntean, Cristina Ioana Pappalardo, Luca Pedreschi, Dino Pournaras, Evangelos Pratesi, Francesca Tesconi, Maurizio Trasarti, Roberto |
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| score |
7.3985195 |