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Conference Paper: Prospects and challenges of big data in construction waste management: A Hong Kong study
Title | Prospects and challenges of big data in construction waste management: A Hong Kong study |
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Authors | |
Issue Date | 2015 |
Publisher | CISA Publisher. |
Citation | Sardinia 2015: 15th International Waste Management and Landfill Symposium, S. Margherita di Pula, Cagliari, Sardinia, Italy, 5-9 October 2015 How to Cite? |
Abstract | Big Data has shown great potentials in improving management discretion in many areas. However, its applications in construction waste management (CWM) are still in infant stage. This research aims to investigate the prospects and challenges of big data in CWM, by focusing on Hong Kong where a big dataset is made available recently. This study first conducted a comprehensive literature review of big data to understand its definitions, applications, and general challenges of using big data in various sectors. Next, the data collected from Hong Kong government was analyzed to explore the prospects and challenges of Big Data in CWM. Except for the general challenges, big data in CWM has specific challenges mainly due to the specificity of public sectors implementing schemes of CWM. Possible strategies are raised to deal with the challenges so as to embrace the prospects of big data in CWM and relevant domains. This study not only provides government and other sectors in CWM and relevant domains with a clearer understanding of the prospects and challenges of big data that they are facing and corresponding strategies, but also acts as a driving force to stimulate the adoption and proper utilization of big data in sectors involved in CWM. |
Persistent Identifier | http://hdl.handle.net/10722/223957 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Chen, X | - |
dc.contributor.author | Lu, W | - |
dc.contributor.author | Wang, HD | - |
dc.date.accessioned | 2016-03-18T02:32:41Z | - |
dc.date.available | 2016-03-18T02:32:41Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Sardinia 2015: 15th International Waste Management and Landfill Symposium, S. Margherita di Pula, Cagliari, Sardinia, Italy, 5-9 October 2015 | - |
dc.identifier.isbn | 9788862650212 | - |
dc.identifier.uri | http://hdl.handle.net/10722/223957 | - |
dc.description.abstract | Big Data has shown great potentials in improving management discretion in many areas. However, its applications in construction waste management (CWM) are still in infant stage. This research aims to investigate the prospects and challenges of big data in CWM, by focusing on Hong Kong where a big dataset is made available recently. This study first conducted a comprehensive literature review of big data to understand its definitions, applications, and general challenges of using big data in various sectors. Next, the data collected from Hong Kong government was analyzed to explore the prospects and challenges of Big Data in CWM. Except for the general challenges, big data in CWM has specific challenges mainly due to the specificity of public sectors implementing schemes of CWM. Possible strategies are raised to deal with the challenges so as to embrace the prospects of big data in CWM and relevant domains. This study not only provides government and other sectors in CWM and relevant domains with a clearer understanding of the prospects and challenges of big data that they are facing and corresponding strategies, but also acts as a driving force to stimulate the adoption and proper utilization of big data in sectors involved in CWM. | - |
dc.language | eng | - |
dc.publisher | CISA Publisher. | - |
dc.relation.ispartof | International Waste Management and Landfill Symposium | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Prospects and challenges of big data in construction waste management: A Hong Kong study | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lu, W: wilsonlu@hku.hk | - |
dc.identifier.authority | Lu, W=rp01362 | - |
dc.description.nature | preprint | - |
dc.identifier.hkuros | 257349 | - |
dc.publisher.place | Padova, Italy | - |