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Article: Big Data In Construction Waste Management: Prospects And Challenges

TitleBig Data In Construction Waste Management: Prospects And Challenges
Authors
KeywordsBig data
Construction waste management
Big data analytics
Hong Kong
Issue Date2018
PublisherCISA Publisher. The Journal's web site is located at https://digital.detritusjournal.com/issues/current
Citation
Detritus, 2018, v. 4, p. 129-139 How to Cite?
Abstract‘Big data’ has been rapidly sprawling in various research disciplines such as biology, ecology, medical science, business, finance, and public governance but rarely in construction waste management (CWM). The CWM community around the world generally relies on ‘small data’ collected via active solicitation such as sampling and ethnographic methods. This small data is intrinsically limited by its inability to account for the totality of CWM and research findings generated from the small data cannot be accepted with a high level of confidence. With the growing interests in big data, it can be reasonably expected that the waste management community will augment efforts to develop big data and its analytics. However, the efforts are currently constrained by the limited knowledge to do so. This research aims to provide a synoptic overview of the prospects and challenges of big data in CWM. It adopts an inductive, qualitative case study method whereby the empirical data is collected using an ethnographic–action-meta-analysis research approach and triangulated with data from literature, ongoing debate, and other sources. The paper offers some insights on big data acquisition, storage, analytics, implementation, and challenges. Although having a focus on waste management in the construction sector, the insights generated from this study can be of value to general waste management research, which suffers from the same problems of erratic and poor quality data as CWM.
Persistent Identifierhttp://hdl.handle.net/10722/265995
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLu, W-
dc.contributor.authorWebster, CJ-
dc.contributor.authorPeng, Y-
dc.contributor.authorChen, X-
dc.contributor.authorChen, K-
dc.date.accessioned2018-12-17T02:16:29Z-
dc.date.available2018-12-17T02:16:29Z-
dc.date.issued2018-
dc.identifier.citationDetritus, 2018, v. 4, p. 129-139-
dc.identifier.issn2611-4135-
dc.identifier.urihttp://hdl.handle.net/10722/265995-
dc.description.abstract‘Big data’ has been rapidly sprawling in various research disciplines such as biology, ecology, medical science, business, finance, and public governance but rarely in construction waste management (CWM). The CWM community around the world generally relies on ‘small data’ collected via active solicitation such as sampling and ethnographic methods. This small data is intrinsically limited by its inability to account for the totality of CWM and research findings generated from the small data cannot be accepted with a high level of confidence. With the growing interests in big data, it can be reasonably expected that the waste management community will augment efforts to develop big data and its analytics. However, the efforts are currently constrained by the limited knowledge to do so. This research aims to provide a synoptic overview of the prospects and challenges of big data in CWM. It adopts an inductive, qualitative case study method whereby the empirical data is collected using an ethnographic–action-meta-analysis research approach and triangulated with data from literature, ongoing debate, and other sources. The paper offers some insights on big data acquisition, storage, analytics, implementation, and challenges. Although having a focus on waste management in the construction sector, the insights generated from this study can be of value to general waste management research, which suffers from the same problems of erratic and poor quality data as CWM.-
dc.languageeng-
dc.publisherCISA Publisher. The Journal's web site is located at https://digital.detritusjournal.com/issues/current-
dc.relation.ispartofDetritus-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectBig data-
dc.subjectConstruction waste management-
dc.subjectBig data analytics-
dc.subjectHong Kong-
dc.titleBig Data In Construction Waste Management: Prospects And Challenges-
dc.typeArticle-
dc.identifier.emailLu, W: wilsonlu@hku.hk-
dc.identifier.emailWebster, CJ: cwebster@hku.hk-
dc.identifier.emailChen, K: chenk726@hku.hk-
dc.identifier.authorityLu, W=rp01362-
dc.identifier.authorityWebster, CJ=rp01747-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.31025/2611-4135/2018.13737-
dc.identifier.hkuros296281-
dc.identifier.volume4-
dc.identifier.spage129-
dc.identifier.epage139-
dc.publisher.placeItaly-

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