Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorRyu, Minjungen_US
dc.contributor.authorTruong, Linhen_US
dc.contributor.authorKannala, Mattien_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorComputer Science Professorsen
dc.contributor.groupauthorComputer Science - Computing Systems (ComputingSystems)en
dc.contributor.groupauthorComputer Science - Artificial Intelligence and Machine Learning (AIML)en
dc.contributor.organizationSolibrien_US
dc.date.accessioned2021-02-26T07:13:25Z
dc.date.available2021-02-26T07:13:25Z
dc.date.issued2021-12en_US
dc.description.abstractOptimizing quality trade-offs in an end-to-end big data science process is challenging, as not only do we need to deal with different types of software components, but also the domain knowledge has to be incorporated along the process. This paper focuses on methods for tackling quality trade-offs in a common data science process for classifying Building Information Modeling (BIM) elements, an important task in the architecture, engineering, and construction industry. Due to the diversity and richness of building elements, machine learning (ML) techniques have been increasingly investigated for classification tasks. However, ML-based classification faces many issues, w.r.t. vast amount of data with heterogeneous data quality, diverse underlying computing configurations, and complex integration with industrial BIM tools, in an end-to-end BIM data analysis. In this paper, we develop an end-to-end ML classification system in which quality of analytics is considered as the first-class feature across different phases, from data collection, feature processing, training to ML model serving. We present our method for studying the quality of analytics trade-offs and carry out experiments with BIM data extracted from Solibri to demonstrate the automation of several tasks in the end-to-end ML classification. Our results have demonstrated that the quality of data, data extraction techniques, and computing configurations must be carefully designed when applying ML classifications for BIM in order to balance constraints of time, cost, and prediction accuracy. Our quality of analytics methods presents generic steps and considerations for dealing with such designs, given the time, cost, and accuracy trade-offs required in specific contexts. Thus, the methods could be applied to the design of end-to-end BIM classification systems using other ML techniques and cloud services.en
dc.description.versionPeer revieweden
dc.format.extent30
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationRyu, M, Truong, L & Kannala, M 2021, ' Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling ', Journal of Big Data, vol. 8, no. 1, 31 . https://doi.org/10.1186/s40537-021-00417-xen
dc.identifier.doi10.1186/s40537-021-00417-xen_US
dc.identifier.issn2196-1115
dc.identifier.otherPURE UUID: 7bd229e5-06e0-412e-8868-18bd337e3ad2en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/7bd229e5-06e0-412e-8868-18bd337e3ad2en_US
dc.identifier.otherPURE LINK: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00417-xen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85100948783&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/56090812/s40537_021_00417_x.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/102788
dc.identifier.urnURN:NBN:fi:aalto-202102262077
dc.language.isoenen
dc.publisherSpringerOpen
dc.relation.ispartofseriesJournal of Big Dataen
dc.relation.ispartofseriesVolume 8en
dc.rightsopenAccessen
dc.titleUnderstanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modelingen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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