Challenges in Enabling Quality of Analytics in the Cloud

Truong, Hong-Linh; Murguzur, Aitor; Yang, Erica

doi:10.1145/3138806

Cited by 3 publications

(2 citation statements)

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“…Our previous work outlines challenges in QoA [5] and issues of quality in big data analytics [31]. The main tenet of QoA is that, in an end-to-end data processing system, one must consider various trade-offs of quality of data, processing time, cost, result accuracy, underlying computing capabilities, to name just a few, based on specific analysis context.…”

Section: Understanding the Quality Trade-offs In End-to-end Bim Objecmentioning

confidence: 99%

“…However, this has not been researched well in big data analytics/ML for BIM classification. Hence understanding the trade-offs between factors of Quality of Analytics (QoA) [5,6], such as quality of data, execution time, and resulting prediction accuracy, is of paramount importance for ML classification systems for BIM, especially because building models are heavily created by professionals through manual design-experiment tasks. In this paper, we contribute (i) the design of an end-to-end BIM classification system with QoA and (ii) methods and extensive analysis of trade-offs of BIM classification pipelines considering QoA.…”

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confidence: 99%

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

2021

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Optimizing 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.

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Section: Understanding the Quality Trade-offs In End-to-end Bim Objecmentioning

confidence: 99%

mentioning

confidence: 99%