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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
A framework for big data analytics approach to failure prediction of construction firms
ترجمه فارسی عنوان مقاله:
چارچوبی برای رویکرد تحلیل داده های بزرگ برای پیش بینی شکست شرکت های ساختمانی
منبع:
Sciencedirect - Elsevier - Applied Computing and Informatics, Accepted manuscript: doi:10:1016/j:aci:2018:04:003
نویسنده:
Hafiz A. Alaka, Lukumon O. Oyedele, Hakeem A. Owolabi, Muhammad Bilal, Saheed O. Ajayi, Olugbenga O. Akinade
چکیده انگلیسی:
This study explored use of big data analytics (BDA) to analyse data of a large number of construction
firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of
literature revealed financial ratios as the best form of variable for this problem. Because of
MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA
initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was
proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural
network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was
developed in eight seconds while the same process without BDA was aborted after nine hours without
success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious
duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an
ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample
size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost
associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very
important for, among others, financiers, clients and policy makers.
Key Words: Big data analytics; failure prediction models; construction businesses; machine learning; MapReduce/Spark
قیمت: رایگان
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