کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4965483 1448368 2018 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Towards a data science toolbox for industrial analytics applications
ترجمه فارسی عنوان
به سوی یک جعبه ابزار دانش داده ای برای برنامه های کاربردی تجزیه و تحلیل صنعتی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


- We provide an overview of data science methods for industrial analytics.
- We propose a toolbox for predictive modeling in manufacturing.
- The toolbox is illustrated by means of a large-scale real world case study.

Manufacturing companies today have access to a vast number of data sources providing gigantic amounts of process and status data. Consequently, the need for analytical information systems is ever-growing to guide corporate decision-making. However, decision-makers in production environments are still very much focused on static, explanatory modeling provided by business intelligence suites instead of embracing the opportunities offered by predictive analytics. We develop a data science toolbox for manufacturing prediction tasks to bridge the gap between machine learning research and concrete practical needs. We provide guidelines and best practices for modeling, feature engineering and interpretation. To this end, we leverage tools from business information systems as well as machine learning. We illustrate the usage of this toolbox by means of a real-world manufacturing defect prediction case study. Thereby, we seek to enhance the understanding of predictive modeling. In particular, we want to emphasize that simply dumping data into “smart” algorithms is not the silver bullet. Instead, constant refinement and consolidation are required to improve the predictive power of a business analytics solution.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Industry - Volume 94, January 2018, Pages 16-25
نویسندگان
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