Abstract
Data-driven method has shown many advantages over experience- and mechanism-based approaches in optimizing production. In this paper, we propose an AI-driven automatic data analysis system. The system is developed for small and medium-sized industrial enterprises who are lack of expertise on data analysis. To achieve this goal, we design a structural and understandable task description language for problem modeling, propose an supervised learning method for algorithm selecting and implement a random search algorithm for hyper-parameter optimization, which makes our system highly-automated and generic. We choose R language as the algorithm engine due to its powerful analysis performance. The system reliability is ensured by an interactive analysis mechanism. Examples show how our system can apply to representative analysis tasks in manufactory.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
IBM SPSS Modeler. https://www.ibm.com/products/spss-modeler. Accessed 10 Jan 2020
SAS. https://www.sas.com/en_us/home.html. Accessed 10 Jan 2020
Castiello, C., Castellano, G., Fanelli, A.M.: Meta-data: characterization of input features for meta-learning. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds.) MDAI 2005. LNCS (LNAI), vol. 3558, pp. 457–468. Springer, Heidelberg (2005). https://doi.org/10.1007/11526018_45
Hwang, Y., Tong, A., Choi, J.: Automatic construction of nonparametric relational regression models for multiple time series. In: International Conference on Machine Learning, pp. 3030–3039 (2016)
Zhang, H., Wang, H., Li, J., Gao, H.: A generic data analytics system for manufacturing production. Big Data Min. Anal. 1(2), 160–171 (2018)
Acknowledgements
This paper was partially supported by NSFC grant U1866602, 61602129, 61772157, CCF-Huawei Database System Innovation Research Plan DBIR2019005B and Microsoft Research Asia.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liang, Z., Wang, H., Zhang, H., Guo, H. (2020). GMDA: An Automatic Data Analysis System for Industrial Production. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12114. Springer, Cham. https://doi.org/10.1007/978-3-030-59419-0_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-59419-0_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59418-3
Online ISBN: 978-3-030-59419-0
eBook Packages: Computer ScienceComputer Science (R0)