@inproceedings{487257066d3d41bf8fcb1e632070e8d4,
title = "GMDA: An Automatic Data Analysis System for Industrial Production",
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.",
keywords = "Automatic, Data analysis system, Interactive",
author = "Zhiyu Liang and Hongzhi Wang and Hao Zhang and Hengyu Guo",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 ; Conference date: 24-09-2020 Through 27-09-2020",
year = "2020",
doi = "10.1007/978-3-030-59419-0\_56",
language = "英语",
isbn = "9783030594183",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "780--784",
editor = "Yunmook Nah and Bin Cui and Sang-Won Lee and Yu, \{Jeffrey Xu\} and Yang-Sae Moon and Whang, \{Steven Euijong\}",
booktitle = "Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings",
address = "德国",
}