@inproceedings{5c8bcfb314414542a8118ef1c394562e,
title = "M-IDAs: A Scalable and Droppable Big Data Intelligent Platform Based on Modular Design",
abstract = "More and more companies and individuals aware of the potential value in the accumulating data. With the development of data analysis technology and the application of machine learning methods, it becomes possible to mine the value in data and maximize it. However, there are problems such as low data utilization and insufficient depth and breadth of data mining. And the main reasons are: (1) The development cost of the data analysis application platform is high. (2) Business personnel lack knowledge in the field of data analysis and have high learning costs, or the data processing efficiency is low. Therefore, this paper proposes a Scalable and Droppable Big Data Intelligent Platform base on Modular Design called M-IDAs to provide intelligent data analysis support. Which adopts modular design technology to decompose and package the data processing and analyzing functions to form various visual modules that can be easily used and operated independently. It can effectively reduce costs, improve efficiency, and promote data utilization and value maximization.",
keywords = "Big Data, data analysis technology, machine learning, modular design, visualization",
author = "Haitian Zeng and Chao Wang and Ruili Ye and Yan Wang and Nan Yang and Yibo Xue",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2020 ; Conference date: 23-05-2020",
year = "2020",
month = may,
doi = "10.1109/HPBDIS49115.2020.9130564",
language = "English",
series = "2020 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 International Conference on High Performance Big Data and Intelligent Systems, HPBD and IS 2020",
address = "United States",
}