Scalable and Efficient Data Analytics and Mining with Lemonade

被引:5
|
作者
dos Santos, Walter [1 ]
Avelar, Gustavo P. [1 ]
Ribeiro, Manoel Horta [1 ]
Guedes, Dorgival [1 ]
Meira Jr, Wagner [1 ]
机构
[1] Univ Fed Minas Gerais, Dept Ciencia Comp, Belo Horizonte, MG, Brazil
来源
PROCEEDINGS OF THE VLDB ENDOWMENT | 2018年 / 11卷 / 12期
关键词
D O I
10.14778/3229863.3236262
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Professionals outside of the area of Computer Science have an increasing need to analyze large bodies of data. This analysis often demands high level of security and has to be done in the cloud. However, current data analysis tools that demand little proficiency in systems programming struggle to deliver solutions which are scalable and safe. In this context we present Lemonade, a platform which focuses on creating data analysis and mining flows in the cloud, with authentication, authorization and accounting (AAA) guarantees. Lemonade provides an interface for the visual construction of flows, and encapsulates storage and data processing environment details, providing higher-level abstractions for data source access and algorithms. We illustrate its usage through a demo, where a data processing flow builds a classification model for detecting fake-news, also extracting some insights along the way.
引用
收藏
页码:2070 / 2073
页数:4
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