Abstract
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 ows in the cloud, with authentication, authorization and accounting (AAA) guarantees. Lemonade provides an interface for the visual construction of ows, 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 ow builds a classification model for detecting fake-news, also extracting some insights along the way.
Original language | English (US) |
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Pages (from-to) | 2070-2073 |
Number of pages | 4 |
Journal | Proceedings of the VLDB Endowment |
Volume | 11 |
Issue number | 12 |
DOIs | |
State | Published - 2018 |
Externally published | Yes |
Event | 44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil Duration: Aug 27 2018 → Aug 31 2018 |
All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- General Computer Science