Scalable and efficient data analytics and mining with Lemonade

Walter dos Santos, Gustavo P. Avelar, Manoel Horta Ribeiro, Dorgival Guedes, Wagner Meira

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

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 languageEnglish (US)
Pages (from-to)2070-2073
Number of pages4
JournalProceedings of the VLDB Endowment
Volume11
Issue number12
DOIs
StatePublished - 2018
Externally publishedYes
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: Aug 27 2018Aug 31 2018

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science

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