Second ICAI WorkShop on Data Engineering and Analytics

Abstract

Data is everywhere. Data Analytics, Data Mining, Data Science or more generally the practice of identifying patterns or constructing mathematical models based on data is at the heart of most new business models or projects. This together with what has been termed as the Big Data phenomena, associated to data sets too big to be handled as usual, with non structured contents, data streams or all of these, has led to a whole new series of rapidly evolving tools, algorithms and methods which combine data engineering, computer science, statistics and mathematics.

The aim of this Workshop is to present recent results in this area, discussing new methods or algorithms, or applications including Machine Learning, Databases, optimization or any type of algorithms which consider managing or obtaining value from data.

Introduction

According to the Harvard Business Review, the data scientist is a new role that would be 'the sexiest job of the 21st century'. Over the last few years, the Big Data phenomenon has become part of the everyday language. According to IDC's expert committee, the digital universe is expected to grow by a factor of 44 from 2009 to 2020 to a trillion gigabytes. However, there is currently no consensus about what this new discipline called data science is. According to an article in Forbes magazine, it is a union between traditional disciplines such as statistics and computer science.

With this in mind, the workshop aims at combining two main areas of knowledge. The first one, data Engineering, which is defined as the process of designing, building, managing, and evaluating advanced data systems and applications. The second one, data analytics, which is defined as the process of deriving higher-level information from (usually large) sets of raw data.

The workshop will be hosted during the 2nd International Conference on Applied Informatics (ICAI), which aims to bring together researchers and practitioners working in different domains in the field of informatics in order to exchange their expertise and to discuss the perspectives of development and collaboration.

ICAI will be held in the Universidad Complutense de Madrid located in Madrid, Spain, from 7th to 9th November 2019. It is organized by the Universidad Distrital Francisco Jose de Caldas, Universidad de Bogota Jorge Tadeo Lozano, Universidad Complutense de Madrid, Universidad Nacional de Loja, Universidad a Distancia de Madrid, and BITrum Research Group.

Call for Papers

This workshop will consider papers which:

  • Develop/implement methods and techniques to store, prepare and analyze data in traditional as well as Big Data contexts.
  • Apply these methods to solve problems related to data management as required by society or business goals.

Keywords

Big Data, machine learning, databases, noSQL, data analytics, optimization, data engineering

Important Dates

  • Workshop Paper Submission: August 26th 2019
  • Workshop Paper Notification: September 16th 2019
  • Workshop Camera Ready: October 2nd 2019
  • Author registration : 2nd October 2019
  • Events date: 7st to 9rd November 2018

Submission Guidelines

Authors must submit an original full paper (10 to 15 pages) that has not previously been published. Authors should consult Springer's authors' guidelines and use LNCS templates, either for LaTeX or for MSWord (http://www.springer.com/la/computer-science/lncs/conference-proceedings-guidelines), for the preparation of their papers.

To submit or upload a paper please go to easychair

We encourage Overleaf users to use Springer's proceedings template for LaTeX

All submissions will be reviewed by 2 experts. Authors must remove personal details, acknowledgements section and any other information related to the authors identity.

Registration

For registration go to ICAI registration page

Proceedings

Workshop papers shall be submitted to CEUR-WS.org for online publication.

Organized by

Master in data engineering and analytics of UTadeo



Research group Data Driven Science of UNIR