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WDEA 2019 : SECOND WORKSHOP ON DATA ENGINEERING AND ANALYTICS
WDEA 2019 : SECOND WORKSHOP ON DATA ENGINEERING AND ANALYTICS

WDEA 2019 : SECOND WORKSHOP ON DATA ENGINEERING AND ANALYTICS

MADRID
Event Date: November 07, 2019 - November 09, 2019
Submission Deadline: September 02, 2019
Notification of Acceptance: September 16, 2019
Camera Ready Version Due: October 02, 2019




Call for Papers

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.

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.

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

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



Credits and Sources

[1] WDEA 2019 : SECOND WORKSHOP ON DATA ENGINEERING AND ANALYTICS


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