Big Data and Climate Change Impact Track

@ IDEAS 2019

23rd International Database Engineering & Applications Symposium

June 10-12, 2019 Athens, Greece

Track Organized by

Harokopio University of Athens, with the cooperation of ACM, and

Track Chairs:
Martine Collard, Université des Antilles, France
General Chairs: .
Bipin C. Desai, Concordia University, Montreal
Dimosthenis Anagnostopoulos, Harokopio University of Athens;

Program Chairs:
Yannis Manolopoulos, Open University of Cyprus
Mara Nikolaidou, Harokopio University of Athens,

Local Chair:
George Dimitrakopoulos, Harokopio University of Athens
Dimitris Michail, Harokopio University of Athens,

Call for Track Papers: Big Data and Climate Change Impact

This track forms part of the annual IDEAS conference: a top international forum for data engineering researchers, practitioners, developers, and application users to explore revolutionary ideas and results, and to exchange techniques, tools, and experiences.



« Big Data » is a domain that offers a wide panel of techniques and tools for managing, processing and analyzing big volumes of structured, semi-structured and unstructured data that are potentially interesting to be mined. Massive data need specific methods and tools to be stored and analyzed as well. Traditional data mining algorithms and relational database operators are not efficient on them as they require high-performance solutions. Global climate change is one of the main current challenges all over the world. There is considerable work to find solutions for climate mitigation and adaptation in human life, biodiversity and natural environments among which big data analysis and data science can provide valuable insights into climate change impact. Indeed both real data collected by observation of natural processes and data resulting from model simulations reach very large sizes and offer what we can call climate big data challenges. Climate data and meteorological data are specially abundant and related domains such as environment, biodiversity, energy, health, mobility, agriculture or tourism for instance generate also large amounts of data.

The goal of this track is to present contributions on big data solutions as they contribute to studying climate change impact and more broadly to gain insight the diversity of massive data techniques and tools as they relate specifically to climate change.

The track will provide a forum for researchers, practitioners, business leaders and policy makers, for the discussion and the sharing of new ideas and solutions, aiming at creating a scientific background for a solid development of innovative solutions using big data for analyzing climate change .



Topics of interest include, but are not limited to the following:

– Big data models related to climate change

– Preprocessing of Big data for climate change impact analysis

– Big data quality and climate change

– Algorithms and techniques for Big data analysis in the context of climate change

– Big data solutions as climates services

– Big data and climate change in urban planning, energy, water management, economics, biodiversity, agriculture, tourism, human behavior domains

Submission for the track

When submitting a paper, pl. select the appropriate track unless you are submitting to the main event (indicated by « General Pool »).


The papers submitted for a track would be reviewed by the program committee aided by the members of this track committee. The accepted papers would be presented during IDEAS 2019 and would be included in the proceedings of IDEAS 2019.

Please consult the guides given in the right hand margin for FAQ, submission guidelines etc.

All paper submission for IDEAS’2019 would be via the corresponding pages on ConfSys. Authors should sign up as user of the system at:

Conference Publication

The conference proceedings will be published by ACM in their digital library; the ISBN assigned by ACM to IDEAS’2019 is: 978-1-4503-6249-8

A version of the proceedings to be distributed to the conference attendees would be prepared by BytePress.