Motivation & Main Objectives

Our society faces a wide range of global challenges in different areas such as sociology, economy, ecology and technology. Solving the most vital problems in these fields often requires the analysis of a huge amount of data, and the utilization of appropriate methods and facilities for ample computations. In HiDALGO we develop a computational and data analytics environment, which enables systematic collaboration between scientists with different background and facilitates a profound understanding of several major global challenges.

Scientific Goals

In HiDALGO we conduct research and go beyond state-of-the-art in multiple areas. Our scientific goals drive the technology evolution targeted in our project and form the foundation of our business objectives. Specifically, we pursue the following goals:

Technology Evolution

In HiDALGO, we advance HPC, HPDA and AI technologies in order to improve data-centric computation in general. Although we mainly concentrate on three specific pilot applications, the technology baseline we develop in this project can also be extended to other application domains as well. The technology evolution in HiDALGO integrates our scientific objectives into a platform, which documents the success of the individual project developements and generates the required impact to establish a sustainable Centre of Excellence.

"The HiDALGO project focusses on modelling and simulating the complex processes which arise in connection with major global challenges. The researchers have developed the Flu and Coronavirus Simulator (FACS) with the objective to support decision makers to provide an appropriate response to the current pandemic situation taking into account health and care capabilities...." 

Graph Analyzer Tool

The Graph Analyzer is a validation tool for social graph generators. It compares an empirical graph of a real world social network with a synthetic graph from a generator. It does this by computing and comparing statistical properties of both graphs. For comparison we use distributions of: vertex degree, clustering coefficient, shortest distance between vertices and harmonic centrality.

PREDICTING FORCED DISPLACEMENT USING A GENERALISED AND AUTOMATED AGENT-BASED SIMULATION

Within the last decades, international migration demonstrated an escalating growth with more than 68 million people forcibly displaced worldwide. Forced displacement has a huge impact on society today as 40 million people internally displaced within their home country and 25.4 million refugees fled to neighbouring countries. Forcibly displaced people face several concerns, namely, the choice to stay or flee, the choice to flee internally or across borders, and the choice of destination.

Applying machine learning methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon

Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a “canyon” in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO2), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM10 particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013.

Jan Velimsky, Christoph Schweimer, Christine Gfrerer

Message spread on Social Media. Comparing the FPÖ and NEOS during the election campaign of the 2019 Austrian National Council Elections

Dimitrios Tsoumakos, Ioannis Giannakopoulos

Content-based Analytics: Moving Beyond Data Size