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Recommendations for the MIP Technical Development During SGA3

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An Exploratory Study of Reactions to Bot Comments on GitHub

Denis Gillet, Juan Carlos Farah, Sandy Ingram, Xinyang Lu

The widespread use of bots to support software development makes social coding platforms such as GitHub a particularly rich source of data for the study of human-bot interaction. Software development bots are used to automate repetitive tasks, interacting ...

ACM2022

An Integrated Approach to Learning Analytics

Denis Gillet, Juan Carlos Farah, Sandy Ingram

Despite the rise in services generating learning analytics, there is a lack of standard models and guidelines for data integration and aggregation to inform the design choices of applications supporting learning analytics. We propose a bottom-up, user-driv ...

2021

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Damien Bouffard, Camille Roland Marie Minaudo, Theo Baracchini

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters als ...

MDPI2021

In-depth assessment of potential new data integration into the MIP

Tomas Teijeiro Campo

In the scope of the Human Brain Project (SGA2), this document summarizes from the technical perspective the main challenges faced by the current Medical Informatics Platform for the integration of three new types of data: complex neuroimaging, omics, and d ...

2020

DELF: Safeguarding deletion correctness in Online Social Networks

Georgios Damaskinos

Deletion is a core facet of Online Social Networks (OSNs). For users, deletion is a tool to remove what they have shared and control their data. For OSNs, robust deletion is both an obligation to their users and a risk when developer mistakes inevitably oc ...

USENIX ASSOC2020

A semantic-driven approach for Industry 4.0

Gökan May, Sangje Cho

Toward industry 4.0, modern manufacturing companies are aiming at building digital twins to manage physical assets, processes, people, and places. Since in this environment, massive amounts of data have been generated and collected, integration and managem ...

IEEE2019

Macroecology in the age of Big Data - Where to go from here?

Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this "Big Data" trend, with a strong rise in the volume of data that we are using for our research. Here, we sum ...

2019

Time- and Space-Efficient Spatial Data Analytics

Mirjana Pavlovic

Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...

EPFL2019

Thermal profile construction for energy-intensive industrial sectors

François Maréchal, Ivan Daniel Kantor, Greta Martha Van Eetvelde, Maziar Kermani, Anna Sophia Wallerand, Hür Ebuzer Bütün, Alessio Santecchia, Franz Wolf

Industrial plant data are difficult to find in academic literature for a number of reasons such as confidentiality, and thus intentional masking, or problem size reduction. These common practices limit the ability of researchers to apply novel methods to r ...

2018

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of datasets to gain insights. At the same time, data variety increases continuously across multiple axes. First, data comes in mu ...

EPFL2017