Dr. Mario Pichler

International Cooperations Manager

Telefon: +43 50 343 898

mario.pichler@scch.at

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Mario Pichler is responsible for International Cooperations in Data Science & Software Science at the SCCH. He has more than 20 years of experience in application-oriented research in the areas of Ubiquitous Computing, Software Science and Data Science.

He obtained both his Master's degree (in 2001) and Doctoral degree (in 2007) in Computer Science from the Johannes Kepler University Linz, Austria, with a doctoral thesis on Ubiquitous Computing. Beyond that, he completed a BSc study of Geosciences and Atmosphere Sciences at the University of Innsbruck in 2015. As a result of these research activities, he has published more than 40 scientific publications in aforementioned areas.

His current work focusses on the development of international collaborations and projects in the areas of Green and Digital (Industrial) Transformation and Artificial Intelligence with a personal interest on formal knowledge representation and reasoning. Mario has many years of project management experience in national and international projects. He is currently coordinating the Interreg AT-CZ project PredMAIn (AI-based Predictive Maintenance, ATCZ279) and the international project outreach in the H2020 project TEAMING.AI (Grant nr. 957402).

Publikationen

2023
Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review

Martinez-Gil, J., Hoch, T., Pichler, M., Heinzl, B., Moser, B., Kurniawan, K., Kiesling, E., & Krause, F. (2023). Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review. Artificial Intelligence in Manufacturing, 55–70.
Leveraging Semantic Representations via Knowledge Graph Embeddings

Krause, F., Kurniawan, K., Kiesling, E., Martinez-Gil, J., Hoch, T., Pichler, M., Heinzl, B., & Moser, B. (2023). Leveraging Semantic Representations via Knowledge Graph Embeddings. Artificial Intelligence in Manufacturing, 71–85.
Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0

Hoch, T., Martinez-Gil, J., Pichler, M., Silvina, A., Heinzl, B., Moser, B., Eleftheriou, D., Estrada-Lugo, H. D., & Leva, M. C. (2023). Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0. Artificial Intelligence in Manufacturing, 407–421.
2022
A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME

Qasim, M., Khan, M., Mehmood, W., Sobieczky, F., Pichler, M., & Moser, B. (2022). A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME. Database and Expert Systems Applications - DEXA 2022 Workshops, 22–31.
A Systematic Mapping Study of Predictive Maintenance in SMEs

Khan, M., Ahmad, A., Sobieczky, F., Pichler, M., Moser, B. A., & Bukovsky, I. (2022). A Systematic Mapping Study of Predictive Maintenance in SMEs. IEEE Access, 10, 88738–88749.
2021
AVUBDI: A Versatile Usable Big Data Infrastructure and its Monitoring Approaches for Process Industry

Luftensteiner, S., Mayr, M., Chasparis, G. C., & Pichler, M. (2021). AVUBDI: A Versatile Usable Big Data Infrastructure and Its Monitoring Approaches for Process Industry. Frontiers in Chemical Engineering, 3.
A Digital Platform to Facilitate the Resilience of Rural Territories

Martinez-Gil, J., Pichler, M., Lentini, G., Mazzeschi, V., Doukhan, G., & Belet, C. (2022). A Digital Platform to Facilitate the Resilience of Rural Territories. Journal of Information & Knowledge Management, 21(03).
2020
Framework for data analysis in the context of the smart villages

Martinez-Gil, J., Pichler, M., Turkanović, M., Beranič, T., Gradišnik, M., Lentini, G., Lué, A., Vitale, A. C., Doukhan, G., & Belet, C. (2020). Framework for Data Analysis in the Context of the Smart Villages. Lecture Notes in Computer Science, 31–45.
General model for tracking manufacturing products using graph databases

Martinez-Gil, J., Stumptner, R., Lettner, C., Pichler, M., Mahmoud, S., Praher, P., & Freudenthaler, B. (2020). General Model for Tracking Manufacturing Products Using Graph Databases. Data-Driven Process Discovery and Analysis, 86–100.
2019
Facing mental workload in AI-transformed working environments

Gusenleitner, N., Siedl, S., Stübl, G., Polleres, A., Recski, G., Sommer, R., Leva, M. C., Pichler, M., Kopetzky, T., &Moser, B. (2019). Facing mental workload in AI-transformed working environments. H-WORKLOAD 2019 : 3rd International Symposium on Human Mental Workload: Models and Applications, Rome, Italy, November 14-15, 2019.
Facilitating the digital transformation of villages

Beranič, T., Zamuda, A., Brezočnik, L., Lentini, G., Polettini, F., Lue, A., Colorni Vitale, A., Martinez-Gil, J., Pichler, M., & Turkanović, M. (2019) Facilitating the digital transformation of villages. Proceedings of the Central European Conference on Information and Intelligent Systems (CECIIS 2019), pages 281-288, October 2019.
A cooperative demand-response framework for day-ahead optimization in battery pools

Chasparis, G. C., Pichler, M., Spreitzhofer, J., & Esterl, T. (2019). A cooperative demand-response framework for day-ahead optimization in battery pools. Energy Informatics, 2(S1).
Design and implementation of a graph-based solution for tracking manufacturing products

Martinez-Gil, J., Stumpner, R., Lettner, C., Pichler, M., & Fragner, W. (2019). Design and Implementation of a Graph-Based Solution for Tracking Manufacturing Products. New Trends in Databases and Information Systems, 417–423.
Framework for assessing the smartness maturity level of villages

Martinez-Gil, J., Pichler, M., Beranič, T., Brezočnik, L., Turkanović, M., Lentini, G., Polettini, F., Lué, A., Vitale, A. C., Doukhan, G., & Belet, C. (2019). Framework for Assessing the Smartness Maturity Level of Villages. New Trends in Databases and Information Systems, 501–512.
A demand-response framework in balance groups through direct battery-storage control

Chasparis, G. C., Pichler, M., & Natschlager, T. (2019). A Demand-Response Framework in Balance Groups through Direct Battery-Storage Control. 2019 18th European Control Conference (ECC).
A novel approach for learning how to automatically match job offers and candidate profiles

Martinez-Gil, J., Paoletti, A. L., & Pichler, M. (2019). A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles. Information Systems Frontiers, 22(6), 1265–1274.
Decentralized energy networks based on blockchain: Background, overview and concept discussion

Pichler, M., Meisel, M., Goranovic, A., Leonhartsberger, K., Lettner, G., Chasparis, G., Vallant, H., Marksteiner, S., & Bieser, H. (2019). Decentralized Energy Networks Based on Blockchain: Background, Overview and Concept Discussion. Lecture Notes in Business Information Processing, 244–257.

Dr. Mario Pichler

Details

Publikationen

2023

Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review

Leveraging Semantic Representations via Knowledge Graph Embeddings

Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0

2022

A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME

A Systematic Mapping Study of Predictive Maintenance in SMEs

2021

AVUBDI: A Versatile Usable Big Data Infrastructure and its Monitoring Approaches for Process Industry

A Digital Platform to Facilitate the Resilience of Rural Territories

2020

Framework for data analysis in the context of the smart villages

General model for tracking manufacturing products using graph databases

2019

Facing mental workload in AI-transformed working environments

Facilitating the digital transformation of villages

A cooperative demand-response framework for day-ahead optimization in battery pools

Design and implementation of a graph-based solution for tracking manufacturing products

Framework for assessing the smartness maturity level of villages

A demand-response framework in balance groups through direct battery-storage control

A novel approach for learning how to automatically match job offers and candidate profiles

Decentralized energy networks based on blockchain: Background, overview and concept discussion