Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics (98770)

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    Inffeldgasse 13 Graz


Organisation profile

Organisation profile

High-impact cutting-edge research in six strategic research areas – application-oriented and in close cooperation with our consulting team. We develop the foundations to drive the data-driven transformation of companies and society. Data Management for AI The Data Management for AI (DAI) research area aims to tame these datasets by providing high-level data science abstractions and developing systems that perform these tasks efficiently and are scalable. All this, while considering increasing specialization at all levels, such as hardware, software, and domain-specific applications. Data Privacy for AI In the research area “Data Privacy for AI”, we research and develop efficient and long-term secure (quantum computer safe) cryptographic methods that can be used for a wide range of practical applications. We create secure foundations for trustworthy AI and also overcome the performance problems that still exist today with complex machine learning algorithms. Methods & Algorithms for AI The Research Area “Methods and Algorithms for AI” researches and develops a wide range of foundations, methods, and algorithms for trustworthy AI. We combine key technical concepts, and methods such as Machine Learning, Natural Language Processing (NLP), Deep Reinforcement Learning (DLR), and Data Science (DS). Our long-term goal is to develop trustworthy algorithms that are highly advanced. So, that they can automatically derive hypotheses from data from a wide variety of sources while maintaining the highest privacy standards, and can validate these hypotheses with additional data. Human-AI Interaction The Human-AI Interaction research area works on interactive machine learning techniques, immersive and visual analytics, and AI-driven computer user interface methods that promote mutual understanding and cooperation between humans and AI. For this to succeed, both humans and AI must be able to communicate with each other, provide feedback, and act on that knowledge. The result for us is the use of an AI that enables fluid cooperation between humans and machines and gives us the freedom to use our human strength of genuine creativity. Fair AI The mission of the Fair AI Research Area is to research and develop fair, bias-free algorithms and evaluation methods that minimize the risks of discrimination and promote trust in AI. In close collaboration with the other Research Areas, we are working on the profound understanding of causal relationships underlying wrong decisions. Our goal is to develop fair algorithms and evaluation methods that are essential building blocks of trustworthy AI. We want to support users in their self-determined, critical, and informed decision-making and interaction with AI-based systems (e.g., recommender systems). Digital Transformation Design The Digital Transformation Design research area develops novel methods and (technological) tools for processes that provide the best possible support for people, companies, and society in shaping digital transformation. Whether an AI is accepted as trustworthy also depends on how transparent, secure, and comprehensible the processes behind it are. We are pursuing the vision of an AI-based technology that brings different stakeholders of companies, institutions, or society into dialog with each other and thus enables new, constructive social processes. Contact: Know-Center GmbH Inffeldgasse 13/6
 A-8010 Graz
 Tel.: +43 316 873 30801
 Fax: +43 316 873 1030810
 E-Mail: [email protected] For further information please see:


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Collaborations and top research areas from the last five years

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  • Connecting Actors With the Introduction of Mobile Technology in Health Care Practice Placements (4D Project): Protocol for a Mixed Methods Study

    4D Project Consortium, Jan 2024, In: JMIR Research Protocols. 13, 1, e53284.

    Research output: Contribution to journalArticlepeer-review

    Open Access
  • CyVerse: Cyberinfrastructure for open science

    Swetnam, T. L., Antin, P. B., Bartelme, R., Bucksch, A., Camhy, D., Chism, G., Choi, I., Cooksey, A. M., Cosi, M., Cowen, C., Culshaw-Maurer, M., Davey, R., Davey, S., Devisetty, U., Edgin, T., Edmonds, A., Fedorov, D., Frady, J., Fonner, J., Gillan, J. K., & 29 othersHossain, I., Joyce, B., Lang, K., Lee, T., Littin, S., McEwen, I., Merchant, N., Micklos, D., Nelson, A., Ramsey, A., Roberts, S., Sarando, P., Skidmore, E., Song, J., Sprinkle, M. M., Srinivasan, S., Stanzione, D., Strootman, J. D., Stryeck, S., Tuteja, R., Vaughn, M., Wali, M., Wall, M., Walls, R., Wang, L., Wickizer, T., Williams, J., Wregglesworth, J. & Lyons, E., Feb 2024, In: PLoS Computational Biology. 20, 2 February, e1011270.

    Research output: Contribution to journalArticlepeer-review

    Open Access
  • DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines

    Vontzalidis, A., Psomadakis, S., Bitsakos, C., Dokter, M., Innerebner, K., Damme, P., Boehm, M., Ciorba, F., Eleliemy, A., Karakostas, V., Zamuda, A. & Tsoumakos, D., 2024, Euro-Par 2023: Parallel Processing Workshops - Euro-Par 2023 International Workshops, 2023, Revised Selected Papers. Zeinalipour, D., Blanco Heras, D., Pallis, G., Herodotou, H., Trihinas, D., Balouek, D., Diehl, P., Cojean, T., Fürlinger, K., Kirkeby, M. H., Nardelli, M. & Di Sanzo, P. (eds.). Springer Science and Business Media Deutschland GmbH, p. 242-246 5 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14352 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review