Adaptive and intelligent data

Project Details

Description

Despite recent advances in artificial intelligent technologies, the potential of applying data-driven methods to optimize the performance of networking systems remains poorly investigated and largely unexplored. In this project we propose a radical paradigm shift by making the data in the network to be the key entity. Our objective is to enhance networks with capabilities of large-scale data analytics, so as to enable novel network-management policies that anticipate the network behavior and make intelligent choices. We will achieve this by introducing artificial intelligence into the network and exploiting the learning and adaptation possibilities that it offers. The key research question we seek to answer is how artificial intelligence can help make the network and its services better, both for the users and network operators alike.
StatusFinished
Effective start/end date01/01/201830/06/2022

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  • Clustering with Fair-Center Representation: Parameterized Approximation Algorithms and Heuristics

    Thejaswi, S., Gadekar, A., Ordozgoiti, B. & Osadnik, M., 14 Aug 2022, KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, p. 1749-1759 11 p.

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

    Open Access
    3 Citations (Scopus)
  • Concise and interpretable multi-label rule sets

    Ciaperoni, M., Xiao, H. & Gionis, A., 2022, Proceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022. Zhu, X., Ranka, S., Thai, M. T., Washio, T. & Wu, X. (eds.). IEEE, p. 71-80 10 p. (IEEE International Conference on Data Mining; vol. 2022-November).

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

    Open Access
  • Generalized Leverage Scores: Geometric Interpretation and Applications

    Ordozgoiti, B., Matakos, A. & Gionis, A., 2022, Proceedings of the 39th International Conference on Machine Learning. JMLR, p. 17056-17070 (Proceedings of Machine Learning Research ; vol. 162).

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

    Open Access
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    1 Citation (Scopus)
    36 Downloads (Pure)