From the evolution of public data ecosystems to the evolving horizons of the forward-looking intelligent public data ecosystem empowered by emerging technologies
<|reference_start|>From the evolution of public data ecosystems to the evolving horizons of the forward-looking intelligent public data ecosystem empowered by emerging technologies: Public data ecosystems (PDEs) represent complex socio-technical systems crucial for optimizing data use in the public sector and outside it. Recognizing their multifaceted nature, previous research pro-posed a six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed as a result of a systematic literature review on the topic spanning three decade, this model, while theoretically robust, necessitates empirical validation to enhance its practical applicability. This study addresses this gap by validating the theoretical model through a real-life examination in five European countries - Latvia, Serbia, Czech Republic, Spain, and Poland. This empirical validation provides insights into PDEs dynamics and variations of implementations across contexts, particularly focusing on the 6th generation of forward-looking PDE generation named "Intelligent Public Data Generation" that represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence, Natural Language Processing tools, Generative AI, and Large Language Models (LLM) with potential to contribute to both automation and augmentation of business processes within these ecosystems. By transcending their traditional status as a mere component, evolving into both an actor and a stakeholder simultaneously, these technologies catalyze innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.<|reference_end|>
@article{nikiforova2024from,
    title={From the evolution of public data ecosystems to the evolving horizons of
  the forward-looking intelligent public data ecosystem empowered by emerging
  technologies},
    author={Anastasija Nikiforova, Martin Lnenicka, Petar Mili'c, Mariusz Luterek
  and Manuel Pedro Rodr'iguez Bol'ivar},
    journal={In: Janssen, M, J. Crompvoets, J. Ramon Gil-Garcia, H. Lee, I
  Lindgren, A Nikiforova, G. Viale Pereira. Electronic Government. EGOV 2024.
  Lecture Notes in Computer Science, Springer, Cham},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13606},
    primaryClass={cs.CY cs.AI cs.ET cs.HC cs.IR}
}