From the Evolution of Public Data Ecosystems to the Evolving Horizons of the Forward-Looking Intelligent Public Data Ecosystem Empowered by Emerging Technologies
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021792" target="_blank" >RIV/62690094:18450/24:50021792 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-3-031-70274-7_25" target="_blank" >http://dx.doi.org/10.1007/978-3-031-70274-7_25</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-70274-7_25" target="_blank" >10.1007/978-3-031-70274-7_25</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
From the Evolution of Public Data Ecosystems to the Evolving Horizons of the Forward-Looking Intelligent Public Data Ecosystem Empowered by Emerging Technologies
Popis výsledku v původním jazyce
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 proposed a six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed as a result of a systematic literature review on the topic spanning three decades, 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" which represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence (AI), Natural Language Processing tools, Generative AI, and Large Language Models 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 catalyse innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.
Název v anglickém jazyce
From the Evolution of Public Data Ecosystems to the Evolving Horizons of the Forward-Looking Intelligent Public Data Ecosystem Empowered by Emerging Technologies
Popis výsledku anglicky
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 proposed a six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed as a result of a systematic literature review on the topic spanning three decades, 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" which represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence (AI), Natural Language Processing tools, Generative AI, and Large Language Models 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 catalyse innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
ELECTRONIC GOVERNMENT, EGOV 2024
ISBN
978-3-031-70274-7
ISSN
0302-9743
e-ISSN
1611-3349
Počet stran výsledku
17
Strana od-do
402-418
Název nakladatele
SPRINGER INTERNATIONAL PUBLISHING AG
Místo vydání
Cham
Místo konání akce
Ghent
Datum konání akce
3. 9. 2024
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001308584400025