Ontological Framework for Integrating Predictive Analytics, AI, and Big Data in Decision-Making Systems Using Knowledge Graph

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022577" target="_blank" >RIV/62690094:18450/25:50022577 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0013514400003970" target="_blank" >http://dx.doi.org/10.5220/0013514400003970</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0013514400003970" target="_blank" >10.5220/0013514400003970</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ontological Framework for Integrating Predictive Analytics, AI, and Big Data in Decision-Making Systems Using Knowledge Graph

Popis výsledku v původním jazyce

The rapid development of AI, big data and DSS is changing decision-making processes by enabling the efficient processing of huge volumes of data for strategic and operational decisions. The increasing complexity of data-driven decision making requires the integration of predictive analytics, machine learning and knowledge-based systems. This paper presents an ontological framework that uses a knowledge graph to systematically depict the interrelationships between these technologies and supports transparent, efficient and ethical decision making in the areas of business intelligence, healthcare, public policy and crisis management. It also addresses challenges such as algorithmic bias, ethical considerations and explain ability and highlights the need for responsible AI deployment. © 2025 by Paper published under CC license (CC BY-NC-ND 4.0).

Název v anglickém jazyce

Ontological Framework for Integrating Predictive Analytics, AI, and Big Data in Decision-Making Systems Using Knowledge Graph

Popis výsledku anglicky

The rapid development of AI, big data and DSS is changing decision-making processes by enabling the efficient processing of huge volumes of data for strategic and operational decisions. The increasing complexity of data-driven decision making requires the integration of predictive analytics, machine learning and knowledge-based systems. This paper presents an ontological framework that uses a knowledge graph to systematically depict the interrelationships between these technologies and supports transparent, efficient and ethical decision making in the areas of business intelligence, healthcare, public policy and crisis management. It also addresses challenges such as algorithmic bias, ethical considerations and explain ability and highlights the need for responsible AI deployment. © 2025 by Paper published under CC license (CC BY-NC-ND 4.0).

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)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the International Conference on Simulation and Modeling Methodologies, Technologies and Applications

ISBN

978-989-758-759-7

ISSN

2184-2841

e-ISSN

—

Počet stran výsledku

8

Strana od-do

287-294

Název nakladatele

Science and Technology Publications, Lda

Místo vydání

Setúbal

Místo konání akce

Bilbao

Datum konání akce

11. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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