Integrating Data Lakes with Self-Adaptive Serious Games

Michalis Pingos, Spyros Loizou, Andreas Andreou

2025

Abstract

Big Data challenges traditional data management tools due to their size, speed of generation, and variety of formats. The application of Big Data has become essential in areas like serious games, where it enhances functionality and effectiveness. Serious games benefit significantly from Big Data analytics, allowing for real-time data collection, processing and analysis of a vast number of users/players and their interactions. Traditional data management systems strive to handle the complexity of Big Data, particularly in environments like serious games, where diverse data sources create heterogeneity. This paper presents an approach that employs Data Lakes and semantic annotation as a solution for providing a scalable, flexible storage system for raw and unstructured data, enabling real-time processing and efficient management of Big Data produced by serious games. The effectiveness of the proposed approach is demonstrated through a speech therapy game example developed for the purposes of this study. A qualitative evaluation and comparison with two rival approaches is also performed using a set of criteria introduced in this work. The proposed approach offers an effective solution for handling data in multi-user gaming environments thus enhancing adaptability, personalization, and functional flexibility of serious games, and driving better user engagement and outcomes.

Paper Citation

in Harvard Style

Pingos M., Loizou S. and Andreou A. (2025). Integrating Data Lakes with Self-Adaptive Serious Games. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-742-9, SciTePress, pages 762-772. DOI: 10.5220/0013467700003928

in Bibtex Style

@conference{enase25,
author={Michalis Pingos and Spyros Loizou and Andreas Andreou},
title={Integrating Data Lakes with Self-Adaptive Serious Games},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2025},
pages={762-772},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013467700003928},
isbn={978-989-758-742-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Integrating Data Lakes with Self-Adaptive Serious Games
SN - 978-989-758-742-9
AU - Pingos M.
AU - Loizou S.
AU - Andreou A.
PY - 2025
SP - 762
EP - 772
DO - 10.5220/0013467700003928
PB - SciTePress