Current Aspects of Data Ecosystems

Current trends and future developments in the field of data storage, processing, analysis, output and visualization include various possible directions and future developments of these systems. This paper discusses the current state of data ecosystems and possible future developments based on an analysis of publications and studies from the last time. Commonly known trends point to approaches that enable more efficient data management, faster data analysis and, of course, the integration of different types of data coming from different sources. Additionally, the study evaluates the ETL process performance, identifying key metrics, optimizations, and bottlenecks in data processing workflows. The benefits and development of generative models are also changing the way we work with data, with implications for improved visualizations and general process automation. Thus, in the area of analysis, it can be assumed there will be an even greater emphasis on interactive and intuitive reports that make it as easy as possible to understand complex data structures. In terms of the use of system resources, the use of Cloud services is the basis for comprehensive use of scalability and flexibility at lower infrastructure costs. Another area that the paper discusses is the current growing demand for real-time data analysis due to the increasing need for quick decisions based on data evaluation.

cloud computing, data analysis, data management, data visualization, real-time processing

1. M. Kvet, S. Toth and E. Krsak E., "Concept of temporal data retrieval: Undefined value management”, Concurrency Computation Practice and Experience. 2020, https://doi.org/10.1002/cpe.5399

2. M. Kvet, “Developing Robust Date and Time Oriented Applications in Oracle Cloud: A comprehensive guide to efficient date and time management in Oracle Cloud”, Packt Publishing, 2023, ISBN: 978-1804611869

3. Erasmus+ project EverGreen, Accessed: 15.1.2025, Available: https://evergreen.uniza.sk/

4. M. Salnitri, E. Ramalli and B. Pernici, "Towards a policy tuning method for data ecosystems," 2024 IEEE International Conference on Web Services (ICWS), Shenzhen, China, 2024, pp. 55-64, https://doi.org/10.1109/ICWS62655.2024.00024

5. D. Rudmark and M. Andersson, "Feedback Loops in Open Data Ecosystems," in IEEE Software, vol. 39, no. 1, pp. 43-47, Jan.-Feb. 2022, https://doi.org/10.1109/MS.2021.3116874

6. C. Rix, H. Stein, Q. Chen, J. Frank and W. Maass, "Conceptualizing Data Ecosystems for Industrial Food Production," 2021 IEEE 23rd Conference on BusinessInformatics (CBI), Bolzano, Italy, 2021, pp. 201-210, https://doi.org/10.1109/CBI52690.2021.00031

7. E. Ramalli and B. Pernici, "Sustainability and Governance of Data Ecosystems," 2023 IEEE International Conference on Web Services (ICWS), Chicago, IL, USA, 2023, pp. 740-745, https://doi.org/10.1109/ICWS60048.2023.00100

8. T. Brée, E. Karger and F. Ahlemann, "Shaping the Future of Data Ecosystem Research—What Is Still Missing?," in IEEE Access, vol. 12, pp. 103162-103175, 2024, https://doi.org/10.1109/ACCESS.2024.3432969

9. Dhaouadi, A.; Bousselmi, K.; Gammoudi, M.M.; Monnet, S.; Hammoudi, S. “Data Warehousing Process Modeling from Classical Approaches to New Trends: Main Features and Comparisons”. Data 2022, 7, 113. https://doi.org/10.3390/data7080113

10. Favaretto M, De Clercq E, Schneble CO, Elger BS (2020) What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PLOS ONE 15(2): e0228987. https://doi.org/10.1371/journal.pone.0228987

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