In the digital age, streaming data - information that is generated and processed in real-time - is abundant. Applying Artificial Intelligence (AI) to mine this data holds immense value. It enables real-time decision-making and provides immediate insights, which is particularly beneficial for industries like finance, healthcare, and transportation, where instant responses can make a significant difference.
However, mining streaming data with AI is not without challenges [1]. The sheer volume and speed of the data make it difficult for conventional data mining methods to keep up. It demands high-speed processing and robust algorithms to handle real-time analysis. Furthermore, maintaining data quality and integrity is paramount, but challenging in a real-time context. Ensuring privacy and security of the data while mining it also poses significant obstacles. And, given the 'black box' nature of many AI systems, transparency and understanding of the data mining process can also be a concern.
SEDIMARK, a secure decentralized and intelligent data and services marketplace, is making strides to address these issues. The Insight Centre for Data Analytics in University College Dublin contributes to the development of innovative AI technologies capable of efficiently handling and mining streaming data. By combining advanced distributed AI technologies with a strong commitment to ethical guidelines, SEDIMARK is paving the way for a future where AI-driven insights from streaming data can be harnessed effectively, reliably, and ethically. Our aim is to transform the challenges of real-time data processing into opportunities, enhancing decision-making capabilities and fostering a more data-driven world.
[1] Gomes, Heitor Murilo, et al. "Machine learning for streaming data: state of the art, challenges, and opportunities." ACM SIGKDD Explorations Newsletter 21.2 (2019): 6-22.
