Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and
changed over time in various time-series-based tasks, eg weather prediction, network …

Data streams are potentially unbounded sequences of instances arriving over time to a
classifier. Designing algorithms that are capable of dealing with massive, rapidly arriving …

Streaming data incorporates dynamicity due to a nonstationary environment where data
samples may endure class imbalance and change in data distribution over the period …

Deep learning-based approaches have gained popularity for many applications in recent
years and have become the state-of-the-art method in machine learning applications …

This chapter focuses on strategies to extend and adapt traditional machine learning
algorithms for remote sensing and geospatial big data. Ten major strategies are discussed …

Big data analytics is rapidly emerging as a key Internet of Things (IoT) initiative aiming at
providing meaningful insights and supporting optimal decision making under time …

En la actualidad, muchas fuentes generan flujos de datos ilimitados a altas tasas de
entrada. Es imposible almacenar estos grandes volúmenes de datos por lo que es …

The extensive growth of digital technologies has led to new challenges in terms of
processing and distilling insights from data that generated continuously in real-time. To …

En la actualidad, muchas fuentes generan flujos de datos ilimitados a altas tasas de
entrada. Es imposible almacenar estos grandes volúmenes de datos por lo que es …

En la actualidad, muchas fuentes generan flujos de datos ilimitados a altas tasas de
entrada. Es imposible almacenar estos grandes volúmenes de datos por lo que es …