Apache Kafka has become a popular solution for managing networked data streaming in a
variety of applications, from industrial to general purpose. This paper systematically surveys …

This chapter provides students, industry experts, and researchers a high-level and
comprehensive overview of the end-to-end architectures of big data stream processing …

Abstract Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to
train, improve, and make predictions through their algorithms. With the digital revolution and …

Contribution: Recently, real-time data warehousing (DWH) and big data streaming have
become ubiquitous due to the fact that a number of business organizations are gearing up to …

Class label noise is a critical component of data quality that directly inhibits the predictive
performance of machine learning algorithms. While many data-level and algorithm-level …

In this paper, the performance of a solution providing stream processing is evaluated, and its
accuracy in the classification of suspicious flows in simulated network traffic is investigated …

Analyzing time-dependent data acquired in a continuous flow is a major challenge for
various fields, such as big data and machine learning. Being able to analyze a large volume …

Industry 4.0 is characterized by digitalized production facilities, where a large volume of
sensors collect a vast amount of data that is used to increase the sustainability of the …

Several software systems are built upon stream processing architectures to process large
amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) …

To support the growing demand for data-intensive and low-latency IoT applications, Multi-
Access Edge Computing (MEC) is emerging as an effective edge-computing approach …