Big data processing systems are evolving to be more stream oriented where each data
record is processed as it arrives by distributed and low-latency computational frameworks on …

Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

Data-intensive applications fueled the evolution of log structured merge (LSM) based key-
value engines that employ the out-of-place paradigm to support high ingestion rates with low …

Scale-out stream processing engines (SPEs) are powering large big data applications on
high velocity data streams. Industrial setups require SPEs to sustain outages, varying data …

Recently, machine learning has become a hot research topic. Therefore, this study
investigates the interaction between software engineering and machine learning within the …

Today's social networks continuously generate massive streams of data, which provide a
valuable starting point for the detection of rumours as soon as they start to propagate …

Fog computing was designed to support the specific needs of latency-critical applications
such as augmented reality, and IoT applications which produce massive volumes of data …

The emergence of social media, the worldwide web, electronic transactions, and next-
generation sequencing not only opens new horizons of opportunities but also leads to the …

Data is a precious resource in today's society, and it is generated at an unprecedented and
constantly growing pace. The need to store, analyze, and make data promptly available to a …