Over the last decade, several interconnected disruptions have happened in the large scale
distributed and parallel computing landscape. The volume of data currently produced by …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …

In social network big data scheduling, it is easy for target data to conflict in the same data
node. Of the different kinds of entropy measures, this paper focuses on the optimization of …

An important as well as challenging task in modern applications is the management and
processing with very short delays of large data volumes. It is quite often, that the capabilities …

Cloud environments have provided great help for the development of big data. Today, cloud
environments have become the most active platform for big data analysis applications …

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of
data that arrives continuously. Streams of big data led to the emergence of several …

Cloud computing has become a popular platform for processing big data analysis jobs with
its advantages of high-availability, elasticity and cost-efficiency. Many big data analysis …

Big data applications are currently used in many application domains, ranging from
statistical applications to prediction systems and smart cities. However, the quality of these …

Abstract The emergence of Big Data has had a profound impact on how data are analyzed.
Open source distributed stream processing platforms have gained popularity for analyzing …

Many IoT applications and services such as smart parking and smart traffic control contain a
network of different analytical components, which are composed in the form of a workflow to …