With an ever-increasing amount of options, the task of selecting machine learning tools for
big data can be difficult. The available tools have advantages and drawbacks, and many …

Many governments are considering adopting the smart city concept in their cities and
implementing big data applications that support smart city components to reach the required …

Big data has become an important issue for a large number of research areas such as data
mining, machine learning, computational intelligence, information fusion, the semantic Web …

Spark SQL is a new module in Apache Spark that integrates relational processing with
Spark's functional programming API. Built on our experience with Shark, Spark SQL lets …

Apache Flink 1 is an open-source system for processing streaming and batch data. Flink is
built on the philosophy that many classes of data processing applications, including real …

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day
business (eg Web logs, mobile usage statistics, and sensor networks). At the same time …

Stream processors are emerging in industry as an apparatus that drives analytical but also
mission critical services handling the core of persistent application logic. Thus, apart from …

The rising need for custom machine learning (ML) algorithms and the growing data sizes
that require the exploitation of distributed, data-parallel frameworks such as MapReduce or …

The traditional databases are not capable of handling unstructured data and high volumes
of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious …

The Internet of Things (IoT) has become widespread around the world. Since a large
number of diverse devices, such as vehicles, household electrical appliances, smart …