The recent advances in sensor and communication technologies can provide the
foundations for linking the physical manufacturing facility and machine world to the cyber …

Big Data has become a very popular term. It refers to the enormous amount of structured,
semi-structured and unstructured data that are exponentially generated by high …

In this paper we introduce LDBC Graphalytics, a new industrial-grade benchmark for graph
analysis platforms. It consists of six deterministic algorithms, standard datasets, synthetic …

Big data applications (eg, Extract, Transform, and Load (ETL) applications) are designed to
handle great volumes of data. However, processing such great volumes of data is time …

Abstract The field of Big Data and related technologies is rapidly evolving. Consequently,
many benchmarks are emerging, driven by academia and industry alike. As these …

Big data analytics workloads are very significant ones in modern data centers, and it is more
and more important to characterize their representative workloads and understand their …

Distributed dataflow systems like Spark or Flink enable users to analyze large datasets.
Users create programs by providing sequential user-defined functions for a set of well …

For getting up-to-date insight into online services, extracted data has to be processed in
near real time. For example, major big data companies (Facebook, LinkedIn, Twitter) …

Sheer increase in volume of data over the last decade has triggered research in cluster
computing frameworks that enable web enterprises to extract big insights from big data …

While cluster computing frameworks are continuously evolving to provide real-time data
analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics …