Apache Spark in Healthcare: Advancing Data-Driven Innovations and Better Patient Care

Approximate Stream Analytics in Apache Flink and Apache Spark Streaming

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic trade-off between the output accuracy and computation effi...

CMS Analysis and Data Reduction with Apache Spark

Experimental Particle Physics has been at the forefront of analyzing the world’s largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called ”Big Data” technologies have emerged from industry and open source projects to support th...

Static and Dynamic Big Data Partitioning on Apache Spark

Many of today’s large datasets are organized as a graph. Due to their size it is often infeasible to process these graphs using a single machine. Therefore, many software frameworks and tools have been proposed to process graph on top of distributed infrastructures. This software is often bundled with generic data decomposition strategies that are not optimised for specific algorithms. In this ...

Getting Started with Apache Spark

A comparison on scalability for batch big data processing on Apache Spark and Apache Flink

*Correspondence: [email protected] 1Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Calle Periodista Daniel Saucedo Aranda, 18071 Granada, Spain Full list of author information is available at the end of the article Abstract The large amounts of data have created a need for new fram...