Big data analytic untuk pembuatan rekomendasi koleksi film personal menggunakan Mlib. Apache Spark

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 ...

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...

An Information Theoretic Feature Selection Framework for Big Data under Apache Spark

With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory. Among many techniques, feature selection has been growing in interest as an important tool to identify relevant features on huge datasets –both in number of instances and features–. The purpose of this work is to demonstrate that standard feature selection methods can be paralleli...

Conquering Big Data with Spark

Today, big and small organizations alike collect huge amounts of data, and they do so with one goal in mind: extract "value" through sophisticated exploratory analysis, and use it as the basis to make decisions as varied as personalized treatment and ad targeting. To address this challenge, we have developed Berkeley Data Analytics Stack (BDAS), an open source data analytics stack for big data ...

Getting Started with Apache Spark