Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark

Getting Started with Apache Spark

Apache Spark and Apache Kafka at the Rescue of Distributed RDF Stream Processing Engines

Due to the growing need to timely process and derive valuable information and knowledge from data produced in the Semantic Web, RDF stream processing (RSP) has emerged as an important research domain. In this paper, we describe the design of an RSP engine that is built upon state of the art Big Data frameworks, namely Apache Kafka and Apache Spark. Together, they support the implementation of a...

Balanced Graph Partitioning with Apache Spark

A significant part of the data produced every day by online services is structured as a graph. Therefore, there is the need for efficient processing and analysis solutions for large scale graphs. Among the others, the balanced graph partitioning is a well known NP-complete problem with a wide range of applications. Several solutions have been proposed so far, however most of the existing state-...

SPARQL query processing with Apache Spark

The number and the size of linked open data graphs keep growing at a fast pace and confronts semantic RDF services with problems characterized as Big data. Distributed query processing is one of them and needs to be efficiently addressed with execution guaranteeing scalability, high availability and fault tolerance. RDF data management systems requiring these properties are rarely built from sc...

Benchmarking Apache Spark with Machine Learning Applications

We benchmarked Apache Spark with a popular parallel machine learning training application, Distributed Stochastic Gradient Descent for Matrix Factorization [5] and compared the Spark implementation with alternative approaches for communicating model parameters, such as scheduled pipelining using POSIX socket or MPI, and distributed shared memory (e.g. parameter server [13]). We found that Spark...