Machine Learning Supported Diabetes Prediction with Apache Spark

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...

MLlib: Machine Learning in Apache Spark

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shi...

Efficient iterative virtual screening with Apache Spark and conformal prediction

BACKGROUND Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. CONTRIBUTION In this study we propose a strategy that is b...

Getting Started with Apache Spark

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-...