At the crux of many an enterprise-scale big data system stands either MapReduce or a parallel database management system. But which is more efficient? Researchers from Dublin Institute of Technology, ...

A startup named TigerGraph emerged from stealth today with a new native parallel graph database that its founder thinks can shake up the analytics market. With $31 million in venture funding and ...

In this video from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases. In this session we will present an ...

Abstract: In present scenario parallel database systems are being applicable in a broad range of systems, right from database applications (OLTP) server to decision support systems (OLAP) server.

Abstract: Hadoop is an efficient and simple parallel framework following the Map Reduce paradigm, and making the parallel processing recently become a hot issue in data-intensive applications. Since ...

This project aims to accelerate database query execution using a hybrid parallel approach, combining CPU-based parallelism (OpenMP) and GPU-based acceleration (CUDA). The expected outcome is a ...

Timely and cost-effective analytics over “big data” has emerged as a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors. Web clicks, social ...

Traditionally data acquisition has been the bottleneck for large scale proteomics. This has also remained one of the limitations in leveraging mass spectrometry within the clinic. PASEF and short ...