51 citations · 129 across the 5 of their papers we have counts for
5 papers
Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
Yingyi Bu, Vinayak Borkar, Jianfeng Jia +2
There is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large graph datasets. Unfortunately, this challenge has not been eas…
AsterixDB: A Scalable, Open Source BDMS
Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry +20
AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its fea…
Iterative MapReduce for Large Scale Machine Learning
Joshua Rosen, Neoklis Polyzotis, Vinayak Borkar +5
Large datasets ("Big Data") are becoming ubiquitous because the potential value in deriving insights from data, across a wide range of business and scientific applications, is incr…
Scaling Datalog for Machine Learning on Big Data
Yingyi Bu, Vinayak Borkar, Michael J. Carey +5
In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine le…
Astronomy in the Cloud: Using MapReduce for Image Coaddition
Keith Wiley, Andrew Connolly, Jeff Gardner +5
In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources w…