Daron Green

Learn More
This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to the ALOJA project that uses machine learning techniques to interpret Hadoop benchmark performance data and performance tuning; here we detail the approach, efficacy of the model and initial results. The ALOJA-ML project is the latest phase of a long-term collaboration between BSC and(More)
This article presents the ALOJA project, an initiative to produce mechanisms for an automated characterization of cost-effectiveness of Hadoop deployments and reports its initial results. ALOJA is the latest phase of a long-term collaborative engagement between BSC and Microsoft which, over the past 6 years has explored a range of different aspects of(More)
In this paper, we describe the performance of the parallel GROMOS87 code, developed under the ESPRIT EUROPORT–2/PACC project, and indicate its potential impact in industry. An outline of the parallel code structure is given, followed by a discussion of the results of some industrially–relevant testcases. Conclusions are drawn as to the overall success of(More)
During the past years the exponential growth of data, its generation speed, and its expected consumption rate presents one of the most important challenges in IT both for industry and research. For these reasons, the ALOJA research project was created by BSC and Microsoft as an open initiative to increase cost-efficiency and the general understanding of Big(More)
A major factor in determining the suitability of a dried blood spot (DBS) specimen is the subjective nature of evaluation by laboratory personnel. Using newborn screening DBS specimen cards as they were submitted to a public health NBS program, we conducted a systematic pilot study of DBS evaluation by multiple experienced laboratory personnel (ELP) and by(More)