SystemML: Declarative machine learning on MapReduce
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Scalable and Numerically Stable Descriptive Statistics in SystemML
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
| Hi-index | 0.00 |
Analytics on big data range from passenger volume prediction in transportation to customer satisfaction in automotive diagnostic systems, and from correlation analysis in social media data to log analysis in manufacturing. Expressing and running these analytics for varying data characteristics and at scale is challenging. To address these challenges, SystemML implements a declarative, high-level language using an R-like syntax extended with machine-learning-specific constructs, that is compiled to a MapReduce runtime [2]. The language is rich enough to express a wide class of statistical, predictive modeling and machine learning algorithms (Fig. 1). We chose robust algorithms that scale to large, and potentially sparse data with many features.