Apache SystemML Framework

A Apache SystemML Framework is a machine learning framework ...

• Example(s):
  • SystemML v0.14.0 (2017-06-26) for Spark 2.
  • …
• Counter-Example(s):
  • Spark MLlib.
• See: Declarative Programming, Apache Spark.

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References

2017

• https://systemml.apache.org/docs/0.14.0/algorithms-reference.html
  • QUOTE:

Descriptive Statistics
   Univariate Statistics
   Bivariate Statistics
   Stratified Bivariate Statistics
Classification
   Multinomial Logistic Regression
   Support Vector Machines
       Binary-Class Support Vector Machines
       Multi-Class Support Vector Machines
   Naive Bayes
   Decision Trees
   Random Forests
Clustering
   K-Means Clustering
Regression
   Linear Regression
   Stepwise Linear Regression
   Generalized Linear Models
   Stepwise Generalized Linear Regression
   Regression Scoring and Prediction
Matrix Factorization
   Principal Component Analysis
   Matrix Completion via Alternating Minimizations
Survival Analysis
   Kaplan-Meier Survival Analysis
   Cox Proportional Hazard Regression Model

2016

• (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/Apache_SystemML Retrieved:2016-6-3.
  • Apache SystemML (incubating) is a flexible machine learning system that automatically scales to Spark and Hadoop clusters. SystemML's

    distinguishing characteristics are:

    1. Algorithm customizability via R-like and Python-like languages.
    2. Multiple execution modes, including Standalone, Spark Batch, Spark MLContext, Hadoop Batch, and JMLC.
    3. Automatic optimization based on data and cluster characteristics to ensure both efficiency and scalability.

2014

• http://researcher.watson.ibm.com/researcher/view_group.php?id=3174
  • Declarative large-scale machine learning (ML) in SystemML aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations to distributed computations on MapReduce or Spark. ML algorithms are expressed in an R-like syntax, that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data and cluster characteristics ensures both efficiency and scalability. As such, SystemML differs from existing work on large-scale ML libraries, which mostly provide fixed algorithms and runtime plans.

2011

• (Ghoting et al., 2011) ⇒ Amol Ghoting, Rajasekar Krishnamurthy, Edwin Pednault, Berthold Reinwald, Vikas Sindhwani, Shirish Tatikonda, Yuanyuan Tian, and Shivakumar Vaithyanathan. (2011). “SystemML: Declarative Machine Learning on MapReduce.” In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering. ISBN:978-1-4244-8959-6 doi:10.1109/ICDE.2011.5767930

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