A Survey on Domain-Specific Languages for Machine

Learning

August 3, 2017

CISC 603-50- R-2017/Summer – Theory of Computation

Student: Dileep Sharma

Instructor: Majid Shaalan

Contents

1 Statement 2

2 Abstract 2

3 Introduction 2

3.1 Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

3.2 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

3.3 Domain Specific Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

4 DSL Feature Model 4

4.1 Language Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

4.2 Transformation Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4.3 DSL Tool Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

4.4 DSL Process Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

5 Languages Surveyed 9

5.1 OptiML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

5.2 ScalOps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5.3 Scala . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5.4 PIG LATIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5.5 Breukervl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

5.6 Possibility of survey of other language . . . . . . . . . . . . . . . . . . . . . 11

5.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

6 Reference 12

1

1 Statement

The purpose of this paper is to identify, describe and design Domain Specific Language(

DSL ) applicable to Machine learning world in big data space, that can make process more

faster and efficient.

2 Abstract

In last couple of decades, the data we have at our disposal have increased tremendously

because of technology advance. This technology advance has helped us in capturing, storing,

analogizing and visualizing data and that has lead to big data. We need better algorithm

to read and analyze these big and complex diastases. Machine Learning is turning out to be

the most effective way of analyzing these datasets and predicting future behavior. To better

analyze these datasets with Machine Learning we need enhanced computational power, that

can be obtained using parallel processing using GPUs. Machine Learning algorithms needs

to be adapted and optimized to specific applications. However, programming these devices

to run efficiently and correctly is difficult, error-prone, and results in software that is harder

to read and maintain. This paper is primarily concern about Domain Specific language that

can help us in writing Machine Algorithms in efficient way to analyze Big Data.

3 Introduction

Technological advance in recent past has caused a data revolution. This high volume of data

is called big data.Every second, smartphones, tablets,cars, websites, and systems generate

a massive amount of data, and users and software engineers have access to a subset of that

data to perform their activities.

3.1 Big Data

Apart for large amount of data, Big data also accounts for complex data, known as vari-

ety.Big Data also created new challenges in data management. Traditional ways of data

storage and analysis do not scale well to this amount of data, which can reach hundreds

of terabytes or more, and new approaches are being developed to address these issues Big

data is basically defined by 5V’s:

• Volume

– Refers to amount of data

– Big Data doesnt sample

2

– Big Data observes and tracks what happens

• Velocity

– Speed of data processing

– Speed of data generation

– Big Data is often available in real-time

• Variety

– Number of types of data

• Variability

– Inconsistency of data

• Veracity

– Quality of data

3.2 Machine Learning

Machine learning is turning out to be one of the most advanced technique to process and

make inferences from Big Data. Machine Learning is widely used to discover identify trends,

patterns, suggest actions, and optimize output. There are still a lot of challenges in using

Machine Learning to solve big data problems, such as memory and time issues. To resolve

these issues, we can use GPUs for parallel processing and scatter data across different

machines. There are basically two kind of Machine Learning:

• Supervised

– All data is labeled

– You have both Input variable and Output variable

– Use an algorithm to learn the mapping function from the input to the output

• Unsupervised

– All data is unlabeled

– You only have input data and no corresponding output variables

– Algorithm try to find pattern in input data

3

3.3 Domain Specific Language

In model-driven engineering, a Domain-Specific Language (DSL) is a specialized language,

which, combined to a transformation function, serves to raise the abstraction level of soft-

ware and ease software development. The Machine Learning Implementation can be made

better by using techniques such as Domain-Specific. DSL solves problem in a single domain

while General Purpose Languages(GSL) solves problems in a couple of domains. DSLs

facilitate results to be expressed in the idiom and at the level of abstraction of the prob-

lem domain Language.DSLs offer pre-defined abstractions to represent concepts from the

application domain. This representation may be more clear and intuitive. Moreover, DSL

compilers may optimize the code written for the specific domain, and they can perform

error detection more efficiently. Lastly, DSLs may have more specific tool support that help

software engineers increase their productivity. These languages are easier to learn. There

can be three kind of DSL languages:

• Markup language

• Specification Language

• Programming Language

4 DSL Feature Model

DSl Feature model covers languages, transformation, tooling, and process aspects 1. Lan-

guage and transformation are mandatory features because they are parts of the DSL defini-

tion. Tool is also mandatory because it serves to automate transformation from a domain,

the problem space, down to lower abstraction levels, the solution space. Process is optional

because it can be undefined or implicit.

4.1 Language Features

There are two language features called as2:

• Abstract Syntax

– Characterizes elements of a domain and their relationships without implementa-

tion consideration

• Concrete Syntax

– Representation of a DSL in a human usable form

4

Figure 1: Roots of DSL Feature Language

Figure 2: Language Feature

5

Figure 3: Root of the Transformation Features

4.2 Transformation Feature

Transformation feature ensures the correspondence from the problem to the solution, takes

into account the problem-to-solution element mapping, and all design, implementation,

platform and architecture decisions. Transformation has to answer to three questions3:

How to specify transformation4? What are the assets expected from the transformation5?

How to realize the transformation to produce the expected assets? 6.