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Table 3 The big data analysis frameworks and methods

From: Big data analytics: a survey

\(\mathcal {P}\)

Name

References

Year

Description

\(\mathcal {T}\)

Analysis framework

DOT

[88]

2011

Add more computation resources via scale out solution

Framework

GLADE

[89]

2011

Multi-level tree-based system architecture

 

Starfish

[92]

2012

Self-tuning analytics system

 

ODT-MDC

[96]

2012

Privacy issues

 

MRAM

[91]

2013

Mobile agent technologies

 

CBDMASP

[94]

2013

Statistical computation and data mining approaches

 

SODSS

[97]

2013

Decision support system issues

 

BDAF

[93]

2014

Data centric architecture

 

HACE

[95]

2014

Data mining approaches

 

Hadoop

[83]

2011

Parallel computing platform

Platform

CUDA

[84]

2007

Parallel computing platform

 

Storm

[85]

2014

Parallel computing platform

 

Pregel

[125]

2010

Large-scale graph data analysis

 

MLPACK

[86]

2013

Scalable machine learning library

ML

Mahout

[87]

2011

Machine-learning algorithms

 

MLAS

[124]

2012

Machine-learning algorithms

 

PIMRU

[124]

2012

Machine Learning algorithms

 

Radoop

[129]

2011

Data analytics, machine learning algorithms, and R statistical tool

 

Mining algorithm

DBDC

[144]

2004

Parallel clustering

CLU

PKM

[145]

2009

Map-reduce-based k-means clustering

 

CloudVista

[111]

2012

Cloud computing for clustering

 

MSFCUDA

[113]

2013

GPU for clustering

 

BDCAC

[127]

2013

Ant on grid computing environment for clustering

 

Corest

[114]

2013

Use a tree construction for generating the coresets in parallel for clustering

 

SOM-MBP

[126]

2013

Neural network with CGP for classification

CLA

CoS

[115]

2013

Parallel computing for classification

 

SVMGA

[72]

2014

Using GA for reduce the number of dimensions

 

Quantum SVM

[116]

2014

Quantum computing for classification

 

DPSP

[121]

2010

Applied frequent pattern algorithm to cloud platform

FP

DHTRIE

[120]

2011

Applied frequent pattern algorithm to cloud platform

 

SPC, FPC, and DPC

[117]

2012

Map-reduce model for frequent pattern mining

 

MFPSAM

[119]

2014

Concerned the specific interest constraints and applied map-reduce model

 
  1. \(\mathcal {P}\) perspective, \(\mathcal {T}\) taxonomy, ML machine learning, CLU clustering, CLA classification, FP frequent pattern