Table 1 Analyzed methods and the maximum data size managed by each one

[70]

FS

630

65,003,913

305.1196

Hadoop MapReduce

[69]

FS

630

65,003,913

305.1196

Hadoop MapReduce

[74]

FS

1,156

5,670,000

48.8350

MPI

[60]

FS

29,890,095

19,264,097

4.1623

C++/MATLAB

[59]

FS

100,000

10,000,000

1.4901

MapReduce

[76]

FS

100

1,600,000

1.1921

Apache Spark

[80]

FS

127

1,131,571

1.0707

Hadoop MapReduce

[71]

FS

54,675

2,096

0.8538

Hadoop MapReduce

[75]

FS

54

581,012

0.2338

Hadoop MapReduce

[73]

FS

20

1,000,000

0.1490

MapReduce

[77]

FS

–

–

0.0976

Hadoop MapReduce

[79]

FS

256

38,232

0.0729

Hadoop MapReduce

[68]

FS

52

5,253

0.0020

Hadoop MapReduce

[78]

FS

–

–

0.0000

Hadoop MapReduce

[67]

FS

–

–

0.0000

Hadoop MapReduce

[72]

FS

–

–

0.0000

Hadoop MapReduce

[83]

Imbalanced

630

32,000,000

150.2037

Hadoop MapReduce

[84]

Imbalanced

630

32,000,000

150.2037

Hadoop MapReduce

[90]

Imbalanced

630

16,000,000

75.1019

Apache Spark

[89]

Imbalanced

41

4,856,151

1.4834

Hadoop MapReduce

[82]

Imbalanced

41

4,000,000

1.2219

Hadoop MapReduce

[81]

Imbalanced

14

1,432,941

0.1495

Hadoop MapReduce

[86]

Imbalanced

9,731

1,446

0.1048

Hadoop MapReduce

[91]

Imbalanced

14

524,131

0.0547

Hadoop MapReduce

[87]

Imbalanced

36

95,048

0.0255

Hadoop MapReduce

[88]

Imbalanced

8

2,687,280

0.0200

Hadoop MapReduce

[93]

Incomplete

625

4,096,000

19.0735

MapReduce (Twister)

[92]

Incomplete

481

191,779

0.6873

Hadoop MapReduce

[95]

discretization

630

65,003,913

305.1196

Apache Spark

[96]

discretization

630

65,003,913

305.1196

Apache Spark

[94]

discretization

–

–

4.0000

Hadoop MapReduce

[97]

IR

41

4,856,151

1.4834

Hadoop MapReduce