Table 8 Methods and techniques for big data stream analysis

SPADE

[132]

Locally supervised metric learning (LSML)

[133]

KTS

[106]

Multinomial latent dirichlet allocation

[106]

Voltage clustering algorithm

[106]

Locality sensitive hashing (LSH)

[134]

User profile vector update algorithm

[134]

Tag assignment stream clustering (TASC)

[134]

StreamMap

[117]

Density cognition

[117]

QRS detection algorithm

[87]

Forward chaining rule

[110]

Stream

[135]

CluStream

[136, 137]

HPClustering

[138]

DenStream

[139]

D-Stream

[140]

ACluStream

[141]

DCStream

[142]

P-Stream

[143]

ADStream

[144]

Continuous query processing (CQR)

[145]

FPSPAN-growth

[146]

Outlier method for cloud computing algorithm (OMCA)

[147]

Multi-query optimization strategy (MQOS)

[148]

Parallel K-means clustering

[72]

Visibly push down automata (VPA)

[73]

Incremental MI outlier detection algorithm (Inc I-MLOF)

[149]

Adaptive windowing based online ensemble (AWOE)

[74]

Dynamic prime-number based security verification

[84]

K-anonymity, I-diversity, t-closeness

[90]

Singular spectrum matrix completion (SS-MC)

[76]

Temporal fuzzy concept analysis

[96]

ECM-sketch

[77]

Nearest neighbour

[91]

Markov chains

[91]

Block-QuickSort-AdjacentJobMatch

[86]

Block-QuickSort-OverlapReplicate

[86]

Fuzzy-CSar-AFP

[150]

Weighted online sequential extreme learning machine with kernels (WOS-ELMK)

[22]

Concept-adapting very fast decision tree (CVFDT)

[151]