Skip to main content

Advertisement

Table 8 Methods and techniques for big data stream analysis

From: Big data stream analysis: a systematic literature review

Methods and techniques Article
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]