Name
Abstract | ||
|---|---|---|
The rise of the Big Data age made traditional solutions for data processing and analysis unsuitable due to the high computational complexity. To address this problem, novel solutions specifically-designed techniques to analyse Big Data have been recently presented. In this path, when such a large amount of data arrives in a streaming manner, a sequential mechanism for the Big Data analysis is required. In this paper we target the modelling of high-dimension datastreams through hidden Markov models (HMMs) and introduce a HMM-based solution, named h-HMM, suitable for datastreams characterized by high dimensions. The proposed h-HMM relies on a suitably-defined clustering algorithm (operating in the space of the datastream dimensions) to create clusters of highly uncorrelated dimensions of the datastreams (as requested by the theory of HMMs) and a two-layer hierarchy of HMMs modelling the datastreams of such clusters. Experimental results on both synthetic and real-world data confirm the advantages of the proposed solution. |
Year | Venue | Field |
|---|---|---|
2016 | INNS Conference on Big Data | Data mining,Cluster (physics),Data processing,Computer science,Uncorrelated,Speech recognition,Hierarchy,Cluster analysis,Hidden Markov model,Big data,Computational complexity theory |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cesare Alippi | 1 | 1040 | 115.84 |
| Stavros Ntalampiras | 2 | 166 | 16.15 |
| Manuel Roveri | 3 | 272 | 30.19 |