A graph-based big data optimization approach using hidden Markov model and constraint satisfaction problem

A graph-based big data optimization approach using hidden Markov model and constraint satisfaction problem

Abstract To address the challenges of big data analytics, several works have focused on big data optimization using metaheuristics. The constraint satisfaction problem (CSP) is a fundamental concept of metaheuristics that has shown great efficiency in several fields. Hidden Markov models (HMMs) are...

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Bibliographic Details
Main Authors: Imad Sassi, Samir Anter, Abdelkrim Bekkhoucha
Format: Article
Language:English
Published: SpringerOpen 2021-06-01
Series:Journal of Big Data
Subjects:
Machine learning
Big data analytics
Optimization
Metaheuristics
Time series forecasting
Graphical modeling
Online Access:https://doi.org/10.1186/s40537-021-00485-z

Internet

https://doi.org/10.1186/s40537-021-00485-z

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