Ready Mealy, Moore & Markov Mathematical Modeling Machines for Big Data

Hafiz Furqan; Ahmad Wahab; Sultan Sadiq; Pir Ali Shah

Future Intelligent Vehicular Technologies. First International Conference, Future 5V 2016, Porto, Portugal, September 15, 2016, Revised Selected Papers

Research Article

Ready Mealy, Moore & Markov Mathematical Modeling Machines for Big Data

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@INPROCEEDINGS{10.1007/978-3-319-51207-5_21,
    author={Hafiz Furqan and Ahmad Wahab and Sultan Sadiq and Pir Ali Shah},
    title={Ready Mealy, Moore \& Markov Mathematical Modeling Machines for Big Data},
    proceedings={Future Intelligent Vehicular Technologies. First International Conference, Future 5V 2016, Porto, Portugal, September 15, 2016, Revised Selected Papers},
    proceedings_a={FUTURE 5V},
    year={2017},
    month={1},
    keywords={Markov model Probability Hidden-Markov model Finite state machine Process control Finite state automaton Big data},
    doi={10.1007/978-3-319-51207-5_21}
}

Hafiz Furqan
Ahmad Wahab
Sultan Sadiq
Pir Ali Shah
Year: 2017
Ready Mealy, Moore & Markov Mathematical Modeling Machines for Big Data
FUTURE 5V
Springer
DOI: 10.1007/978-3-319-51207-5_21

Hafiz Furqan¹^,*, Ahmad Wahab¹^,*, Sultan Sadiq¹^,*, Pir Ali Shah¹^,*

1: University of South Asia

*Contact email: hmftj@live.com, abdulwahabahmad96@gmail.com, shksadiqsultan@gmail.com, amad.ali@usa.edu.pk

Abstract

In this paper we have given a new idea of bringing automation to the Big data. This paper basically focused on the three core topics in automaton for Big data. Automation machines are used in today world to automate certain applied sciences ideas, into computational models. Mealy, Moore & Markov Probabilistic Modeling are one of those used to accomplish the said task. In particular we have focused on transducer and features in MATLAB and implication of system General Knowledge are focused in this paper.

Keywords: Markov model Probability Hidden-Markov model Finite state machine Process control Finite state automaton Big data

Published: 2017-01-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-51207-5_21