# An Algebraic Expert System with Neural Network Concepts for Cyber, Big Data and Data Migration

Title | An Algebraic Expert System with Neural Network Concepts for Cyber, Big Data and Data Migration |

Publication Type | Conference Paper |

Year of Publication | 2019 |

Authors | Rudd-Orthner, Richard N M, Mihaylova, Lyudmilla |

Conference Name | 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) |

Date Published | dec |

Keywords | algebraic expert system, artificial intelligence, Bayes methods, Bayesian probability tree approach, Big Data, Cognition, Cyber database, data handling, data migration, data migration applications, expert systems, Expert Systems and Security, False Data Detection, grading decisions, Human Behavior, information assurance, knowledge based systems, knowledge-base organization, Knowledgebase, learning (artificial intelligence), machine assistance approach, machine learning experts system, mathematical algebraic form, neural nets, Neural Network, neural network computational graph structure, neural network node structure, Neural networks, output layers, pubcrawl, Relational Database, relational databases, Resiliency, rule probabilities, Scalability, security, structured approach, Syntactics, trees (mathematics), value probabilities |

Abstract | This paper describes a machine assistance approach to grading decisions for values that might be missing or need validation, using a mathematical algebraic form of an Expert System, instead of the traditional textual or logic forms and builds a neural network computational graph structure. This Experts System approach is also structured into a neural network like format of: input, hidden and output layers that provide a structured approach to the knowledge-base organization, this provides a useful abstraction for reuse for data migration applications in big data, Cyber and relational databases. The approach is further enhanced with a Bayesian probability tree approach to grade the confidences of value probabilities, instead of the traditional grading of the rule probabilities, and estimates the most probable value in light of all evidence presented. This is ground work for a Machine Learning (ML) experts system approach in a form that is closer to a Neural Network node structure. |

DOI | 10.1109/ISSPIT47144.2019.9001880 |

Citation Key | rudd-orthner_algebraic_2019 |

- Relational Database
- machine learning experts system
- mathematical algebraic form
- neural nets
- neural network
- neural network computational graph structure
- neural network node structure
- Neural networks
- output layers
- pubcrawl
- machine assistance approach
- relational databases
- Resiliency
- rule probabilities
- Scalability
- security
- structured approach
- Syntactics
- trees (mathematics)
- value probabilities
- data migration applications
- algebraic expert system
- Artificial Intelligence
- Bayes methods
- Bayesian probability tree approach
- Big Data
- cognition
- Cyber database
- data handling
- data migration
- False Data Detection
- expert systems
- Expert Systems and Security
- grading decisions
- Human behavior
- Information Assurance
- knowledge based systems
- knowledge-base organization
- Knowledgebase
- learning (artificial intelligence)