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Poneglyph is an optimized distributed advanced analytics as a service framework designed to be highly efficient, flexible and scalable. It implements machine learning algorithms such as UMAP and Gaussian Mixture Models to yield insights and shed light to business, industry and scinentific questions.

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Poneglyph is an optimized distributed advanced analytics as a service framework designed to be highly efficient, flexible and scalable. It implements machine learning algorithms such as UMAP and Gaussian Mixture Models to yield insights and shed light on business, industry and scientific questions.

Features

Application Structure:

Directory Structure

.
├── app
│   ├── data
│   │   ├── raw
│   │   │   └── dados_municipais.csv
│   │   └── transformed
│   │       ├── clustered_dataset
│   │       ├── dados_limpos.csv
│   │       └── UMAP_embeddings.csv
│   ├── index.html
│   ├── rsconnect
│   │   └── shinyapps.io
│   │       └── jailson-rodrigues
│   │           └── Cluster-test.dcf
│   ├── server.R
│   ├── src
│   │   └── R
│   │       ├── libs.R
│   │       ├── modules
│   │       │   ├── inputs.R
│   │       │   └── outputs.R
│   │       ├── multiClassSummary.R
│   │       └── MyGgthemes.R
│   ├── ui.R
│   └── www
│       ├── alert.js
│       └── style.css
├── appscreen.png
├── data
│   ├── raw
│   │   └── dados_municipais.csv
│   └── transformed
│       ├── clustered_dataset
│       ├── dados_limpos.csv
│       └── UMAP_embeddings.csv
├── README.md
├── src
│   ├── clustering.py
│   ├── Dockerfile
│   ├── ranking.py
│   └── sankey.R
└── UMAP-GMM.ipynb



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Poneglyph is an optimized distributed advanced analytics as a service framework designed to be highly efficient, flexible and scalable. It implements machine learning algorithms such as UMAP and Gaussian Mixture Models to yield insights and shed light to business, industry and scinentific questions.

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