Metadata-Version: 2.1
Name: nist-nestor
Version: 0.3
Summary: Quantifying tacit human knowledge for Smart Manufacturing Maintenance,                        for maintnenance-based investigatory analysis
Home-page: https://github.com/usnistgov/nestor/
Author: Thurston Sexton
Author-email: thurston.sexton@nist.gov
License: UNKNOWN
Description: 
        ## Purpose
        
        This application was designed to help manufacturers "tag" their
        maintenance work-order data according to [the methods being researched](https://www.researchgate.net/project/Knowledge-Extraction-and-Application-for-Smart-Manufacturing) by the Knowledge Extraction and Applications project at the NIST Engineering Laboratory. The goal of this
        application is to give understanding to data sets that previously were
        too unstructured or filled with jargon to analyze. The current build is
        in very early alpha, so please be patient in using this application. If
        you have any questions, please do not hesitate to contact us (see [Who
        are we?](#who-are-we). )
        
        ### Why?
        
        There is often a large amount of maintenance data *already* available
        for use in Smart Manufacturing systems, but in a currently-unusable
        form: service tickets and maintenance work orders (MWOs). **Nestor** is
        a toolkit for using Natural Language Processing (NLP) with efficient
        user-interaction to perform structured data extraction with minimal
        annotation time-cost.
        
        ### Features
        
        
        -   Ranks concepts to be annotated by importance, to save you time
        -   Suggests term unification by similarity, for you to quickly review
        -   Basic concept relationships builder, to assist assembling problem
            code and taxonomy definitions
        -   Strucutred data output as tags, whether in readable (comma-sep) or
            computation-friendly (sparse-mat) form.
        
        ### What's Inside?
        
        Documentation is contained in the /docs subdirectory, and are hosted as
        webpages and
        [PDF](https://media.readthedocs.org/pdf/nestor/latest/nestor.pdf)
        available at [readthedocs.io](https://nestor.readthedocs.io/en/latest/)
        .
        
        Current:
        
        -   Tagging Tool: Human-in-the-loop Annotation Interface (pyqt)
        -   Unstructured data processing toolkit (sklearn-style)
        -   Vizualization tools for tagged MWOs-style data (under development)
        
        Planned/underway:
        
        -   KPI creation and visualization suite
        -   Machine-assisted functional taxonomy generation
        -   Quantitative skill assement and training suggestion engine
        -   Graph Database creation assistance and query tool
        
        ### Pre-requisites
        
        This package was built as compatible with Anaconda python distribution.
        See our [default requirements file](https://github.com/usnistgov/nestor/blob/master/requirements/defaults.txt) for a complete list of major dependencies, along with the requirements to run our [experimental dashboard](https://github.com/usnistgov/nestor/blob/master/requirements/dash.txt) or to [compile our documentation locally](https://github.com/usnistgov/nestor/blob/master/requirements/docs.txt)
        
        
        ## Who are we?
        
        
        This toolkit is a part of the Knowledge Extraction and Application for
        Smart Manufacturing (KEA) project, within the Systems Integration
        Division at NIST.
        
        ### Points of Contact
        
        -   [Michael Brundage](https://www.nist.gov/people/michael-p-brundage)
            Principal Investigator
        -   [Thurston Sexton](https://www.nist.gov/people/thurston-sexton) Nestor Technical Lead
        
        ### Contributors:
        Name             |   GitHub Handle
        ---              |   ---
        Thurston Sexton  |   [@tbsexton](https://github.com/tbsexton)
        Sascha Moccozet  |   [@saschaMoccozet](https://github.com/saschaMoccozet)
        Michael Brundage |   [@MichaelPBrundage](https://github.com/MichaelPBrundage)
        Madhusudanan N.  |   [@msngit](https://github.com/msngit)
        Emily Hastings   |   [@emhastings](https://github.com/emhastings)
        Lela Bones       |   [@lelatbones](https://github.com/lelatbones)
        
        ### Why KEA?
        
        The KEA project seeks to better frame data collection and transformation
        systems within smart manufacturing as *collaborations* between human
        experts and the machines they partner with, to more efficiently utilize
        the digital and human resources available to manufacturers. Kea (*nestor
        notabilis*) on the other hand, are the world's only alpine parrots,
        finding their home on the southern Island of NZ. Known for their
        intelligence and ability to solve puzzles through the use of tools, they
        will often work together to reach their goals, which is especially
        important in their harsh, mountainous habitat.
        
Keywords: nlp smart manufacturing maintenance tag app
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Manufacturing
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 3
Classifier: License :: Public Domain
Provides-Extra: dash
Provides-Extra: all
Provides-Extra: docs
