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'''[[CMIS Lookup]]'''
[[Data Context]]
</blockquote>
</blockquote>


[[File:cmis_lookup_002.png|right|link=CMIS Lookup]]
Data without context is meaningless. Context is critical to understanding and modeling the relationships between pieces of information on a document.  Without context, it’s impossible to distinguish one data element from another.  Context helps us understand what data refers to or “means”. 


<blockquote style="font-size:14pt">
This allows us to build an extraction logic using '''[[Data Type]]''' and '''[[Field Class]]''' extractors in order to build and populate a '''[[Data Model]]'''. 
Performing data lookups on '''[https://en.wikipedia.org/wiki/Content_Management_Interoperability_Services CMIS]''' sources like '''[https://en.wikipedia.org/wiki/SharePoint SharePoint]''' can be extremely powerful in your data integration endeavors.</blockquote>
 
There are three fundamental data context relationships:
 
* '''Syntactic''' - Context given by the syntax of data.
* '''Semantic''' - Context given by the lexical content associated with the data.
* '''Spatial''' - Context given by where the data exists on the page, in relationship with other data.


'''Grooper''' uses the CMIS protocol to connect to a variety of '''[https://en.wikipedia.org/wiki/Content_management_system Content Management Systems.]''' This connection can be used to integrate data in powerful ways by allowing the collection of one (or sometimes many) fields of information within a model in '''Grooper''' (let's call it Field A), then leveraging that extracted data point against the column of like information in the Content Management system (called Column A) to then pass back other desired fields from other columns in the same row of information.
Using the context these relationships provide allows us to understand how to target data with extractors.
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Grooper re-envisioned how to connect to CMIS sources and other storage platforms in version 2.72.  Prior to this version, Grooper users connected to various storage systems through a variety of Import and Export Providers.  In version 2.72, we created a unified framework to connect to data sources with our [[CMIS+]] Architecture!
You can now manually manipulate the confidence of an extraction result.  The '''''[[Confidence Multiplier and Output Confidence]]''''' properties of '''[[Data Type]]''' and '''[[Data Format]]''' extractors allow you to change the confidence score of extraction results.  No longer are you forced to accept the score Grooper provides.  These properties give you more control when it comes to what confidence a result ''should'' be.
 
This allows you to prioritize certain results over others.  You can create a kind of "fall back" or "safety net" result by using this property.  You can even ''increase'' the confidence of an extractor's result, allowing you to give more weight to a fuzzy extractor's result over a non-fuzzy one, for example.


This new architecture allows for easier integration of CMIS systems with Grooper, as well as exposing non-CMIS sources, such as the Windows file system and Microsoft Outlook inboxes, as if they were CMIS sources.  This provided a more standardized import/export workflow across a variety of storage systems, increased ability to map document metadata on import and export, better ability to search, query and filter document repositories, and faster integration of new content management systems, such as the new [[Box (CMIS Binding)|Box.com]] integration.
For more information visit, the [[Confidence Multiplier and Output Confidence]] article.
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* [http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
* [http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
* [http://grooper.bisok.com/Documentation/2.80/SDK/HTML5/index.htm#t=Developer_Reference.htm 2.80 SDK Documentation]
* [http://grooper.bisok.com/Documentation/2.80/SDK/HTML5/index.htm#t=Developer_Reference.htm 2.80 SDK Documentation]
* [https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
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* [https://blog.bisok.com/webinars Webinars and Video]
* [https://blog.bisok.com/webinars Webinars and Video]

Revision as of 10:02, 23 September 2020

Getting Started

Grooper is a software application that helps organizations innovate workflows by integrating difficult data.

Grooper empowers rapid innovation for organizations processing and integrating large quantities of difficult data. Created by a team of courageous developers frustrated by limitations in existing solutions, Grooper is an intelligent document and digital data integration platform. Grooper combines patented and sophisticated image processing, capture technology, machine learning, and natural language processing. Grooper – intelligent document processing; limitless, template-free data integration.

Getting Started
Install and Setup
2.90 Reference Documentation


Featured Articles Did you know?

Data Context

Data without context is meaningless. Context is critical to understanding and modeling the relationships between pieces of information on a document. Without context, it’s impossible to distinguish one data element from another. Context helps us understand what data refers to or “means”.

This allows us to build an extraction logic using Data Type and Field Class extractors in order to build and populate a Data Model.

There are three fundamental data context relationships:

  • Syntactic - Context given by the syntax of data.
  • Semantic - Context given by the lexical content associated with the data.
  • Spatial - Context given by where the data exists on the page, in relationship with other data.

Using the context these relationships provide allows us to understand how to target data with extractors.

You can now manually manipulate the confidence of an extraction result. The Confidence Multiplier and Output Confidence properties of Data Type and Data Format extractors allow you to change the confidence score of extraction results. No longer are you forced to accept the score Grooper provides. These properties give you more control when it comes to what confidence a result should be.

This allows you to prioritize certain results over others. You can create a kind of "fall back" or "safety net" result by using this property. You can even increase the confidence of an extractor's result, allowing you to give more weight to a fuzzy extractor's result over a non-fuzzy one, for example.

For more information visit, the Confidence Multiplier and Output Confidence article.

New in 2.9 Featured Use Case

Welcome to Grooper 2.9!
Below you will find helpful links to all the articles about the new/changed functionality in this version of Grooper.
Compile Stats Microsoft Office Integration Document Viewer Separation and Separation Review
Data Review Confidence Multiplier Data Element Overrides Database Export
CMIS Lookup Content Type Filter Output Extractor Key Box (CMIS Binding)
LINQ to Grooper Objects

They’re Saving Over 5,000 Hours Every Year in Data Discovery and Processing


American Airlines Credit Union has transformed their data workflows, quickly saving thousands of hours in electronic data discovery , resulting in much greater efficiency and improved member services.

Discover how they:

  • Quickly found 40,000 specific files among one billion
  • Easily integrated with data silos and content management systems when no other solution would
  • Have cut their mortgage processing time in half (and they process mortgages for 47 branch offices!)
  • Learn from the document and electronic data discovery experts at BIS!

You can access the full case study clicking this link.


Other Resources


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