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|[http://grooper.bisok.com/Documentation/2.80/Main/HTML5/index.htm#t=Start_Page.htm 2.80 Reference Documentation]
|[https://grooper.bisok.com/Documentation/2.90/Main/HTML5/index.htm#t=Start_Page.htm 2.90 Reference Documentation]
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<blockquote style="font-size:14pt">
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'''[[Database Export]]'''
[[Data Context]]
</blockquote>
</blockquote>


[[File:Database_export_002.png|thumb]]
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”. 


Database Export is one of the main ways to '''[[Five Phases of Grooper|Deliver]]''' data '''[[Five Phases of Grooper|Collected]]''' in Grooper.
This allows us to build an extraction logic using '''[[Data Type]]''' and '''[[Field Class]]''' extractors in order to build and populate a '''[[Data Model]]'''.


A completed '''[[Content Model]]''' and accompanying '''[[Batch]]''' for what will be built can be found '''[[Media:Database Export.zip|here]]'''. It is not required to download to understand this article, but can be helpful because it can be used to follow along with the content of this article. ''This file was exported from and meant for use in Grooper 2.9''
There are three fundamental data context relationships:


The most important goal of '''Grooper''' is to deliver accurate data to line of business systems that allow the information to be integrated into impactful business decisioning. [https://en.wikipedia.org/wiki/Table_(information) Tables] in [https://en.wikipedia.org/wiki/Database databases] remain, to this day, one of the main vessels by which this information is stored. '''Grooper's''' '''Database Export''' activity is the mechanism by which this delivery is performed. '''Database Export''' uses a configured '''[[Data Connection]]''' to establish a link to ('''[https://en.wikipedia.org/wiki/Microsoft_SQL_Server Microsoft SQL Server]''' or '''[https://en.wikipedia.org/wiki/Open_Database_Connectivity ODBC-compliant]''') tables in a database and intelligently populate said tables.</p>
* '''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.


Two ''key distinctions'' about '''Grooper's''' '''Database Export''' activity are its ability to take full advantage of its sophisticated hierarchical data modeling to flatten complex/inherited data structures, and the ease of delivery to multiple tables at once.
Using the context these relationships provide allows us to understand how to target data with extractors.
 
For more information on Database Export, visit the full wiki artcile [[Database Export|here]].
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[[Database Lookup]]s changed in version 2.80Prior to Version 2.80, database lookups were performed on individual Data Fields in a Data Model, using simple field mappings.  
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 providesThese properties give you more control when it comes to what confidence a result ''should'' be.


Now, lookups are configured on a Data Model, Data Section or Data Table’s properties, using SQL queries. Other improvements include:
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.
*Multiple database lookups using multiple SQL queries can be written on the Data Model.
*Lookups can reference any number of database columns and Grooper fields.
*During review, Grooper now provides a UI dialogue box when multiple matches are returned to choose the correct value.


Visit the [[Database Lookup]] article for more information.
For more information visit, the [[Confidence Multiplier and Output Confidence]] article.
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|style="text-align:center"|'''[[Output Extractor Key]]'''
|style="text-align:center"|'''[[Output Extractor Key]]'''
|style="text-align:center"|'''[[Box (CMIS Binding)]]'''
|style="text-align:center"|'''[[Box (CMIS Binding)]]'''
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|colspan="4"|[[Image:Linq_to_grooper_objects_001.png|center|200px|link=LINQ to Grooper Objects]]
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|colspan="4" style="text-align:center"|'''[[LINQ to Grooper Objects]]'''
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[[File:Olers-insurance-document-capture-service-case-study-grooper.jpg|thumb]]
[[File:American-airlines-credit-union-financial-services-document-data-capture-integration-grooper.jpg|400px|right|link=https://www.bisok.com/case-studies/electronic-data-discovery-case-study/]]


<blockquote style="font-size:14pt">
<blockquote style="font-size:14pt">
'''Empowering Faster and Safer Services for Thousands of Public Servants'''
'''They’re Saving Over 5,000 Hours Every Year in Data Discovery and Processing'''
</blockquote>
</blockquote>




With over 100 years of public servant records on disks or paper files, the Oklahoma Law Enforcement Retirement System needed a new modern system to protect private information and streamline daily workflows.
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.
 
In 1947, Oklahoma Senate Bill 125 created a Death, Disability and Retirement
Fund for Department of Public Safety members. In 1980, a new bill
established the Oklahoma Law Enforcement Retirement System (OLERS)
which continued the previous plan and expanded upon it to include
members of other law enforcement agencies.


Currently, OLERS provides retirement funds to 11 different statewide law
Discover how they:
enforcement agencies.


Key Outcomes:
* Quickly found 40,000 specific files among one billion
*Saving Hundreds of Hours Annually with Modern Document Management
* Easily integrated with data silos and content management systems when no other solution would
*Monthly Capture Service Provides Additional Time Savings
* Have cut their mortgage processing time in half (and they process mortgages for 47 branch offices!)
*Many Layers of Personal InformationProtection
* Learn from the document and electronic data discovery experts at BIS!
*Redaction of At-Risk Data


[https://www.bisok.com/case-studies/empowering-faster-and-safer-services-for-thousands-of-public-servants/ You can access the full case study clicking this link].
[https://www.bisok.com/case-studies/electronic-data-discovery-case-study/ You can access the full case study clicking this link].
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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]
|style="width:25%"|
|style="width:25%"|
* [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