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The focus of the article is on comparing documents that can be stored on Windows File System in one respect and in the other respect their comparative analysis that can be performed with Semantic Search in SQL Server.
Additionally, the readers will learn how to store unstructured data by exploring File Table and creating MS Word documents on the fly (instantly) to be consumed by Semantic Search.
This part of the article is related to the use of Semantic Search on unstructured data for the extraction of basic level business-crucial information provided standard naming is in place.
The readers need to go through **Learn to Store and Analyse Documents on Windows File System with SQL Server Semantic Search Part 1 **to proceed further with it.
This article assumes the readers are familiar with most (if not all) of the following:
This article also assumes that following steps have already been completed.
Please refer to the article Learn to Store and Analyse Documents on Windows File System with SQL Server Semantic Search Part 1 to complete any of the above missing steps, which are mandatory to cover the walkthrough completely.
As we are preparing to perform documents comparative analysis based on their standard naming, at this point it is worth doing a quick check by querying the FILESTREAM Enabled database we set up in the first part of the article.
Run the following script against EmployeesFilestreamSample database:
-- View stored documents managed by File Table to check
SELECT stream_id
,[name]
,file_type
,creation_time
FROM EmployeesFilestreamSample.dbo.EmployeesDocumentStore
#sql server #full-text search #semantic search #sql server #sql server 2016
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SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
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In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.
Fig. 1 System Databases
There are five system databases, these databases are created while installing SQL Server.
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We know Humans learn from their past experiences. Mean while Machines follow Instructions given by Humans. But what if Human can train Machines to learn from the past data?. In simple, this is what Machine learning is !!!. SQL Server has capabilities of Machine Learning. In this article, we will discuss about the capabilities of Machine Learning in SQL Server.
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Being the final part of the article, it is going to take you to the next level of analyzing word documents stored in Windows folders, managed by File Table, and consumed by Semantic Search.
Additionally, the readers are going to gain more understanding of Semantic Search and how to make it work with MS Word documents for analysis.
This article provides a name-based analysis of the documents with equal attention to both theory and practice.
This article assumes the following:
I suggest that you go through the following articles to fill any gaps in your understanding of the above prerequisites and to implement the walkthrough(s) in the final part of the article:
In the first part of the series we learned about the following things:
In the second part of the article we implemented the following things:
#sql server #full-text search #semantic search #sql server #sql server 2016