SchemaMerger

by Lonko Java Version: v1.0 License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | SchemaMerger Summary

SchemaMerger is a Java library. SchemaMerger has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

SchemaMerger

Support

Quality

Security

License

Reuse

Support

SchemaMerger has a low active ecosystem.

It has 1 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

SchemaMerger has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SchemaMerger is v1.0

Quality

SchemaMerger has no bugs reported.

Security

SchemaMerger has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

SchemaMerger does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

SchemaMerger releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

Top functions reviewed by kandi - BETA

kandi has reviewed SchemaMerger and discovered the below as its top functions. This is intended to give you an instant insight into SchemaMerger implemented functionality, and help decide if they suit your requirements.

Fetch all products for a given category
Extract the domain from the URL
Returns the head threshold value
Computes the sample sampling
Region > uploads
This method converts a SourcePage to a Document
Uploads a collection of products to the database
Converts a catalog page into a document object
Sample product pages from a category
Helper to convert a Document to a SourcePage
Compares this source product to another
Compares this product to another product
Creates a 64 - bit hash code for the website
Creates a hashCode of this map
Compares this token to another
Converts the CSV into a CSV format
Converts the CSV file to a list of CSV strings
Returns the columns in CSV format
Compares this attribute to another
Calculates the head value that contains the tail of the sample
Generate a percentage percentage based on the given string and percentage values
Read a training set from a TS file
Compares this tuple for equality
Fetch all pages of a given list of links that contain a specific attribute
Get a list of products from a collection that link to a given category
Launch the datasetAlignment algorithm

Get all kandi verified functions for this library.

SchemaMerger Key Features

No Key Features are available at this moment for SchemaMerger.

SchemaMerger Examples and Code Snippets

No Code Snippets are available at this moment for SchemaMerger.

Community Discussions

No Community Discussions are available at this moment for SchemaMerger.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SchemaMerger

Premise: results of Agrawal depends on order with which it computes each sources. If they are provided in order of linkage descendent, its results should be better. The problem is that an efficient way for calculating this order has not been implemented yet. For this reason this order should be provided in some way. Parameters: Run parameters should provide a token PROD, TEST or CUSTOM, so that config_prod.json, config_test.json or config_custom.json will be used (custom is in gitignore).
launchers.SyntheticDatasetGenerator#main --> generate Synthetic dataset and add to Mongo
launchers.Cohordinator#main --> Launch Agrawal algorithm. 2 possibilities (depends on user input y/n): Launch on real dataset --> order of sources should be provided as input (currently there is a constant in Cohordinator Launch on synthetic dataset --> launches the S.D. generation, then Agrawal. This cannot be done currently on different steps, as SD object keeps source order in an instance variable.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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