Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • DataBuck Reviews & Ratings
    6 Ratings
    Company Website
  • Google Cloud SQL Reviews & Ratings
    553 Ratings
    Company Website
  • Windocks Reviews & Ratings
    7 Ratings
    Company Website
  • DbVisualizer Reviews & Ratings
    561 Ratings
    Company Website
  • RaimaDB Reviews & Ratings
    12 Ratings
    Company Website
  • Oxylabs Reviews & Ratings
    1,151 Ratings
    Company Website
  • Planfix Reviews & Ratings
    58 Ratings
    Company Website
  • ALMobile Reviews & Ratings
    29 Ratings
    Company Website
  • BrandMap® 10 Reviews & Ratings
    Company Website
  • Odoo Reviews & Ratings
    1,641 Ratings
    Company Website

What is QDeFuZZiner?

In the QDeFuZZiner application, the core organizational element is termed a project, which includes the definitions of two datasets intended for import and analysis, identified as the "left dataset" and the "right dataset." Each project contains these datasets along with a variable number of solutions that outline the procedures for performing fuzzy match analysis. When a project is created, it is assigned a unique project tag that is then added to the names of the input tables during the raw data importation process. This tagging mechanism ensures the distinctiveness of the imported tables by linking them to their corresponding project names. Additionally, throughout the import process and in subsequent steps for generating and implementing solutions, QDeFuZZiner constructs several indexes within the PostgreSQL database, which improves the effectiveness of fuzzy data matching tasks. The datasets can originate from various spreadsheet formats such as .xlsx, .xls, .ods, or from CSV (comma-separated values) files, which are uploaded to the server database, facilitating the creation, indexing, and processing of the associated left and right database tables. This organized methodology not only enhances data management but also optimizes the analytical process, allowing users to efficiently extract valuable insights from their datasets. Ultimately, this design aims to provide a seamless experience for users, ensuring that they can easily navigate complex data environments.

What is NetOwl NameMatcher?

NetOwl NameMatcher, celebrated for its superior performance in the MITRE Multicultural Name Matching Challenge, offers exceptional accuracy, rapid processing, and scalability in its name matching solutions. Utilizing a cutting-edge machine learning framework, NetOwl adeptly addresses the complex challenges associated with fuzzy name matching. Traditional techniques like Soundex, edit distance, and rule-based systems frequently struggle with precision, leading to an abundance of false positives, and recall issues that result in false negatives, particularly when faced with the varied fuzzy name matching scenarios mentioned earlier. In contrast, NetOwl adopts a data-driven, machine learning-based probabilistic approach to overcome these name matching challenges effectively. It autonomously develops advanced, probabilistic name matching rules from vast real-world datasets containing multi-ethnic name variants. Additionally, NetOwl implements specialized matching models designed for different entity types, including individuals, organizations, and geographical locations. To enhance its functionality, NetOwl incorporates automatic detection of name ethnicity, which significantly boosts its adaptability to the complexities inherent in multicultural name matching. This holistic strategy not only elevates accuracy but also ensures dependable performance across a wide array of applications. Consequently, organizations relying on precise name matching can greatly benefit from the innovative solutions provided by NetOwl.

Media

Media

Integrations Supported

ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
PostgreSQL
SolrCommerce
Tableau

Integrations Supported

ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
PostgreSQL
SolrCommerce
Tableau

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

QDeFuZZiner

Company Website

zmatasoft.wixsite.com/qdefuzziner/qdefuzziner-software-features

Company Facts

Organization Name

NetOwl

Date Founded

1996

Company Location

United States

Company Website

www.netowl.com/name-matching-software

Categories and Features

Categories and Features

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Popular Alternatives

Popular Alternatives

Lead to Account Matcher Reviews & Ratings

Lead to Account Matcher

Eustace Consulting