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features
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Description

Legislative measures dictate the conditions under which information can be disseminated and the scenarios that justify such sharing. To enhance the efficiency of information exchange among agencies during investigations of particular cases, data matching and discovery techniques are employed. At the core of the Kalinda system lies an advanced machine-learning algorithm designed to correlate individual records across various agencies by analyzing personal traits such as name and date of birth, as well as the characteristics of associated individuals. Thorough investigations frequently necessitate examining connections between individuals or locations, especially when only incomplete data is available. Kalinda is equipped to handle queries involving partial matches related to individuals, locations, and their interrelations. Additionally, it provides sophisticated algorithms that enable the discovery of records that bear resemblance to the matched ones by utilizing probabilistic record matching methods. This capability significantly broadens the scope of potential leads in investigations, making Kalinda an invaluable tool for law enforcement and investigative agencies.

Description

In QDeFuZZiner software, the fundamental unit is referred to as a project, which encompasses the definitions of two source datasets for import and analysis, known as the "left dataset" and "right dataset." Each project not only includes these datasets but also a variable number of solutions that detail the methodology for conducting fuzzy match analysis. Upon creation, every project is assigned a distinct project tag, which is subsequently appended to the names of the corresponding input tables during the raw data import process. This tagging system guarantees that the imported tables maintain uniqueness through association with their respective project names. Furthermore, during the import phase and later when generating and executing solutions, QDeFuZZiner establishes various indexes on the PostgreSQL database, thereby enhancing the efficiency of fuzzy data matching procedures. The datasets themselves can be sourced from spreadsheet formats such as .xlsx, .xls, .ods, or from CSV (comma separated values) flat files, which are uploaded to the server database, leading to the creation, indexing, and processing of the associated left and right database tables. This structured approach not only simplifies data management but also streamlines the analysis process, making it easier for users to derive insights from their datasets.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

PostgreSQL No 

Integrations

PostgreSQL Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Kalinda

Country

Australia

Website

www.factil.io/products/kalinda/

Vendor Details

Company Name

QDeFuZZiner

Website

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

Product Features

Government

Budgeting & Forecasting No 
Code Enforcement No 
Compliance Management No 
Fixed Asset Management No 
Inventory Management No 
License Issuance No 
Permit Issuance No 
Purchasing & Receiving No 
Self Service Portal No 
Taxation & Assessment No 
Utility Billing No 
Work Order Management No 

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