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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
API Access
Has API
Integrations
PostgreSQL
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
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
Code Enforcement
Compliance Management
Fixed Asset Management
Inventory Management
License Issuance
Permit Issuance
Purchasing & Receiving
Self Service Portal
Taxation & Assessment
Utility Billing
Work Order Management