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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
NetOwl NameMatcher, recognized for its excellence in the MITRE Multicultural Name Matching Challenge, delivers unparalleled accuracy, speed, and scalability in name matching solutions. By employing an innovative machine learning framework, NetOwl effectively tackles the intricate challenges of fuzzy name matching. Conventional methods like Soundex, edit distance, and rule-based systems often face significant issues with precision, leading to false positives, and recall, resulting in false negatives, when confronting the diverse fuzzy name matching scenarios outlined previously. In contrast, NetOwl leverages a data-driven, machine learning-based probabilistic strategy to address these name matching difficulties. It automatically generates sophisticated, probabilistic name matching rules from extensive, real-world multi-ethnic name variant datasets. Furthermore, NetOwl employs distinct matching models tailored to various entity types, such as individuals, organizations, and locations. To add to its capabilities, NetOwl also integrates automatic detection of name ethnicity, enhancing its adaptability to the complexities of multicultural name matching. This comprehensive approach ensures a higher level of accuracy and reliability in diverse applications.
API Access
Has API
API Access
Has API
Integrations
ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
Tableau
Integrations
ArcGIS
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
Tableau
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
NetOwl
Founded
1996
Country
United States
Website
www.netowl.com/name-matching-software
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
Product Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management