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    For subscription companies searching for a failed payment recovery solution to grow revenue, and retain customers.

    FlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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    AestheticsPro Medical Spa Software

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  • 1
    TRAK Viewpoints

    TRAK Viewpoints

    Specifications for TRAK architecture views

    The architecture viewpoints (specifications for architecture views iaw ISO 42010) for TRAK. TRAK is a general systems-thinkers'/system engineering enterprise architecture framework. It is simple, user-friendly, pragmatic and not limited to IT. 100% triple-centric and semantically-sound. Defines a total of 24 viewpoints. The ones needed are selected by taking the task sponsor's concerns and matching them to the typical concerns that each TRAK viewpoint addresses. The triples that address each concern are defined in the TRAK metamodel (https://sf.net/p/trakmetamodel). The mapping between metamodel triple and architecture viewpoint is held with a Neo4J graph model (https://doi.org/10.1002/eng2.12168) and defined using (more) architecture viewpoints (https://www.researchgate.net/publication/335176248_Architecture_Description_Viewpoints_Metamodel_Description_Implementation_and_Model_Changes). The minimal process is defined in the overall TRAK specification (https://sf.net/p/trak)
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  • 2
    Testcontainers node

    Testcontainers node

    Supports tests, providing lightweight, throwaway instances of database

    Testcontainers is an open source library for providing throwaway, lightweight instances of databases, message brokers, web browsers, or just about anything that can run in a Docker container. No more need for mocks or complicated environment configurations. Define your test dependencies as code, then simply run your tests and containers will be created and then deleted. With support for many languages and testing frameworks, all you need is Docker. Use a containerized instance of your database to test your data access layer code for complete compatibility, without requiring a complex setup on developer machines. Trust that your tests will always start with a known state. Use containerized web browsers, compatible with Selenium, to run automated UI tests. Each test gets a fresh, clean instance of the browser, without having to worry about variations in plugins or required updates.
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  • 3
    Wikipedia Concept Association Map (WCAM) is new approach for textual knowledge representation and understanding. All concepts and associations are stored in a graph database for better performance and easy distribution.
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  • 4

    cog

    A pure python graph database

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  • 5

    cosd_java

    A Tool for Searching and Demonstrating Code Structure

    CoSD is a tool for searching and demonstrating code structure based on graph database. Since some commercial code is involved in the industrial version of CoSD , we just give out a baseline version of it. We also provide a quick demo for this baseline version. To use CoSD, please read the file readme.txt at first.
    Downloads: 0 This Week
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  • 6
    falkordb

    falkordb

    A super fast Graph Database

    A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG).
    Downloads: 0 This Week
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  • 7
    prOOst - wisdom through provenance
    prOOst is a provenance storing system, which allows users to store their provenance information in OPM notation. It is based on a graph database and can be accessed for querying and adding of information through a REST interface.
    Downloads: 0 This Week
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  • 8

    reco4j-core

    Reco4j is a graph-based recommendation engine

    Reco4j is an open source project aims at developing a recommendation framework based on graph data sources. We choose graph databases for several reasons. They are NoSQL databases, so "schemaless". This means that it is possible to extend the basic data structure with intermediate information, i.e. similarity value between item and so on. Moreover, since every information is expressed with properties, nodes and relations, the recommendation process can be customized to work on every graph. Reco4j can be used on every graph where "user" and "item" is represented by node and the preferences are modelled as relationship between them. Current implementation leverage on Neo4j as first graph database integrated in our framework.
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  • 9
    An intranet engine with an underlying graph database
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  • 10
    StrixDB is a RDF store supporting SPARQL, SPARQL/Update and SPARQL/Protocol. This RDF graph database could be used standalone (as a Lua Module) or with httpd (Apache Web Server). Provides inference capabilities through Datalog rules.
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