Scoring Concepts: Network Scoring
We can feed networks derived in Graph Scripting DSL into a Scoring pipeline to derive information and insight in the form of network-based scenarios. This article outlines: Concepts and approaches available for batch Network Scoring, including extracting information from graphs, testing, and debugging. Available tools and methods for performing network analysis with Assess. Read the article (login required): 2. Scoring Concepts: Network Scoring - Quantexa Community This article outlines the concepts and approaches available for batch Network Scoring, including extracting information from graphs, testing, and debugging. This article also highlights the available tools and methods for performing network analysis. The below steps are applicable for Assess only. Introduction We can feed…
New guide 📖 Scoring Concepts: Write-Once Steps
Discover how to efficiently use Write-Once Steps in the Assess framework for data transformation. This detailed guide complements the Write-Once Steps documentation and helps you determine when to apply Write-Once Steps effectively in both Batch and Dynamic Scoring contexts. Key topics covered: The potential cost of using the wrong method When to use Write-Once Steps vs. Logical Sources Strategies for scoring networks in Batch (SparkScoringContext) and Dynamic (DynamicScoringContext) environments Gain a deeper understanding of how to avoid duplicating logic across contexts and streamline your data engineering workflows. Read the full article (login required) to explore practical scenarios and best practices for scoring networks with Write-Once Steps: 5. Scoring Concepts: Write-Once Steps - Quantexa Community This article serves as an extension of the Product documentation of Write-Once Steps and provides a guide on which situation should the Write-Once Steps be used. Introduction The Write-Once Step is a data transformation step in the Assess framework, which is executable in both Batch and Dynamic contexts, meaning you do not…
Welcome to Quantexa 2.3 | 2.3.0 Release Announcement
In Quantexa 2.3, you will find: Entity Store, a new component in our set of Entity Resolution capabilities; Introduction of Security Model V2, a new way to manage Role-Based Access and Control within The Quantexa Platform; Enhancements to Assess including Path Ranking, a new way to define paths; Support for Elasticsearch 8 and Spark 3.2. Entity Store In 2.3.0, we are introducing Entity Store in beta. Before 2.3.0, all interactions with the Quantexa UI and the Quantexa Mid-tier APIs depended upon resolving Entities on-the-fly (dynamically). The Entity Store allows a persisted or materialized view of Entities to be stored in the system. In this first release, the core Entity Store will load a full set of pre-resolved Entities produced by Batch Resolver, into Elasticsearch. The Entity Store can then be used by Explorer to enable querying of the underlying entities, as a Cache for Resolver to improve performance, and to support the new Entity REST API. Security Model V2 We have introduced a new framework for authentication and authorization across the platform. This gives the platform greater control over the data and features that a user can access and enables easier integration with Identity Providers (IdPs). End-users no longer need to interact directly with low-level Quantexa Roles. They are now able to share their work with Groups that are logical to their organizational structure. For example: UK Fraud Investigators. Using a new User Management Screen, you can now provision users and Groups, collections of users that can be assigned Roles and Dynamic Privileges, to The Quantexa Platform from Identity Providers simply and easily. Path Ranking It is now possible to specify the rules for defining the relative importance of paths, a chain of Documents and Entities that connect two Nodes, when writing Network Scores with Path Ranking, which make the Network easier to analyze by signaling the most significant areas to explore further. Dual Context Sources Dual Context Sources enable scoring logic to be designed that will work in both batch and dynamic, and provides a config-based tool that generates Source steps for both pipelines. This simplifies the deployment of a build with a dual architecture. Support for Elasticsearch and Spark Quantexa now supports Elasticsearch 8 across the platform, except for Offline Indexers and Spark 3.2 is now supported across the platform. Warning: Spark 3.0 is deprecated and support will be removed completely in the next release of the platform. Spring Boot upgrade Spring Boot has been upgraded to version 2.6.8. This results in faster startup times and solves a number of security vulnerabilities found in older versions of Spring Boot. Documentation Site Glossary We have completely revamped and reworked our Documentation site glossary, adding almost 100 new terms. Explore the Glossary to find out more. Other highlights For more control and flexibility when running ETL, Quantexa has has added the ability to generate Resolver Search Loader and Compound Creator scripts at the point where users define a Root Model in Data Fusion. List inputs have been introduced for Entity Attribute functions, to allow for more flexibility and customizability, granting users more ways to define Entity Attributes in Data Fusion, and cater to a wider range of data models. You are now able to collapse the Query Builder in Explorer. This is the next step in improving the user experience of Explorer, allowing users to focus on the results of the query. You will find the full set of Release Notes on the Quantexa Documentation site. If you are unable to access them, you will need to get a user with access to submit a Documentation site access request through the Quantexa Support Portal.
Welcome to Quantexa 2.5 | 2.5.0 Release Announcement
We are pleased to announce Quantexa 2.5! Read below for a few exciting highlights. Stream Updates to the Entity Store The Entity Store can now be updated with a streamed ingest of new Documents, and creates a change log describing how the Entity population has changed. In combination with the ability to query the full population of resolved Entities, this update enables near real-time access to the most up-to-date view of your data. Why is this important? Users and downstream systems can now get the latest view of an Entity resolved by Quantexa, enabling business processes that need to access or act upon that update in near real-time - in a Master Data Management solution (MDM), for example - to do so. Deploy and Change Custom Scoring Pipelines through Configuration The addition of Scoring Extension Mode to the Assess Template Generation functionality helps simplify the process of setting up Scoring on a project. With the new Extension Mode, it is now possible to change and add new nodes to a custom Scoring model using configuration files. Why is this important? This significantly reduces the technical skills required for setting up the Scoring pipeline and makes the generation of a custom Scoring pipeline more flexible. Flexible Scorecards: Identify and Aggregate Insights Across Multiple Typologies Assess capabilities and helper functions have been added to support multiple Scorecards on the same level within one Scoring pipeline. Scores can contribute to one or more Scorecards and flexible alerting logic can be set up to include the outcome of multiple Scorecards. Why is this important? This enables the flagging of different types of risks or insights at the same level - for different typologies or different products, for example - and helps an investigator or analyst understand and act upon the full context of the data available. Tune Scorecards with QPython QPython utilities can now interact with Assess configuration files and Scoring pipelines, which simplifies the process of tuning a Scoring model, providing an easy way to conduct a what-if analysis. The interface, using pre-written Python Notebooks, calculates a set of the most common and useful metrics for tuning. The use of Jupyter Notebooks allows the export of the reports for inclusion in the wider model governance process. Why is this important? Scorecard tuning is one of the most important parts of model development, as the model must output the most relevant (e.g., risky or interesting) information for a specific use case. These utilities make the process of Scorecard tuning much more accessible, simple, and quick. It also makes sure that the model addresses the business need and risk appetite of the deployment. Other highlights Speed up and simplify deployment of Explorer with no-code configuration; Merge and split Entities, and override Entity Attributes through the Entity Management Panel (e.g., for Master Data Management solutions); Integrate with Kafka streaming typologies more easily. Share your thoughts on this release in our poll: Which feature of Quantexa 2.5 are you most excited for? To receive updates on every release, be sure to follow the Release Announcements topic: Want to learn more? Check out the Release Notes on the Quantexa Documentation site. If you are unable to access the Documentation site, please get in touch with your Quantexa point of contact or the Community team at community@quantexa.com.
