"Just add data."

What is fraXses

fraXses is a federated / virtualised (physical or both) data application framework, that enables data to be used by humans and machines – not just analytics or business applications. We show business that it is possible to use data to make a difference. It is about using algorithms and machine learning to process data more quickly and seamlessly to get insights and answers when and where they are needed.

Federated Application Framework

While based on our metadata methodology the federated application framework is a system in which several databases appear to function as a single entity. Each component database in the system is completely self-sustained and functional. When an application queries the federated framework, the system figures out which of its component databases contains the data being requested and passes the request to it. This application framework uses federated databases, which can be thought of as database virtualisation.

Metadata Methodology

The fraXses metadata methodology uses an application framework to provide a repository that captures, manages and publishes rich metadata, becoming an active participant in the application. The methodology uses a metadata repository, which is both the custodian of the data and business rule definitions, which is also the enforcer of the rules. When rule definition and enforcement is the responsibility of one entity – the metadata repository – it ensures the integrity of the data is easier and application maintenance is reduced. The framework ensures that the applications are never hard coded, simply metadata driven.

Real-time Decision Making Engine

fraXses allows business to transact with live data feeds using alerts, triggers, rules, pattern recognition or other techniques to create actions. The generation of these alerts or actions based on triggers or events can be used to kick of processes or system activity. This all takes place in real-time.

Data Relationship Discovery Engine

fraXses is used to review all data sources, and designed to understand the data taxonomy, the framework and how the data links together. This is espceially useful when used across disparate data sources, where there are no joins, referential integrity or master files. The engine is driven by a set of algorithms, which create a confidence score relating to the data's validity, allowing the business to rapidly make use of the data.

Schema Discovery Engine

The schema discovery engine is designed to reduces manpower required to create schema for new data sources. It will reduce the effort (3 – 4 man days down to 4 seconds), eliminate the risk of human error, build the initial cut of metadata (also for federated design) and create the Spark environment automatically. It allows analytics/data discovery to be available immediately.