Self-learning data platform…

The Dynizer is the only database to integrate structured and unstructured data so it can be queried the same way. It’s the only database to automatically discover the data structure in unstructuered text. And it’s the only database to combine semantic abstraction and semantic context. It’s this combination that makes the Dynizer so versatile.

Where the structure and nature of data changes over time, certain traditional databases and data warehouses become obsolete. The Dynizer solves this issue because the model remains flexible.

New data adds to the strength of the analytics as new data automatically connects to existing elements, often leading to unexpected enhancements in the data set.

No programming or SQL-statements are needed to create Actions or Topologies and data elements can be used multiple times without creating inconsistencies, because they will always be deduplicated at storage time.

Data as four simple types…

We classify all data as four things everyone understands – it’s either someone (a Who), something (a What), it happens in a specific place (a Where), and it happens at a given time (a When).

Data can be queried without knowing the underlying model and in fact you can query on the description of the data or the way it’s classified, without even knowing the data itself.

For instance – show me all the whos…

No need for de-duplication…

We can connect the data elements through the context in which they’re used. Data elements, quite simply, are never duplicated. Their meaning becomes clear through their context and connections.

The Dynizer discerns the structure of structured, semi-structured and unstructured data in the same way and doesn’t need to integrate data to a pre-defined model. This makes it particularly useful in discovering context within large quantities of data on the fly.

Add multiple viewpoints…

By adding descriptions to the elements we can look at the data in many ways, even using composite constraints and time series-based queries, without affecting its original form.

The Dynizer classifies all data as four basic elements in endless combination, making integration faster, more efficient and more consistent.

It doesn’t create point-to-point data matches which means data can be attached to any import action and reused in any relevant data model. Purpose-neutral abstracted data is usable without duplication outside its initial context, remaining consistent over conflicting database schemas.

That makes the Dynizer useful for any organisation that needs to take in and process data from different sources, whether the data is structured as in spreadsheets, semi-structured as in emails, or unstructured as in free text.

Data models keep evolving…

As new data arrives, the data model continues to evolve without breaking existing queries.

The Dynizer makes querying and analytics simple. There’s no rigid use-case predefinition of analytics, data mapping or warehousing. Core datatypes are detected in all types of text, stored and automatically linked. Content and metadata queries are integrated and you don’t need domain specialists to train the analyzer to find the overlaps in data.

Core Who, What, When, and Where datatypes are stored as Topologies in the Dynizer. This automatically links text content with the structured data in the Action Model without having to match element to element. Analysis is available in the REST API as it is in all other functions.

This makes the Dynizer useful for organizations for whom the meaning and context within data is as or more important than the data itself for their day-to-day activities.

One data model, many apps…

Multiple applications can share the same data model. Additionally, the Dynizer’s data model can build its own UI. Developers can create and store components as Actions to dynamically generate forms etc. Dynizer-generated user interfaces can change dynamically if the data model changes and don’t need a separate database to store the data.

This creates a framework for developers to build UI applications on top of the Dynizer, with UI components that are stored in the Dynizer itself.

This means that the very structure of applications can be stored alongside the data it processes, without the infrastructure affecting the information. Applications can be built very quickly requiring very little technical know-how, since modelling the application becomes as simple as modelling the data.

Technically speaking, the model is based on micro services and uses a Restfull API for data management with integrated analytics also available via REST, along with full traceability and auditing.