Object Analytics Database

The root object might be the “Patient”, the “Machine” or the “Customer”. Pull data from different sources and attach them as sub-objects (or recursive sub-objects). Typical sub-objects are event-streams such as the prescriptions, diagnoses, and procedures along the lifetime of the patient. Once such an “object-centric” view is built, you can easily analyze your core business object holistically across all information attached to it.

Relational databases are great. Great for what they are built for – consistently managing complex data. This requires splitting an object into atomic entities and storing different parts in different tables. Once distributed across many tables, however, an object is hard to analyze “as a whole” …

… and that is where our technology differs. With our object-centric representation, complex queries analyzing different sub-objects in relation can be executed with high performance. Algorithms previously painful to implement are now easy to apply. Novel algorithms – previously unimaginable – are becoming feasible.

Object Analytics goes from the world of flat tables to thinking in terms of entire objects. It will propel the field of Data Analytics and Artificial Intelligence into novel orbits.

Some of our larger installations hold 60 million patients and 3 billion events in different sub-objects on a serve with 128 cores – serving numerous client-sessions in parallel. The standard installation is via a Docker container. Deploying our technology on a cloud platform such as AWS (or locally on your machine) is just easy.

Object
Analytics
Database

The root object might be the “Patient”, the “Machine” or the “Customer”. Pull data from different sources and attach them as sub-objects (or recursive sub-objects). Typical sub-objects are event-streams such as the prescriptions, diagnoses, and procedures along the lifetime of the patient. Once such an “object-centric” view is built, you can easily analyze your core business object holistically across all information attached to it.

Relational databases are great. Great for what they are built for – consistently managing complex data. This requires splitting an object into atomic entities and storing different parts in different tables. Once distributed across many tables, however, an object is hard to analyze “as a whole” …

… and that is where our technology differs. With our object-centric representation, complex queries analyzing different sub-objects in relation can be executed with high performance. Algorithms previously painful to implement are now easy to apply. Novel algorithms – previously unimaginable – are becoming feasible.

Object Analytics goes from the world of flat tables to thinking in terms of entire objects. It will propel the field of Data Analytics and Artificial Intelligence into novel orbits.

Some of our larger installations hold 60 million patients and 3 billion events in different sub-objects on a serve with 128 cores – serving numerous client-sessions in parallel. The standard installation is via a Docker container. Deploying our technology on a cloud platform such as AWS (or locally on your machine) is just easy.

Object
Analytics
Database

The root object might be the “Patient”, the “Machine” or the “Customer”. Pull data from different sources and attach them as sub-objects (or recursive sub-objects). Typical sub-objects are event-streams such as the prescriptions, diagnoses, and procedures along the lifetime of the patient. Once such an “object-centric” view is built, you can easily analyze your core business object holistically across all information attached to it.

Relational databases are great. Great for what they are built for – consistently managing complex data. This requires splitting an object into atomic entities and storing different parts in different tables. Once distributed across many tables, however, an object is hard to analyze “as a whole” …

… and that is where our technology differs. With our object-centric representation, complex queries analyzing different sub-objects in relation can be executed with high performance. Algorithms previously painful to implement are now easy to apply. Novel algorithms – previously unimaginable – are becoming feasible.

Object Analytics goes from the world of flat tables to thinking in terms of entire objects. It will propel the field of Data Analytics and Artificial Intelligence into novel orbits.

Some of our larger installations hold 60 million patients and 3 billion events in different sub-objects on a serve with 128 cores – serving numerous client-sessions in parallel. The standard installation is via a Docker container. Deploying our technology on a cloud platform such as AWS (or locally on your machine) is just easy.

Interfaces

Open interfaces offer a wealth of opportunities: Build your own custom application using available interfaces (JavaScript). Use Dash Enterprise via the Python interface for dashboarding.

The Object Analytics Database exposes a web interface. You may issue queries from within any programming language which can initiate web requests.

On top of that web-interface an additional convenience layer is available which makes it more comfortable to run queries and get back results, for example in terms of a Pandas DataFrame. There are packages available for Python, R (beta) and JavaScript. Python and R primarily address data scientists, while the JavaScript interface allows web developers to quickly implement an individual analytics application.

In addition, there is a Java Object MapReduce interface available. It allows you to define an operation on an object and execute it massively parallel on millions of stored object instances. With this interface, you can inject algorithms deeply into the core engine of the database. Algorithms come to data instead the data to algorithms. This interface is still not officially released – please consult the Xplain Data team if you would like to use it.

The “Object Explorer” is Xplain’s web-based frontend to view and interactively analyze objects statistically. It leverages the capabilities of the backend to implement a usability concept for analysis of objects “as a whole”, i. e. across all available data streams. Classical DWH approaches which replicate data into “star schemas” or “OLAP cubes” feel like keyhole views as compared to that. No need for experts to coerce data into those constraint analytical schemas, and with that you bring analytics from the ivory tower into your daily business. Interactively follow your train of thoughts from questions to follow-up questions and – supported by predictive models – discover potential “cause and effect” relationships.

The Object Explorer uses the same interfaces as described in “Interfaces”. That allows you to generate artefacts in the Object Explorer (e. g., a relative time axis), view the definition of that artefact, e. g. in terms of a piece of Python code, and simply copy that code into your Python script. You may indeed convert an entire session into a Python script (or into a Jupiter Notebook). The Object Explorer and your Python environment seamlessly interoperate – making the Object Explorer your extended development workbench.

Object Explorer

Open interfaces offer a wealth of opportunities: Build your own custom application using available interfaces (JavaScript). Use Dash Enterprise via the Python interface for dashboarding.

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The “Object Explorer” is Xplain’s web-based frontend to view and interactively analyze objects statistically. It leverages the capabilities of the backend to implement a usability concept for analysis of objects “as a whole”, i. e. across all available data streams. Classical DWH approaches which replicate data into “star schemas” or “OLAP cubes” feel like keyhole views as compared to that. No need for experts to coerce data into those constraint analytical schemas, and with that you bring analytics from the ivory tower into your daily business. Interactively follow your train of thoughts from questions to follow-up questions and – supported by predictive models – discover potential “cause and effect” relationships.

The Object Explorer uses the same interfaces as described in “Interfaces”. That allows you to generate artefacts in the Object Explorer (e. g., a relative time axis), view the definition of that artefact, e. g. in terms of a piece of Python code, and simply copy that code into your Python script. You may indeed convert an entire session into a Python script (or into a Jupiter Notebook). The Object Explorer and your Python environment seamlessly interoperate – making the Object Explorer your extended development workbench.