Customer Area

Welcome to Xplain Data’s customer area.

How To

How to Install Xplain Data Software on your Server.

Introduction into User Management.

“Application Development” training materials

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XOE Videos

The Object Tree

Here you find elements of the object tree explained, the root-object and recursive sub-objects, dimensions and attributes. You will see how to navigate the object tree, which may be quite extensive in case of a complex object.

Attributes, States and Hierarchies

Each dimension in the object tree may have one or a number of attributes. An attribute groups the values of a dimension into meaningful categories. Those are typically organized into a hierarchy with top-level categories and sub-categories. You will see how to “open” an attribute on the analysis canvas (the simplest way how to “open” a query).

Simple Selections

You will see how to put selections onto attributes in the different branches of the object tree. Those selections go into the “global selections” – the current global selections are visualized via yellow icons. You will see how those affect other open queries.

Adding additional measures / aggregations into a query

We will explain how the term “aggregation” is used, and how additional aggregations can be added into an (open) query – e.g., simply by drag-and-drop. You will learn which aggregation methods exist (COUNT, AVG, SUM, …) and how to switch the aggregation method in a query.

Aggregations along the edges of the object tree

Similar to adding aggregations into a query or aggregate data along the edges of the object tree, thereby generating additional dimensions in the object tree (“aggregation dimensions”). Aggregating data upward the edges of the object tree is one of the distinct features of Object Analytics.

Floating vs. fixed semantics aggregations

An aggregation dimension is defined by an aggregation method (COUNT, AVG, SUM, …) and a “where clause” (i.e., what is aggregated). Usually this where-clause (selection) is fixed when generating the aggregation dimension; it henceforth defines the semantics of the aggregation dimension. Aggregation dimensions may have “floating semantics” as well. Here you will get an idea what floating semantics means, and what this is good for.

How Selections Propagate

Selecting some categories in an attribute means adding a selection (an additional where clause) to the global selections. All “open queries” will react to that and will immediately be updated according to this additional selection – that is why they are called “open” because they are open to additional selections. This defines the basic interactive usability concept, i.e., how selections propagate across the object tree. There is not choice in how selections propagate within one object and from one object to sub-objects. There is, however, a choice in how selections propagate upward the object tree. You will see how floating semantic dimensions can be used to define this propagation behavior.

Multi-dimensional queries

Here you learn how to generate a multi-dimensional query, i.e., a query which has multiple group-by clauses.


The default presentation of query results is simply a list of numbers. You may switch this list to a chart as well. In particular you may switch a multi-dimensional query to a multi-dimensional chart. Some of the charting capabilities are presented in this clip.

How to Pin Instances and Boolean Dimensions

Once you have selected a number of object instances (e.g., a specific class of patients or a market segment of customers), you might want to “pin” those. With that you define a new “Boolean dimension” – a dimension with only two states (yes/no) which defines whether this instance is with the defined segment or not.

The Relative Time Axis (Basics)

Often, more important than the absolute time is the time of an event relative to that of another event. You will learn how to set up a relative time axis, e.g., how to select all prescribed drugs within 3 months after a broken leg. With the relative time axis you may analyze different event streams in relation to each other, e.g. diagnoses relative to prescribed drugs, or machine failures relative to prior alarm messages. Often, sub-object to the root object are different sorts of event streams attached to the object in focus of analysis. The relative time axis is an important element of Object Analytics concept, as it allows to analyse sub-objects in relation to each other.

Multiple Selection Sets

In the standard setting there is just one selection set: the yellow selections. You may, however, have multiple selections sets. This video shows how you can use multiple selection and demonstrates the primary purpose of those selection sets. Multiple selection sets are a means for experts to build very generic analytic applications, which then can be used by a larger number of standard users.