Modeling IoT Data in Azure Times Series Insights Preview

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The Time Series Model (TSM) within Azure Time Series Insights Preview enables IoT architects to create custom models to contextualize IoT telemetry data. Using these models, end users can compute, correlate, explore, and analyze their IoT telemetry data to gain valuable business insights.

This article steps through high-level features and benefits of TSM, discusses its three components, and provides a quick tour of the models used in two different environments: a proof-of-concept smart campus for Microsoft's own corporate headquarters, and the Contoso Wind Farm demo environment.

Key features and benefits

Times Series Model  data stored in Azure Time Series Insights Preview. Below are key features and benefits.

• Defines the hierarchy of IoT telemetry data. Using TSM, users can assign their tags (or sensors, depending on your industry) to one or more hierarchies. In a facilities management solution, for example, telemetry data can be organized in a hierarchy so that temperature, pressure, and air flow measurements are all placed within a specific room. Multiple rooms can be within a level. Multiple levels can be within a building. Multiple buildings can be within a campus. When users explore their IoT telemetry data from within the Azure Time Series Insights Preview Explorer, a relevant hierarchy makes the data easier to explore . Modeling tags or sensors is important for different individuals in an organization to get meaning and value needed for their role. For example, a facilities service engineer might be intimately familiar with what the value of tag "DMPR" (damper—a plate that regulates air flow in a variable air volume control (VAV) system) means and how to respond. However, a regional manager might instead need an aggregated view. An IoT architect can model a solution with Times Series Model so that individuals with different roles within a company will understand the meaning.

•  Enables comparison, correlation, or computation of IoT data. Because TSM stores context data with telemetry data, users can compare or correlate data. For example, a facilities operations manager can configure a model to view air handling units across multiple building sites. Or the manager can correlate the temperature change in a room with the number of people meeting in the room each hour of the day. Business decision makers can gain valuable insights by asking broad questions about their businesses. A model can also be setup to apply aggregate or scalar computations to tags of similar types.

• Reduces cost. TSM stores contextual information. This means that contextual data no longer needs to be stored within the payload of each telemetry message sent to your TSI environment. By reducing the payload size, the cost of Azure services goes down.

Components of Azure Time Series Model

• Instances Time Series Model instances are digital representations of the device emitting time series data. Instances have descriptive information associated with them such as a time series ID, type, name, description, hierarchies, and other instance fields.

• Hierarchies Time Series Model hierarchies organize instances by specifying property names and their parent-child relationships. They could be a spatial topology such as the physical lay-out of a building or a semantic hierarchy in which relationships are defined and visualized.

• Types Time Series Model types help define variables or formulas for doing computations. Types are associated with a specific Time Series Insights instance, and an instance can only have one type. A type can have one or more variables. For example, a Time Series Model instance might be of type Temperature Sensor, which consists of the variables avg temperature, min temperature, and max temperature. Using type variables, you can compute and visualize a derived time series. For example, a measurement could be multiplied by a value to do a unit conversion and then visualized in the explorer.

 
Figure 1 Equipment-Centric Hierarchy

 Figure 2 Building-Centric Hierarchy

 

We can drill down on an instance in the equipment view. Remember that an instance is a virtual representation of the time series. In Fig. 3 below, we are looking at the DMPR _CMD, the value of the sensor on a damper in level 1 of building 121.
  Figure 3 Exploring Instance Device_1210110_AV_9

 

After receiving an alert, an operator might come to the Azure Time Series Insights Preview explorer to get more data. For example, a damper “stuck closed fault” would go off if this damper is commanded open, but the sensor value indicates that it’s closed. This could indicate an obstruction of the damper, or a faulty motor. An operator might come to the explorer to get a visual of how long this has been an issue and to understand if this unit has experienced problems in the past. He or she could also use the level hierarchy to compare damper positions in other rooms to see if this is anomalous.
 
Figure 4 Comparing damper positions on different units

 

We can switch to model view by clicking on the three-dimensional box on the left side of the user interface. From here, we use the Azure Time Series Preview Explorer to look at the instance Device_1210101_A1_10. In Fig. 5 below, we see the hierarchies it’s associated with, its type, as well as its name fields and values.

 

Figure 5 Device_1210110_AV_9 instance field names and values

 

Clicking on the pencil, we can view the underlying instance definition in Fig. 6. We can edit as needed from this interface.
Figure 6 Instance definition

 

Notice that we can also view the underlying JSON code by clicking on “View JSON.” See Fig. 7 below.

 
Figure 7 Underlying JSON Code

 

 
In the explorer we also have the option to bulk-edit instances. This enables associating hundreds of instances a new hierarchy or updating the type in one action.

Figure 8 Bulk editing instances

 

 Figure 9 View a Type within the Model

 

 Figure 10 JSON Code

 

  Contoso Wind Farm

 Figure 11 Contoso Wind Farm Model

 

 Figure 12 Edit Time Series Lanes

 

Figure 13 View Multiple Time Series Simultaneously