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Business Entities

This guide describes how Trendz uses entities from ThingsBoard, like asset, device, relation, etc.

Business Entities Topology

Let’s assume that we have a Smart Building solution. Our topology contains Buildings, Apartments and different Meters that are connected with each other using relations. Here is how our topology will look like:


In fact, Trendz operates with this topology as with the flat table that has columns for all attributes/telemetry from all Devices/Assets in this topology. The Relation between entities used to join fields from different Business Entities.

How it works

Now let’s check how Trendz resolves data from ThingsBoard using following report: we are using only 2 fields from Smart Building topology:

  • building name that belongs to the Building Asset
  • energy telemetry, that belongs to the Energy Meter Device
  • aggregation type SUM
  • time range - last month

  • Trendz will find all available buildings in the ThingsBoard.
  • Then all Apartments for each Building.
  • Finally, all Energy Meters that belong to the apartment.
  • After that, for all Energy Meters for each building, Trendz will load all energy telemetry for the last month
  • Trendz aggregates all loaded telemetry using SUM aggregation.
  • As a result we can see how much energy was consumed by each building.

It is not an exact algorithm description and there are a lot of optimizations performed in the background. But it allows to understand how much complexity handled inside Trendz, so you can focus on analytics but not on data fetching.

Aggregate telemetry and groups

The Next important step is to define how data should be aggregated. Here are supported aggregation types:

  • AVG
  • SUM
  • MIN
  • MAX
  • UNIQ

For changing aggregation type - just click on the field and select required value. image

Work with pulse output telemetry

Water meter is a good example of a device with pulse output - telemetry value always growing and during analysis, we want to convert it into delta values. Here is an example chart for such telemetry:


Let’s apply DELTA aggregation for this field and see how our data will look like:


Trendz automatically computes delta for this field for defined time ranges with required granularity. In case when DELTA aggregation applied for multiple devices - Trendz will apply SUM aggregation to the aggregate group - as the result, we can see total consumption on different levels (city, building, etc.)