Start with three HIGH alerts
Import the IoT scenario and separate the evidence needed to order three machine inspections.
Question for this chapter
What evidence is needed to choose the first inspection among three alerts?
Why this matters now
Starting with pipeline names can leave you with a successful run but no explanation of why its output changes maintenance work. First, define the roles of the source signal, alert, summary score, and machine relationships.
Try it
iot.zip Download(25 KB)In the left sidebar, select Collections. On the Collections page, choose the upper-right more (⋯) menu → Import (가져오기) and upload the ZIP. If validation reports no conflicts, select Start import and wait for completion.
Success looks like this
The collection tree shows raw (Raw IoT Collection) and processed (Processed IoT Collection). The
current scenario registers its data and executable assets under processed; raw is empty. Import is
complete when machine_sensors (Machine Sensor Readings) contains 20 rows.
Interpret the result
| Evidence | Question it answers | Asset to inspect |
|---|---|---|
| Source sensor | What value and status signal arrived? | machine_sensors |
| Alert | Which row was classified as anomalous, and why? | anomaly_detections |
| Summary score | How can machines be compared? | equipment_health |
| Relationship context | Which sensors and maintenance events belong to a machine? | iot_machine ontology |
Deep dive — assets imported by the scenario
| Asset type | Count | Contents |
|---|---|---|
| Collections | 2 | raw, processed |
| Datasets | 3 | sensor readings, anomaly detections, machine health |
| Codes | 4 | normalization, detection, health rollup, ontology materialization |
| Pipelines | 3 | anomaly detection, health rollup, ontology materialization |
| Ontology | 3 entities / 2 relations | machines, sensors, maintenance events, and two relations |
| Knowledge / dashboard | 1 each | maintenance_manual, equipment_health |
The import loads a synthetic sensor Parquet file into machine_sensors. The other two datasets are
populated after you run pipelines in later chapters.
Next decision
You have separated the roles of the evidence. Next, find the three quality_flag values that produced
the alerts among the 20 sensor rows.