Solution Comparison:
┌──────────────────┬───────────┬───────────┬────────────┐
│ Solution         │ Real-time │ Semantics │ Decisions  │
├──────────────────┼───────────┼───────────┼────────────┤
│ Point-to-Point   │ Medium    │ None      │ None       │
│ ESB Integration  │ Med-High  │ Weak      │ None       │
│ Data Warehouse   │ Low (T+1) │ Weak      │ Limited    │
│ Coomia DIP      │ High (sec)│ Strong    │ Built-in   │
└──────────────────┴───────────┴───────────┴────────────┘

ObjectType: Equipment
  description: "Core business entity - factory equipment"
  properties:
    - id: string (PK)
    - name: string
    - type: enum
    - status: enum [Active, Inactive, Pending, Archived]
    - priority: enum [Low, Normal, High, Critical]
    - metadata: dict
  computed_properties:
    - risk_score: float
    - health_index: float
    - trend: enum [Improving, Stable, Declining]

ObjectType: SensorReading
  description: "Sensor time-series data"
  properties:
    - id: string (PK)
    - source_system: string
    - timestamp: datetime
    - value: float
    - unit: string
    - quality_flag: enum [Good, Suspect, Bad]
  time_series: true
  retention: "365d"

ObjectType: MaintenanceOrder
  description: "Maintenance work order"
  properties:
    - id: string (PK)
    - type: enum
    - status: enum [Draft, Submitted, InReview, Approved, Rejected, Completed]
    - requester: string
    - start_time: datetime
    - end_time: datetime
    - severity: enum [Low, Medium, High, Critical]

ObjectType: FailurePrediction
  description: "Failure prediction results"
  properties:
    - id: string (PK)
    - analysis_type: string
    - input_data: dict
    - result: dict
    - confidence: float [0-1]
    - model_version: string

ObjectType: AlertRule
  description: "Alert rule configuration"
  properties:
    - id: string (PK)
    - source_id: string
    - target_id: string
    - relation_type: string
    - weight: float
    - evidence: list[string]

Relations:
  - Equipment -> generates -> SensorReading
    cardinality: 1:N
    description: "Equipment generates sensor data"

  - Equipment -> triggers -> MaintenanceOrder
    cardinality: 1:N
    description: "Equipment triggers maintenance orders"

  - SensorReading -> analyzedBy -> FailurePrediction
    cardinality: N:1
    description: "Sensor data processed by analysis engine"

  - FailurePrediction -> impacts -> Equipment
    cardinality: N:M
    description: "Prediction results feed back to equipment"

  - Equipment -> linkedVia -> AlertRule
    cardinality: N:M
    description: "Equipment linked to alert rules"

  - MaintenanceOrder -> resolvedBy -> FailurePrediction
    cardinality: N:1
    description: "Maintenance resolved through predictive analysis"

┌───────────────────────────────────────────────────────┐
│                   Application Layer                    │
│  ┌───────────┐  ┌────────────┐  ┌───────────┐        │
│  │ Equipment  │  │ Maintenance│  │  Mobile   │        │
│  │ Dashboard  │  │  Reports   │  │           │        │
│  └────┬──────┘  └─────┬──────┘  └────┬──────┘        │
│       └───────────────┼──────────────┘                │
│                       │                                │
│  ┌────────────────────┴────────────────────┐          │
│  │          Ontology Semantic Layer          │          │
│  │   Equipment --- SensorReading --- MaintenanceOrder │
│  │       |           |           |                     │
│  │   FailurePrediction ------- AlertRule              │
│  │   Unified Model / Query / RBAC                      │
│  └────────────────────┬────────────────────┘          │
│                       │                                │
│  ┌────────────────────┴────────────────────┐          │
│  │   Data Ingestion: CDC|API|Stream|Batch   │          │
│  └─────────────────────────────────────────┘          │
└───────────────────────────────────────────────────────┘

from ontology_sdk import OntoPlatform

platform = OntoPlatform()

# Query high-risk equipment with sensor data
entities = (
    platform.ontology
    .object_type("Equipment")
    .filter(status="Active")
    .filter(priority__in=["High", "Critical"])
    .include("SensorReading")
    .include("MaintenanceOrder")
    .order_by("updated_at", ascending=False)
    .limit(100)
    .execute()
)

for entity in entities:
    print(f"Equipment: {entity.name} | Health: {entity.health_index}")

    # Check anomalous sensor data, auto-trigger prediction
    bad_data = [d for d in entity.sensorreadings
                if d.quality_flag == "Bad"]
    if len(bad_data) > 5:
        platform.actions.execute(
            "ExecuteFailurePrediction",
            target_id=entity.id,
            analysis_type="anomaly_detection",
            parameters={"window": "24h"}
        )

rules:
  - name: "High Risk Equipment Alert"
    trigger: Equipment.risk_score > 80
    actions:
      - alert: critical
      - action: Escalate(severity=Critical)

  - name: "Health Trend Deterioration"
    trigger: Equipment.trend == "Declining" AND priority in [High, Critical]
    actions:
      - alert: warning
      - action: ExecuteFailurePrediction(type=root_cause)

  - name: "Sensor Data Anomaly"
    trigger: SensorReading.quality_flag == "Bad" count > 10/hour
    actions:
      - alert: warning

  - name: "Maintenance Auto-Escalation"
    trigger: MaintenanceOrder.severity == "Critical"
    actions:
      - action: Escalate(severity=Critical)
      - notification: sms -> on_call

from intelligence_plane.models import PredictionModel
from datetime import timedelta

class FailurePredictionModel(PredictionModel):
    def __init__(self):
        super().__init__(
            name="failureprediction_v2",
            input_type="Equipment",
            output_type="FailurePrediction"
        )

    def predict(self, entity, context):
        history = (
            context.ontology.object_type("SensorReading")
            .filter(source_id=entity.id)
            .filter(timestamp__gte=context.now - timedelta(days=90))
            .order_by("timestamp")
            .execute()
        )
        features = self.extract_features(history)
        prediction = self.model.predict(features)
        return {
            "level": prediction["level"],
            "confidence": prediction["confidence"],
            "factors": prediction["contributing_factors"],
            "actions": prediction["recommended_actions"]
        }

Year 1 ROI = (340 - 83) / 83 * 100% = 310%
3-Year ROI = (340*3 - 83 - 20*2) / (83 + 20*2) * 100% = 729%