Automated Anomaly Detection with Crop Medoids in n8n
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This n8n workflow automates anomaly detection by identifying and utilizing medoids from your crop datasets. It leverages Qdrant for efficient data storage and retrieval, enabling precise identification of outlier data points.
About This Workflow
This workflow is designed to enhance anomaly detection capabilities by intelligently identifying 'medoids' within your crop datasets stored in Qdrant. Medoids, being representative data points of their clusters, serve as crucial reference points for anomaly detection. The process begins by fetching the total number of points in a collection, followed by calculating a distance matrix to identify potential medoids. A Python script then processes this matrix to pinpoint the most central point within a cluster, which is subsequently marked as a medoid in Qdrant. The workflow further refines this by retrieving the medoid's vector and using its inverse to search for the furthest points, establishing a threshold for anomaly detection. This sophisticated approach ensures robust and accurate identification of anomalies, crucial for maintaining data integrity and identifying unusual patterns in your crop data.
Key Features
- Automated Medoid Identification: Dynamically finds the most representative data point (medoid) for each cluster.
- Qdrant Integration: Seamlessly connects with Qdrant for efficient storage, search, and retrieval of vector data.
- Advanced Anomaly Scoring: Employs vector search and distance calculations to establish a robust anomaly detection threshold.
- Crop-Specific Analysis: Filters data by crop name to perform targeted anomaly detection within specific agricultural contexts.
- Python Scripting for Precision: Utilizes Python for complex matrix operations to accurately determine medoids.
How To Use
- Configure Qdrant Cluster Variables: Ensure the
Qdrant cluster variablesnode is correctly set up with your Qdrant Cloud URL and collection name. - Set Up Qdrant API Credentials: Provide your Qdrant API key to the
QdrantApi accountcredential. - Initialize Workflow: Click 'Test workflow' on the
When clicking ‘Test workflow’node to initiate the process. - Review Cluster Distance Matrix: The
Cluster Distance Matrixnode will fetch data for searching. Adjust parameters likemaxClusterSizeandcropNameas needed within the node's JSON body. - Verify Medoid Identification: The
Scipy Sparse Matrixnode will calculate and identify the medoid ID. - Mark Medoid in Qdrant: The
Set medoid idnode will update Qdrant by marking the identified medoid point. - Retrieve Medoid Vector: The
Get Medoid Vectornode fetches the vector and payload of the identified medoid. - Prepare for Thresholding: The
Prepare for Searching Thresholdnode configures variables likeoppositeOfCenterVector,cropName, andcenterIdfor the next step. - Search for Outliers: The
Searching Scorenode queries Qdrant to find points furthest from the medoid, based on thefurthestFromCentervariable (ensure this is set inMedoids Variables). - Establish Anomaly Threshold: The
Threshold Scorenode calculates thethresholdScorebased on the furthest point found, defining the boundary for anomaly detection.
Apps Used
Workflow JSON
{
"id": "a475fbbb-8895-4002-ab5d-10e71405645f",
"name": "Automated Anomaly Detection with Crop Medoids in n8n",
"nodes": 22,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: a475fbbb-8895...
About the Author
SaaS_Connector
Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
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