https://github.com/ranfysvalle02/doc2kg
https://github.com/ranfysvalle02/doc2kg
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/ranfysvalle02/doc2kg
- Owner: ranfysvalle02
- Created: 2025-01-08T06:00:33.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-08T07:36:05.000Z (9 months ago)
- Last Synced: 2025-01-22T21:19:01.446Z (9 months ago)
- Language: Python
- Size: 209 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# doc2kg
https://github.com/ranfysvalle02/graph-knowledge
https://github.com/ranfysvalle02/mongodb-graph
---
curl -X POST "https://--modal-.modal.run" -F "file=@/Users//Downloads/demo.pdf""
---
```
{
"ai_kg": {
"name": "MongoDB IoT Knowledge Graph",
"nodes": [
{
"id": "mongodb",
"label": "MongoDB",
"properties": {
"description": "A flexible and scalable database designed for IoT applications.",
"features": [
"Rich document model",
"Time series collections",
"Horizontal scalability",
"Data tiering",
"Integrated analytics"
]
}
},
{
"id": "iot_application",
"label": "IoT Application",
"properties": {
"components": [
"Data Collection",
"Data Management",
"Data Analysis"
],
"challenges": [
"Scalability",
"Flexibility",
"Real time analysis"
]
}
},
{
"id": "data_collection",
"label": "Data Collection",
"properties": {
"description": "The process of provisioning devices and collecting data.",
"challenges": [
"Diverse sensor data",
"Rapidly changing data structures"
]
}
},
{
"id": "data_analysis",
"label": "Data Analysis",
"properties": {
"description": "Aggregration and analysis of data for actionable insights.",
"challenges": [
"Creating a unified data model",
"Real-time data analysis"
]
}
},
{
"id": "data_management",
"label": "Data Management",
"properties": {
"description": "Managing and storing IoT data efficiently.",
"challenges": [
"Scalability",
"Data tiering",
"Audit logs"
]
}
},
{
"id": "time_series",
"label": "Time Series Data",
"properties": {
"description": "Data that is indexed in time order, essential for IoT applications."
}
},
{
"id": "atlas",
"label": "MongoDB Atlas",
"properties": {
"description": "Cloud database service provided by MongoDB.",
"features": [
"Auto-scaling",
"Atlas Stream Processing",
"Integrated search capabilities"
]
}
}
],
"edges": [
{
"source": "mongodb",
"target": "iot_application",
"label": "enables"
},
{
"source": "iot_application",
"target": "data_collection",
"label": "includes"
},
{
"source": "iot_application",
"target": "data_management",
"label": "includes"
},
{
"source": "iot_application",
"target": "data_analysis",
"label": "includes"
},
{
"source": "data_collection",
"target": "time_series",
"label": "captures"
},
{
"source": "data_management",
"target": "time_series",
"label": "manages"
},
{
"source": "data_analysis",
"target": "time_series",
"label": "analyzes"
},
{
"source": "mongodb",
"target": "atlas",
"label": "provides"
}
]
}
}
```
---
### **What Is a Knowledge Graph?**
A **knowledge graph** is a visual way to represent information. It uses **nodes** (circles) to represent concepts or entities, and **edges** (lines) to show relationships between them. Think of it as a map that shows how different ideas are connected.
---
### **Central Theme: MongoDB in IoT Applications**
The central focus of this knowledge graph is how **MongoDB** enables and supports **IoT (Internet of Things) applications**. IoT involves connecting devices (like sensors, appliances, vehicles) to the internet, allowing them to send and receive data.
---
### **Breaking Down the Nodes and Relationships**
Let's explore each node and see how they connect.
#### **1. MongoDB**
- **Description:** A flexible and scalable database designed for IoT applications.
- **Features:**
- **Rich Document Model:** Stores data in JSON-like documents, allowing for versatile and complex data structures.
- **Time Series Collections:** Efficiently handles data indexed by time, which is common in IoT.
- **Horizontal Scalability:** Easily adds more servers to handle increased data loads.
- **Data Tiering:** Automatically moves data between different storage types based on how frequently it's accessed.
- **Integrated Analytics:** Analyzes data within the database without needing external tools.
**Relationships:**
- **Enables** **IoT Application**
- **Provides** **MongoDB Atlas**
---
#### **2. IoT Application**
- **Components:**
- **Data Collection**
- **Data Management**
- **Data Analysis**
- **Challenges:**
- **Scalability:** Handling the vast amount of data from numerous devices.
- **Flexibility:** Adapting to different types of devices and data formats.
- **Real-Time Analysis:** Processing data as it arrives to make immediate decisions.
**Relationships:**
- **Includes** **Data Collection**
- **Includes** **Data Management**
- **Includes** **Data Analysis**
---
#### **3. Data Collection**
- **Description:** The process of setting up devices and gathering data from them.
- **Challenges:**
- **Diverse Sensor Data:** Different devices produce various types of data.
- **Rapidly Changing Data Structures:** Data formats may change as devices update or new ones are added.
**Relationships:**
- **Captures** **Time Series Data**
---
#### **4. Data Management**
- **Description:** Efficiently storing and organizing IoT data.
- **Challenges:**
- **Scalability:** Managing large volumes of data.
- **Data Tiering:** Optimizing storage costs by moving less-used data to cheaper storage.
- **Audit Logs:** Keeping records of data access and changes for security and compliance.
**Relationships:**
- **Manages** **Time Series Data**
---
#### **5. Data Analysis**
- **Description:** Processing and examining data to gain insights and inform actions.
- **Challenges:**
- **Creating a Unified Data Model:** Combining data from different sources into a single format.
- **Real-Time Data Analysis:** Quickly analyzing data as it comes in.
**Relationships:**
- **Analyzes** **Time Series Data**
---
#### **6. Time Series Data**
- **Description:** Data indexed in time order, essential for tracking events over time in IoT.
**Importance:**
- IoT devices generate time-stamped data continuously (e.g., temperature readings every second).
- Efficient handling of this data type is crucial for performance.
**Relationships:**
- **Captured by** **Data Collection**
- **Managed by** **Data Management**
- **Analyzed by** **Data Analysis**
---
#### **7. MongoDB Atlas**
- **Description:** A cloud database service provided by MongoDB.
- **Features:**
- **Auto-Scaling:** Automatically adjusts resources based on demand.
- **Atlas Stream Processing:** Processes continuous streams of data in real-time.
- **Integrated Search Capabilities:** Allows powerful search functions within the data.
**Relationships:**
- **Provided by** **MongoDB**
---
### **Connecting the Dots: How Everything Relates**
- **MongoDB** is the foundational database technology that **enables** the development and operation of **IoT Applications**.
- An **IoT Application** **includes** three main components:
- **Data Collection**
- **Data Management**
- **Data Analysis**
- **Data Collection**, **Data Management**, and **Data Analysis** all deal with **Time Series Data**:
- **Data Collection** **captures** this data from devices.
- **Data Management** **manages** the storage and organization of the data.
- **Data Analysis** **analyzes** the data to extract useful information.
- **MongoDB Atlas** is a service **provided by** MongoDB to make deploying and managing databases easier, especially in the cloud.
---
### **Visualizing the Knowledge Graph**
Imagine a diagram where:
- **MongoDB** is connected to **IoT Application** with an arrow labeled "enables".
- **IoT Application** has three branches leading to **Data Collection**, **Data Management**, and **Data Analysis**, each labeled "includes".
- **Data Collection**, **Data Management**, and **Data Analysis** all point to **Time Series Data** with arrows labeled "captures", "manages", and "analyzes" respectively.
- **MongoDB** also connects to **MongoDB Atlas** with an arrow labeled "provides".
---
### **Why Does This Matter?**
Understanding this knowledge graph is significant for several reasons:
#### **1. Handling Vast Amounts of Data**
- **Scalability:** IoT devices generate a massive amount of data. MongoDB's horizontal scalability allows databases to grow seamlessly as more data is collected.
#### **2. Flexibility in Data Structures**
- **Rich Document Model:** IoT environments often involve various devices producing different data types. MongoDB's flexible data model can handle this diversity without rigid schemas.
#### **3. Efficient Time Series Data Management**
- **Time Series Collections:** Specialized storage and querying capabilities for time-series data improve performance and reduce storage costs.
#### **4. Real-Time Data Processing and Analysis**
- **Integrated Analytics and Atlas Stream Processing:** By processing data in real-time, businesses can react immediately to events, which is crucial in areas like monitoring, automation, and alerting systems.
#### **5. Cost Optimization**
- **Data Tiering:** Automatically moves infrequently accessed data to less expensive storage, optimizing costs without sacrificing accessibility.
#### **6. Simplified Deployment and Management**
- **MongoDB Atlas:** Offers a fully managed database service in the cloud, reducing the burden of database administration and allowing developers to focus on building applications.
---
### **Real-World Applications**
- **Smart Homes:** Managing data from various sensors and devices controlling lighting, temperature, and security.
- **Industrial IoT:** Monitoring equipment performance and predicting maintenance needs in factories.
- **Healthcare:** Tracking patient vital signs with wearable devices and providing real-time alerts.
- **Transportation:** Managing fleets with GPS data, optimizing routes, and tracking vehicle performance.
---