Adaptive Data Scraping Framework

The Adaptive Data Scraping Framework is a core component of the Lumora network, designed to efficiently collect publicly available data while maintaining compliance with legal and ethical standards. This framework is dynamic, allowing it to adapt to changing website structures, rate limits, and network conditions to ensure robust and reliable data acquisition.

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Objectives

• Dynamic Adaptation: Automatically adjust to evolving website structures and protocols.

• Ethical Compliance: Adhere to public data scraping policies and respect rate limits.

• Scalability: Handle large-scale, distributed scraping tasks efficiently.

• Fault Tolerance: Recover gracefully from task failures and unexpected changes in data sources.

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Core Components

1. Dynamic Task Assignment:

   • Tasks are distributed to nodes based on proximity, bandwidth availability, and task priority.

   • Ensures efficient resource utilization and reduced latency.

2. Rate Limiting and Throttling:

   • Adapts scraping speed dynamically to respect website-imposed rate limits.

   • Avoids triggering anti-bot mechanisms, ensuring smooth and ethical operation.

3. Data Parsing and Normalization:

   • Supports structured (JSON, XML) and unstructured (HTML, text) data formats.

   • Normalizes collected data into a consistent schema for downstream processing.

4. Failure Detection and Recovery:

   • Detects task failures in real-time and automatically retries or reassigns tasks to other nodes.

   • Implements exponential backoff strategies to prevent repeated failures.

5. Encryption and Aggregation:

   • Encrypts scraped data using AES-256 before transferring it to the Lumora network.

   • Aggregates data at collection points to ensure efficiency and scalability.

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Data Scraping Algorithm

Input Variables:

• T: List of target URLs.

• N: Number of available nodes.

• R: Rate limit per target site (requests/second).

• P: Parsing rules for each target site.

Steps:

1. Task Initialization:

   • Divide target URLs into T/N tasks for distributed execution.

2. Dynamic Throttling:

   Delay = 1 / R

   • Introduce delays between requests to avoid exceeding rate limits.

3. Scraping Execution:

   • Nodes fetch data from assigned URLs and parse it using predefined rules (P).

   • Store parsed data in a normalized format.

4. Failure Handling:

   • Detect task failures using HTTP status codes or timeouts.

   • Retry failed requests with exponential backoff:

     Retry_Delay = Base_Delay * (2 ^ Retry_Attempt)

5. Encryption and Transfer:

   • Encrypt scraped data:

     Encrypted_Data = AES-256(Key, Data)

   • Transfer encrypted data to the Lumora network for aggregation.

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Real-Time Adaptation

Dynamic Parsing Rules:

• XPath and CSS Selectors:

  • Extract specific data points from HTML using dynamic selectors.

• Machine Learning Models:

  • Train models to identify and extract patterns from unstructured data.

Rate Limit Monitoring:

• Nodes monitor HTTP headers (e.g., Retry-After) to detect rate limits.

• Automatically adjust scraping speed based on observed behavior.

Proactive Failure Recovery:

• Detect anti-bot challenges (e.g., CAPTCHAs) and reroute tasks to alternative nodes.

• Use backup URLs or alternative scraping strategies if primary sources fail.

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Example Scenario

Scenario:

• Target site: example.com

• Total URLs: T = 1,000

• Rate limit: R = 5 requests/second

• Nodes: N = 10

Steps:

1. Task Distribution:

   URLs per Node = T / N = 1,000 / 10 = 100

2. Dynamic Throttling:

   Delay = 1 / R = 1 / 5 = 0.2 seconds

3. Scraping Execution:

   • Each node processes 100 URLs with a delay of 0.2 seconds between requests.

4. Failure Recovery:

   • Failed URLs are retried with an exponential backoff:

     Retry_Delay = 0.2 * (2 ^ Retry_Attempt)

5. Data Aggregation:

   • Scraped data is encrypted and sent to the Lumora network for aggregation.

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Key Benefits

1. Adaptability:

   • Automatically adjusts to changes in website structures and rate limits.

2. Scalability:

   • Efficiently handles thousands of URLs across distributed nodes.

3. Compliance:

   • Operates within the bounds of ethical and legal data scraping standards.

4. Efficiency:

   • Reduces bandwidth consumption by dynamically managing requests and retries.

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Implementation in Lumora

Technology Stack:

• Scraping Frameworks: BeautifulSoup, Scrapy, Selenium.

• Parsing and Normalization: JSON Schema, Pandas.

• Encryption: PyCryptodome for AES-256 encryption.

• Communication: WebSocket and HTTP APIs for real-time updates.

Integration:

• Nodes communicate with the Decentralized Task Manager to fetch task assignments and report progress.

• Aggregated data is securely stored in decentralized storage (e.g., IPFS).

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Conclusion

The Adaptive Data Scraping Framework ensures Lumora’s ability to collect high-quality, publicly available data at scale while maintaining compliance, efficiency, and fault tolerance. This dynamic approach positions Lumora as a robust solution for AI and data analytics needs.