SeigrEncoder

SeigrEncoder

The SeigrEncoder is a core class within Seigr’s Encoder/Decoder Module, responsible for converting raw data into modular .seigr capsules, optimized for storage and distribution within Seigr’s decentralized architecture. Designed to handle high data volumes efficiently, the SeigrEncoder employs Seigr's unique senary encoding, HyphaCrypt hashing, and advanced metadata management to segment, encode, and serialize data. The SeigrEncoder’s structured approach to data encoding enables secure, scalable, and adaptive storage across the Seigr Urcelial-net.

Overview

The SeigrEncoder performs a multi-step encoding process that transforms binary data into senary-encoded segments, or capsules. Each capsule is a fixed-size, traceable unit, equipped with metadata, cryptographic identifiers, and parameters for adaptive replication based on data demand. The SeigrEncoder’s structured output aligns with Seigr’s modular design philosophy, allowing each capsule to function autonomously within the network, supporting secure, efficient, and adaptive storage.

Core Functions of the SeigrEncoder

The SeigrEncoder class follows a four-step encoding process, which includes data segmentation, metadata generation, Protocol Buffers serialization, and senary encoding with adaptive error handling:

• Data Segmentation: Splits input data into manageable capsules.
• Metadata Generation: Defines capsule-specific metadata, enabling traceability, version control, and multidimensional referencing.
• Serialization with Protocol Buffers: Ensures compact, efficient metadata storage with support for schema evolution.
• Senary Encoding with Error Handling: Applies Seigr’s base-6 encoding, optimizing storage while ensuring data integrity.

Technical Process

The encoding process is defined by the following primary stages, each managed by specific methods within the SeigrEncoder class:

1. Data Segmentation

Data segmentation is the foundational step in the encoding process. Input data is divided into capsules based on the fixed TARGET_BINARY_SEGMENT_SIZE, typically set at 53,194 bytes, allowing space for metadata while optimizing storage and retrieval efficiency.

• Segment Sizing: The SeigrEncoder splits data based on TARGET_BINARY_SEGMENT_SIZE, ensuring each segment meets the network’s capacity and consistency requirements.
• Hashing with HyphaCrypt: Each capsule is hashed with HyphaCrypt to create a primary hash that uniquely identifies the segment within the network.
• Senary Conversion: Binary data is converted to senary format using the Encoding Utilities, reducing storage requirements while maintaining uniformity.

2. Metadata Generation

Each segment is assigned a unique metadata schema that defines its identity, position, and linkage within the broader .seigr structure. Metadata is structured according to the Seigr Metadata protocol, with components such as FileMetadata, SegmentMetadata, and AccessContext providing traceability, compatibility, and dynamic scaling.

The metadata schema consists of several critical fields:

• Primary and Secondary Hash Links: Each segment contains a primary hash for direct access and secondary hashes for multi-path retrieval, allowing flexible, non-linear data navigation across the Seigr network.
• 4D Coordinate Indexing: Capsules incorporate temporal and spatial coordinates, positioning each segment within a four-dimensional indexing system. This feature supports Seigr’s multi-layered Temporal Layering and allows for both temporal and spatial data management.
• Temporal Layers: Each capsule includes a TemporalLayer record, which maintains a time-stamped snapshot of the capsule’s state at the point of encoding, supporting rollback and historical integrity.
• Adaptive Replication Parameters: Based on Seigr’s Adaptive Replication model, metadata specifies replication settings to manage how capsules are distributed based on access patterns and network load.

Example of generated metadata:

FileMetadata {
   version: "1.0"
   creator_id: "unique_creator_id"
   original_filename: "data_file.bin"
   total_segments: 12
   adaptive_replication_settings: "adaptive"
}

3. Protocol Buffers Serialization

Once metadata is generated, the SeigrEncoder serializes each capsule using Protocol Buffers. This format allows efficient storage, minimal overhead, and the flexibility to expand or alter schema as the Seigr protocol evolves.

• Protocol Buffers Schema Definition: Each metadata component (e.g., FileMetadata, SegmentMetadata) is defined in a .proto schema file, ensuring consistent structure across nodes.
• CBOR Compression: For capsules that do not require human readability, CBOR is applied as a secondary compression layer to further reduce storage requirements.
• Backward Compatibility: Protocol Buffers allow schema updates without breaking compatibility, ensuring that older capsules remain functional in future versions of the protocol.

Serialization example for SegmentMetadata:

SegmentMetadata {
   segment_index: 5
   segment_hash: "abc123hash"
   primary_link: "primary_link_hash"
   secondary_links: ["secondary_link_hash1", "secondary_link_hash2"]
}

4. Senary Encoding with Adaptive Error Handling

After serialization, each segment undergoes senary encoding (base-6) to compress binary data into a space-efficient format suitable for decentralized storage. This step includes adaptive error-handling mechanisms to address potential issues during encoding and to support reliable data retrieval across nodes.

• Base-6 to Binary Conversion: The SeigrEncoder converts binary data to senary format using the Encoding Utilities module, which handles both encoding and compression.
• Checksum Validation: Each capsule is validated through checksums, ensuring error-free storage and retrieval.
• Error Recovery Integration: Capsules include redundancy to correct minor senary encoding errors. Capsules that encounter persistent issues are logged by the Immune System, triggering replication or rollback as needed.

Integration with the Immune System

The SeigrEncoder is integrated with Seigr’s Immune System, a decentralized security framework that continuously monitors capsules for signs of compromise. Capsules identified as at risk undergo additional replication or adaptive adjustments, maintaining data integrity and accessibility.

• Adaptive Replication Triggers: Capsules with high access frequency or security flags receive higher replication priority.
• Self-Healing and Rollback: The Immune System prompts the SeigrEncoder to rebuild corrupted capsules from backup paths or roll back to a secure TemporalLayer.
• Integrity Logging: Capsule integrity status is recorded for traceability and historical verification, supporting Seigr’s ethical data management model.

Demand-Based Replication and Scalability

The SeigrEncoder supports Seigr’s Demand-Based Scaling system, which adjusts capsule replication based on demand metrics and network conditions.

• Replication Scaling: Capsules replicate dynamically in response to demand, ensuring high-demand capsules remain accessible.
• Priority Encoding: Frequently accessed capsules are encoded with priority to minimize latency during encoding or retrieval.
• Self-Organizing Storage: Capsules are distributed across nodes based on access frequency, optimizing retrieval speed and load balancing.

Security and Performance Optimizations

The SeigrEncoder employs a range of security and performance optimizations to ensure data integrity and efficient handling:

• HyphaCrypt Encryption: Capsules are encrypted using HyphaCrypt to protect data at rest.
• Integrity Verification: Each segment undergoes multiple integrity checks, including hash verification and redundancy validation.
• Parallel Processing: Encoding tasks are multi-threaded, enabling the SeigrEncoder to handle high data volumes efficiently.
• Efficient Metadata Management: Protocol Buffers and CBOR serialization ensure that metadata is stored with minimal overhead, supporting scalable storage across nodes.

Practical Example: Encoding Process

Below is an example of how the SeigrEncoder processes a sample file, “data.bin,” from segmentation through encoding and final serialization:

1. Segmentation: The SeigrEncoder splits "data.bin" into 53,194-byte segments. 2. Metadata Generation: Each segment receives metadata, including primary and secondary hashes, temporal coordinates, and adaptive replication settings. 3. Serialization with Protocol Buffers: Metadata is serialized into Protocol Buffers format, and where needed, compressed with CBOR. 4. Senary Encoding: Each segment is converted to base-6 format, and redundant error-handling mechanisms are applied. 5. Output: Encoded segments are stored in a designated directory with appropriate metadata, ready for distributed storage.

Conclusion

The SeigrEncoder is a powerful, multi-functional tool in Seigr’s decentralized network. Through advanced data segmentation, senary encoding, and detailed metadata generation, it

ensures that raw data is securely transformed into efficient, accessible .seigr capsules. Integrated with adaptive replication, Immune System monitoring, and error-handling protocols, the SeigrEncoder underpins Seigr’s commitment to sustainable, scalable, and secure data management.

For additional resources, explore the following links:

• SeigrDecoder
• Seigr Metadata
• Adaptive Replication
• TemporalLayer
• Immune System
• Senary Encoding
• Protocol Buffers
• HyphaCrypt
• Integrity Module
• Encoding Utilities