Encoder/Decoder Module

The Encoder/Decoder Module is a core component of Seigr's data ecosystem, responsible for transforming raw data into the .seigr file format and reassembling .seigr segments into their original form. This module is designed with an emphasis on modularity, data integrity, and efficient encoding, utilizing Seigr’s unique senary (base-6) encoding. It also incorporates advanced features for demand-based replication, multi-path decoding, and adaptive scalability within Seigr's decentralized architecture.

Overview

The Encoder/Decoder Module operates at the intersection of data transformation and storage. By encoding data into modular .seigr segments, it enables scalable data management across a distributed network, allowing seamless data access, resilient replication, and adaptive retrieval.

The Encoder/Decoder module is built upon the following critical functionalities:

Senary Encoding: A space-efficient encoding scheme based on a 6-base numeral system, optimizing both storage size and interoperability across Seigr’s data network.
Modular Data Segmentation: Splits data into fixed-sized capsules, enabling dynamic, decentralized storage and retrieval.
Adaptive Decoding and Reassembly: Supports flexible, multi-path data reassembly, accommodating demand-based access patterns and optimized retrieval.
Protocol Buffers Integration: Encoded segments use Protocol Buffers for metadata serialization, ensuring compatibility, backward support, and schema evolution.

Encoding Process

The encoding process involves transforming raw data into modular, senary-encoded capsules (.seigr files). Each capsule is equipped with comprehensive metadata, primary and secondary hashes, and adaptive replication parameters. This process is handled by the SeigrEncoder class, which performs the following steps:

1. Data Segmentation

The raw input data is split into fixed-size segments according to the defined TARGET_BINARY_SEGMENT_SIZE in the Seigr protocol, typically set at 53,194 bytes to accommodate space for metadata while optimizing network transfer efficiency. The data segmentation algorithm in Seigr uses the following approach:

Segment Size Determination: The encoder sets a segment size based on TARGET_BINARY_SEGMENT_SIZE and divides data accordingly.
Hashing for Uniqueness: Each segment is hashed with HyphaCrypt to create a unique, primary hash identifier.
Senary Conversion: The binary segment data is converted into base-6 (senary) format using the Encoding Utilities, preserving space and ensuring consistency across nodes.

2. Metadata Generation

Each segment is assigned a unique metadata schema that defines its identity, position, and linkage within the larger .seigr structure. The Seigr Metadata standards ensure that metadata is modular, backward-compatible, and designed for adaptive expansion:

Primary and Secondary Hash Links: Each capsule contains primary hash links (for direct segment retrieval) and secondary hash links (for multi-path adaptive retrieval), allowing flexible, non-linear data navigation.
4D Coordinate Indexing: Capsules incorporate spatial and temporal coordinates, positioning each data segment within a four-dimensional grid. This indexing is essential for Seigr’s multi-layered data structure.
Temporal Layers: For data integrity and historical tracking, each capsule maintains a TemporalLayer record. This layer stores the hash and data snapshot at each encoding state, allowing historical verification and rollback if needed.
Demand-Based Replication Parameters: Capsules contain dynamic replication settings based on Seigr’s demand-adaptive protocol. These parameters help manage the level of replication across nodes, scaling with access frequency and adjusting in response to network load.

3. Protocol Buffers Serialization

Once segmented and encoded, each capsule’s metadata is serialized into Protocol Buffers format. Protocol Buffers provide an efficient, extensible serialization system that ensures data compatibility, minimizes storage overhead, and allows for flexible schema evolution:

Metadata Serialization: Metadata for each capsule is encoded using Protocol Buffers to ensure a consistent schema across nodes.
CBOR for Efficient Compression: For capsules where human readability is unnecessary, CBOR (Concise Binary Object Representation) is used as a secondary compression layer, further reducing storage requirements.
Backward Compatibility: Protocol Buffers versioning in the Seigr Protocol ensures that capsules from different protocol versions remain interoperable.

4. Senary Encoding with Adaptive Error Handling

Senary encoding (base-6) converts binary data into a compressed format suitable for high-performance storage. Adaptive error handling mechanisms are in place to address decoding errors and network fluctuations:

Error Checking and Redundancy: Each segment undergoes checksum validation during encoding, adding redundancy to critical data paths.
Adaptive Error Recovery: Capsules include recovery data to correct minor senary encoding errors, preventing interruptions during high-load decoding sessions.
Integration with Immune System: Error handling integrates with Seigr’s immune system, logging failures and triggering replication or rollback as needed.

Decoding Process

The decoding process reassembles .seigr segments into their original form. This operation is handled by the SeigrDecoder class, which navigates multi-path data structures, verifies segment integrity, and ensures accurate reassembly.

1. Segment Retrieval

During decoding, the SeigrDecoder first retrieves the encoded .seigr files from distributed storage. It then verifies the existence and availability of all required segments:

Cluster Files Parsing: Each .seigr cluster file (stored as a Protocol Buffer structure) is parsed, and segment indices are mapped to ensure data continuity.
Multi-Path Access: Capsules with multiple retrieval paths use Seigr’s hashing system to locate secondary links if primary links are missing or corrupted.
Adaptive Retrieval Paths: By identifying high-demand segments, the decoder optimizes retrieval, accessing replicas from the most responsive nodes.

2. Integrity Verification

After retrieving the segments, the decoder performs a thorough integrity check using the primary and secondary hashes embedded within each capsule. This process is supported by the Integrity Module, which confirms that each segment remains unchanged from its original encoded state:

Hash Verification: Each segment’s primary hash is recomputed and compared against the stored hash, ensuring tamper-proof data retrieval.
Layered Integrity Checking: Capsules that include multiple temporal layers undergo layer-specific verification, confirming that historical states align with the current state.
Cross-Node Validation: For segments stored across multiple nodes, cross-validation confirms that the retrieved segments are consistent with other replicas in the network.

3. Senary Decoding

The retrieved segments, stored in senary encoding, are converted back into binary format. The Decoding Utilities module handles this transformation, ensuring efficient conversion with minimal data loss:

Base-6 to Binary Conversion: Senary data is decoded back into binary format, preserving data integrity.
Redundant Error Correction: Error correction is applied to address potential errors from the encoding process, ensuring high-fidelity data recovery.
Hybrid Decoding: For capsules that used CBOR as a compression layer, decompression is performed as part of the decoding process.

4. Segment Reassembly

After decoding, segments are reassembled in their original sequence, using metadata to ensure accurate reconstruction. This step is critical for multi-part files where segment order affects data integrity:

Ordered Assembly: The decoder uses metadata indices to order segments correctly, reconstructing the file in its original format.
Multi-Threaded Reassembly: For large data files, segments are reassembled in parallel, significantly reducing reassembly time.
Integrity Logging: All decoded segments and reconstructed files are logged in the system’s audit trail, ensuring traceability and allowing for historical validation.

Adaptive Replication and Demand Scaling

The Encoder/Decoder Module is tightly integrated with Seigr’s adaptive replication strategy, scaling capsules based on access frequency and network demand. This allows capsules to dynamically adjust replication and accessibility across nodes.

Replication Triggers: Access frequency and network status trigger replication of high-demand capsules, increasing availability on nodes with higher access loads.
Self-Healing and Rollback: Capsules deemed critical but corrupted during retrieval are rebuilt from alternative data paths or rolled back to a secure state using historical data in TemporalLayer.
Demand-Based Decoding Optimization: Capsules identified as frequently accessed are decoded with higher priority, ensuring minimal latency for essential data.

Security and Integrity Protocols

The Encoder/Decoder Module includes a suite of security and integrity protocols to safeguard data throughout encoding and decoding. These mechanisms are supported by HyphaCrypt encryption and the Immune System’s monitoring capabilities.

Encryption with HyphaCrypt: Data capsules are encrypted using HyphaCrypt, Seigr’s cryptographic protocol that enables secure, decentralized data management.
Multi-Temporal Integrity Checks: Capsules undergo hash validation across multiple temporal layers, confirming historical consistency.
Threat Detection and Immune System Response: The Immune System module continuously monitors capsules, initiating replication or rollback as needed in response to detected threats.

Performance and Efficiency

The Encoder/Decoder Module is optimized for high-performance operations within Seigr’s decentralized network. Its design minimizes data storage requirements, ensures fast encoding/decoding, and scales efficiently across nodes:

Parallel Processing: Encoding and decoding operations leverage multi-threading to reduce processing time.
Adaptive Demand Scaling: Capsules replicate dynamically, adjusting to network load and optimizing resource usage.
Efficient Metadata Management: Protocol Buffers and CBOR serialization minimize metadata size without sacrificing schema flexibility, allowing efficient data handling across distributed nodes.

Conclusion

The Encoder/Decoder Module is essential to Seigr’s decentralized data architecture, enabling efficient data transformation, adaptive replication, and resilient data retrieval. By employing advanced encoding techniques, multi-path decoding, and robust integrity protocols, this module ensures the secure and efficient handling of .seigr files across a dynamically adaptive network. This module exemplifies Seigr’s approach to ethical, scalable, and resilient data management, making it a foundational element in Seigr’s broader ecosystem.

For further technical exploration, see also: