SSCA v7 is a layered, adaptive, lossless semantic compression system. The first four layers are MIT-licensed open source, while deeper layers (5–6) are patented core innovations, with v8 upgrades (7–9) adding scale, multimodal, and learning capabilities. Layer 0 is the proprietary “brain” that makes SSCA intelligent and adaptive for any data type and device.
The Layer Stack (in Order of Processing)
Layer 1 – Surface Parser (MIT-licensed) Converts raw input (text, JSON, XML, logs) into basic entity-relation-object triples using heuristics
Layer 2 – Structuree Extractor (MIT-licensed) Builds the initial semantic graph from triples, assigning nodes and edges with efficient ID mapping
Layer 3 – Subgraph Factor (MIT-licensed) Detects and replaces repeated subgraphs with reference nodes, reducing redundancy by 30–50%
Layer 4 – Binary Packer (MIT-licensed) Serializes the factored graph into compact binary format using varint and delta encoding
Layer 5 – Ontology Primitives (Patented – first proprietary core) Canonicalizes nodes/edges to deep semantic primitives and applies differential encoding for maximum ratio
Layer 6 – Handover Manager (Patented – combined proprietary) Intelligently routes data to the optimal compression path based on type, graph density, and entropy
Layer 7 – Streaming Mode (v8 upgrade) Processes large data in chunks with incremental graph merging and disk-backing for petabyte-scale
Layer 8 – Multimodal Extensions (v8 upgrade) Integrates scene graph extraction from images, video, and audio for visual/audio compression
Layer 9 – Dynamic Ontology Learning (v8 upgrade – DNA/PO3 style) Trains and evolves custom primitives on user data for 5–15% additional compression gains
Layer 0 – Intelligent Data Analyzer (Proprietary add-on – the “brain”) Detects device constraints and data types, creates/stores custom parsers on-the-fly, recognizes pre-compressed data to bypass, and configures the entire stack for optimal efficiency
30+ Data Types Recognized by Layer 0 (Grouped by Category)
Layer 0 automatically identifies these data types (and can create new parsers when needed):
Structured Text & Markup (Core Formats)
JSON
XML
CSV
YAML
TOML
Log & Telemetry Streams
Apache access logs
Syslog
JSON telemetry (Tesla FSD style)
Structured event logs
Starlink/satellite telemetry
Server performance metrics
Social & Messaging Data
Social media posts/threads (X, TruthSocial, Rumble comments)
Chat transcripts (timestamped)
Forum discussions
Email threads
Document & Scanned Content
Scanned PDFs (OCR-ready)
Image-based documents
Mixed PDF (text + images)
Scientific & Technical Formats
Spike data (Neuralink style)
Sensor readings (IoT)
Time-series data
CSV scientific datasets
Binary protocol buffers (protobuf)
Web & API Data
HTML fragments
API responses (REST/GraphQL)
Webhook payloads
RSS/Atom feeds
Miscellaneous / Custom
Key-value pairs
Custom log formats
Mixed structured/unstructured
Pre-compressed data (recognized & bypassed)
Unknown/edge-case formats (parser created on-the-fly)
This list is already comprehensive for most enterprise, social, AI, and telemetry use cases — Layer 0 will automatically handle these and grow the parser library with continued use.