🌟 SSUM-STAR — Structural Time And Replay

A New Form of Data Compression: Compressing Time, Not Bytes (Exact Replay)

This is a complete, runnable system — not a concept:

offline, deterministic, lossless structural compression with indexed seek and exact replay,

released with full source code and documentation.

(ASCII-only, reproducible, domain-agnostic)

🔁 For decades, compression systems have focused on shrinking bytes.

But none were designed to answer a deeper question:

Can history itself be compressed — without losing truth, order, or replayability?

SSUM-STAR (Structural Time And Replay) answers this by doing something fundamentally different.

It does not compress data.
It compresses time itself — transforming datasets into deterministic, replayable structural timelines.

And yes — the full source code, scripts, and reproducible cases are publicly available.

⚙️ What Is SSUM-STAR?

SSUM-STAR is a deterministic, offline structural compression and replay system built on Shunyaya Structural Universal Mathematics (SSUM).

Unlike classical compression tools that operate on serialized bytes, SSUM-STAR operates on invariant-preserving state transitions.

The result is a new kind of artifact:

  • not just compressed
  • not just stored
  • but trustworthy as history itself

The .star file is not a container.

The .star file is the timeline.

🧠 The Core Idea (In One Line)

Classical systems assume:

data + timestamps + metadata -> meaning

SSUM-STAR demonstrates:

data + structure -> time + meaning

Time is no longer stored.
Time emerges structurally.

🔐 The Non-Negotiable Invariant

SSUM-STAR is built on a single, strict guarantee:

decode(encode(structure)) == structure

Under SSUM collapse:

phi(decode(encode(structure))) == classical_data

This guarantees:

  • zero approximation
  • zero drift
  • zero reordering
  • zero semantic loss

Replay is provable, not heuristic.

🔁 Exact Replay (Not Approximate Reconstruction)

Replay in SSUM-STAR is deterministic unfolding:

state_0 + delta_1 + delta_2 + ... + delta_n

Which means:

  • identical replay across machines
  • identical replay years later
  • identical replay with or without indexes

Missing data stays missing.
Faults remain faults.
Nothing is interpolated. Nothing is fabricated.

🧱 Structural Compression vs Classical Compression

Classical compression focuses on:

  • byte patterns
  • statistical redundancy
  • size reduction

It does not preserve:

  • intrinsic order
  • replay semantics
  • structural continuity
  • auditability

SSUM-STAR focuses on:

  • invariant preservation
  • deterministic ordering
  • structural time
  • exact replay

Compression happens only if structure allows it — never at the expense of truth.

🗂 Architecture: Truth vs Convenience

SSUM-STAR enforces a hard separation:

Structural Timeline — .star

  • authoritative
  • replayable
  • auditable
  • sufficient on its own

Index — .star.idx

  • optional
  • disposable
  • rebuildable
  • never authoritative

Design rule:

truth -> .star
navigation -> .star.idx

Indexes accelerate seek.
They never define correctness.

🔍 Deterministic Indexed Seek

SSUM-STAR supports deterministic seek:

  • by row index
  • by structural time

Seek works by:

  • resolving the nearest anchor
  • replaying deterministically forward

Even without byte offsets, replay remains correct.

Safety always overrides speed.

📊 Benchmarks & Evidence (Executed, Reproducible)

SSUM-STAR ships with executed, real-world datasets, not synthetic demos.

Verified across all cases:

  • decode(encode(data)) == data
  • exact replay from any position
  • deterministic results across machines and time

Representative cases included in the release:

  • Financial time series (10K+ rows)
    Stable cadence data showing strong structural compression with exact replay.
  • Sensor telemetry (~7K rows)
    Gaps, faults, and missing values preserved exactly — nothing repaired or smoothed.
  • Infrastructure telemetry (200K+ rows, minute resolution)
    Large-scale dataset compressed to a fraction of raw size while maintaining full replay correctness.
  • Irregular event logs (50K rows)
    High-entropy data with minimal compression — correctness and auditability preserved by design.

Key observation:
Compression ratios vary by structure.
Replay fidelity, determinism, and audit correctness do not.

SSUM-STAR optimizes for truth first — size reduction is a consequence, not a requirement.

📦 What You Get (Fully Open)

The SSUM-STAR release includes:

  • full source code
  • deterministic execution scripts
  • Quickstart guide
  • FAQ
  • detailed architecture document
  • executed benchmark cases

Everything runs:

  • offline
  • without randomness
  • without external dependencies
  • without databases or clocks

🌍 Why SSUM-STAR Matters

SSUM-STAR demonstrates that:

  • compression does not require loss
  • time does not need to be stored
  • replay does not require metadata
  • audit does not require databases

It introduces a new class of artifact:

A compressed object that can be trusted as history itself.

🚀 How SSUM-STAR Redefines Compression

Most compression systems answer only one question:

How small can this file be?

SSUM-STAR answers a different one:

Can this compressed artifact still be trusted as history?

This matters because modern systems increasingly require:

  • long-term auditability
  • deterministic replay
  • offline verification
  • reproducible science
  • regulatory traceability

SSUM-STAR enables:

  • auditable compressed archives
  • replayable scientific datasets
  • deterministic research pipelines
  • offline, long-horizon storage with provable correctness

It reframes compression from storage optimization to historical preservation.

This is not an incremental improvement.

It is a new class of compression artifact. 

📂 Repository & Source Code

Complete source code, scripts, and documentation:
https://github.com/OMPSHUNYAYA/SSUM-STAR

Master Index — Shunyaya Symbolic Mathematics:
https://github.com/OMPSHUNYAYA/Shunyaya-Symbolic-Mathematics-Master-Docs

Blogs:
https://shunyaya.blogspot.com
https://shunyaya.blog

📜 License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Attribution is satisfied by referencing the project name SSUM-STAR.

No warranty; use at your own risk.
No redistribution of third-party raw data unless the original license explicitly permits it.

🏁 Conclusion

SSUM-STAR shows that time itself can be compressed —
without approximation, without drift, and without losing truth.

Deterministic.
Offline.
Auditable.
Replay-exact.

A structural rethinking of what compression really means.

Disclaimer

Research and observation only.

Not intended for real-time control, safety-critical, medical, financial, legal, or operational decision-making. 

OMP