Pith Number

pith:A6XA777U

pith:2026:A6XA777UD7EWW2XLUJV477O3KW

not attested not anchored not stored refs resolved

StreamSampling.jl: Efficient Sampling from Data Streams in Julia

Adriano Meligrana

StreamSampling.jl enables unbiased sampling from data streams of unknown size in one pass while using only constant memory.

arxiv:2603.21996 v2 · 2026-03-23 · cs.SE · stat.CO

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{A6XA777UD7EWW2XLUJV477O3KW}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

StreamSampling.jl provides general and efficient methods for sampling from data streams in a single pass, even when the total number of items is unknown, while maintaining a small, constant memory footprint.

C2weakest assumption

The implemented algorithms produce unbiased samples and the reported benchmarks reflect genuine improvements without hidden implementation details or cherry-picked test cases.

C3one line summary

StreamSampling.jl implements efficient one-pass sampling algorithms for data streams in Julia with constant memory footprint and performance gains over traditional methods.

References

26 extracted · 26 resolved · 2 Pith anchors

[1] The methods covered here fall under the umbrella ofonline sampling, i.e

[2] Related Work Online sampling techniques have been implemented across a range of programming languages and frameworks, typically addressing specific use cases rather than providing a unified suite of a

[3] sequential), the sampling scheme (with vs

[4] StreamSampling.jl: Efficient Sampling from Data Streams in Julia 2026 · arXiv:2603.21996

[5] The package also providesitsample, which, similarly toStatsBase.sample, returns anArray, but, by using stream methods, it can be applied to any iterator

Formal links

1 machine-checked theorem link

Cited by

1 paper in Pith

StreamSampling.jl: Efficient Sampling from Data Streams in Julia

Receipt and verification

First computed	2026-05-17T23:38:59.576266Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

07ae0ffff41fc96b6aeba26bcffddb558b6f223a9f37d97babb859fc76ccd1af

Aliases

arxiv: 2603.21996 · arxiv_version: 2603.21996v2 · doi: 10.48550/arxiv.2603.21996 · pith_short_12: A6XA777UD7EW · pith_short_16: A6XA777UD7EWW2XL · pith_short_8: A6XA777U

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/A6XA777UD7EWW2XLUJV477O3KW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 07ae0ffff41fc96b6aeba26bcffddb558b6f223a9f37d97babb859fc76ccd1af

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "93a8a0e09889fa6756278e541eb116ae8045e39e1df12d3706ab3ed45d86b115",
    "cross_cats_sorted": [
      "stat.CO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.SE",
    "submitted_at": "2026-03-23T14:01:16Z",
    "title_canon_sha256": "1884dedc7de00abe731dbdca77ab8262610f7e3765c6291ed1867f078953f448"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.21996",
    "kind": "arxiv",
    "version": 2
  }
}