Pith Number

pith:66IEJF3S

pith:2026:66IEJF3SC6MKKIQB5TZ7PQNXWO

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Toward Temporal Attribution Analytics in Dataflows

Chrysanthi Kosyfaki, Nikos Mamoulis, Ruiyuan Zhang, Xiaofang Zhou

Temporal attribution provides a lightweight provenance method to quantitatively track data dependencies between components in streaming dataflows over time without storing fine-grained metadata.

arxiv:2601.04722 v2 · 2026-01-08 · cs.DB

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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

We define temporal attribution, a new lightweight form of provenance, appropriate for certain tasks, such as monitoring dependencies between system components over time quantitatively.

C2weakest assumption

That a state-based indexing approach can efficiently support the five temporal provenance query types for large-scale dataflows without requiring fine-grained tuple-level dependency metadata.

C3one line summary

Temporal attribution is defined as a new lightweight provenance method using Temporal Interaction Networks to enable time-focused quantitative analysis of dataflows without tuple-level metadata.

References

70 extracted · 70 resolved · 3 Pith anchors

[1] Umut Acar, Peter Buneman, James Cheney, Jan Van den Bussche, Natalia Kwasnikowska, and Stijn Vansummeren. 2010. A graph model of data and workflow provenance 2010

[2] Daniel Alabi, Sainyam Galhotra, Shagufta Mehnaz, Zeyu Song, and Eugene Wu. 2025. Privacy and Security in Distributed Data Markets. InCompanion of the International Conference on Management of Data. 77 2025

[3] Abdullah Hamed Almuntashiri, Luis-Daniel Ibàńez, and Adriane Chapman

[4] In2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

[5] Abdullah Hamed Almuntashiri, Luis-Daniel Ibáñez, and Adriane Chapman

Formal links

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Receipt and verification

First computed	2026-05-17T23:39:16.689570Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f7904497721798a52201ecf3f7c1b7b38fac160a98e35ff62553502452fda865

Aliases

arxiv: 2601.04722 · arxiv_version: 2601.04722v2 · doi: 10.48550/arxiv.2601.04722 · pith_short_12: 66IEJF3SC6MK · pith_short_16: 66IEJF3SC6MKKIQB · pith_short_8: 66IEJF3S

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/66IEJF3SC6MKKIQB5TZ7PQNXWO \
  | 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: f7904497721798a52201ecf3f7c1b7b38fac160a98e35ff62553502452fda865

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "96a20aed031c7e09c8631a53539f35e0124555767bded44786f3dca0ea6da31f",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.DB",
    "submitted_at": "2026-01-08T08:37:09Z",
    "title_canon_sha256": "d1a9678faa6883757e524ac7093ba5bfb352725736d4e554bd2ae1b3366c9bca"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2601.04722",
    "kind": "arxiv",
    "version": 2
  }
}