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Paper Citation Record · LEDGER

Deep Learning for Spatio-Temporal Data Mining: A Survey

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1906.04928.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1906.04928 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:24:47.762015Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

33
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation af9ff887-9cd7-4e82-8e92-934026bacde9 · inbound

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach cites this paper.

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach Deep Learning for Spatio-Temporal Data Mining: A Survey

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:54:59.525732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T22:54:58.440541Z digest=sha256:e657c9bcaa4bc37d4502b12481f97f35968ba25ba2140ab805e6f2a6a1d755bb

Observation a3f549b7-5cd6-4640-b5d6-854c769fb8e6 · inbound

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods cites this paper.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Deep Learning for Spatio-Temporal Data Mining: A Survey

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T16:24:47.762015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:24:47.762015Z digest=sha256:9c9562039d9e0991e5bcb3949652a6cb27a01da01ee25ed184afe1ae72d167f7