Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Empirical Analysis of Deep Learning for Cardinality Estimation
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1905.06425.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T10:53:42.469213Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T12:33:16.592777Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 19bd016e-b8a5-47de-951f-047969144683 · inbound
HERO: Hint-Based Efficient and Reliable Query Optimizer cites this paper.
HERO: Hint-Based Efficient and Reliable Query Optimizer An Empirical Analysis of Deep Learning for Cardinality Estimation
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:38:45.300928Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a816f255-a0e3-419d-b818-d899f541c3e9 · inbound
PLANSIEVE: Real-time Suboptimal Query Plan Detection Through Incremental Refinements cites this paper.
PLANSIEVE: Real-time Suboptimal Query Plan Detection Through Incremental Refinements An Empirical Analysis of Deep Learning for Cardinality Estimation
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T12:33:16.598519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 55557264-a9b9-4586-a6b5-2ab686dbbdf5 · inbound
Evaluating Learned Query Performance Prediction Models at LinkedIn: Challenges, Opportunities, and Findings cites this paper.
Evaluating Learned Query Performance Prediction Models at LinkedIn: Challenges, Opportunities, and Findings An Empirical Analysis of Deep Learning for Cardinality Estimation
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T10:53:42.469213Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.