Paper Citation Record · LEDGER

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems

As of 23 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2504.12074.

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

pith.paper-citation-record.v1
2504.12074 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:40:23.753328Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:58:42.017845Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:58:42.832036Z

Reference resolution

60 of 60 outbound references displayed

verified exact0
verified fuzzy49
unresolved11
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 71feb8aa-f109-453e-9112-9d32a5d54527 · outbound

This paper cites Aapche Flink,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Aapche Flink,

Reference 1

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Observation 750ee91b-58ff-4e96-aec7-450b9ea5ace2 · outbound

This paper cites A Modular Implementation of Timely Dataflow in Rust,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems A Modular Implementation of Timely Dataflow in Rust,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.347004Z

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Observation 3847ff21-851b-4d80-8de4-1f0ec82a1d53 · outbound

This paper cites Aapche Samza,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Aapche Samza,

Reference 3

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Observation fcdbdf95-8cc1-4a8a-a112-6fe7598c97cd · outbound

This paper cites Amazon Kinesis,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Amazon Kinesis,

Reference 4

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Observation 2d796447-4b96-4fd7-947c-1d27273cecde · outbound

This paper cites Aapche Hadoop,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Aapche Hadoop,

Reference 5

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Observation 6129227d-6d49-424b-9c40-6bca5678a8b2 · outbound

This paper cites Aapche Pig,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Aapche Pig,

Reference 6

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Observation de168ffd-8963-4b91-bc7f-f293afb651fb · outbound

This paper cites Aapche Hive,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Aapche Hive,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.292314Z

Source-reported events for the cited work

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Observation e3a00c9c-be4f-4fb3-8445-e27fe670ca21 · outbound

This paper cites StreamOps: Cloud-native runtime management for streaming services in bytedance,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems StreamOps: Cloud-native runtime management for streaming services in bytedance,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.281887Z

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Observation 59efcac2-7092-41ae-994f-ea3e1a96709f · outbound

This paper cites The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data pro- cessing,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data pro- cessing,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.269516Z

Source-reported events for the cited work

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Observation 675b6338-a53d-4022-855b-cea1bfa881fe · outbound

This paper cites Naiad: a timely dataflow system,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Naiad: a timely dataflow system,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.257735Z

Source-reported events for the cited work

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Observation 05e41c4e-2e21-4e09-b26b-71dd1bf733e6 · outbound

This paper cites Lightweight Asynchronous Snapshots for Distributed Dataflows.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Lightweight Asynchronous Snapshots for Distributed Dataflows

Reference 11

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Source-reported events for the cited work

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Observation 53b9c634-e4e6-410b-84ff-4d9ea34effff · outbound

This paper cites Turbine: Facebook’s service management platform for stream processing,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Turbine: Facebook’s service management platform for stream processing,

Reference 12

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Observation f8c2bb4e-8d76-43d0-9235-e915ce22c082 · outbound

This paper cites Dhalion: self-regulating stream processing in heron,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Dhalion: self-regulating stream processing in heron,

Reference 13

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Source-reported events for the cited work

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Observation ccd5e870-9e5d-48c7-b9f2-7c630607e2e3 · outbound

This paper cites Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.225487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee8b4cc2-037c-4f34-bbe9-ed016e471502 · outbound

This paper cites DRS: Auto-scaling for real-time stream analytics,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems DRS: Auto-scaling for real-time stream analytics,

Reference 15

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Source-reported events for the cited work

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Observation aa4d10ec-0f99-4f4f-b43d-502cc235c6d4 · outbound

This paper cites Elastic stream processing with latency guarantees,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Elastic stream processing with latency guarantees,

Reference 16

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Observation 41f71c8f-392c-4786-8b04-da36f3a5e3c7 · outbound

This paper cites Integrating scale out and fault tolerance in stream processing using operator state management,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Integrating scale out and fault tolerance in stream processing using operator state management,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.199564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 81fb6a8e-08c3-4b61-ba20-02d935e5ffb3 · outbound

This paper cites Stela: Enabling stream processing systems to scale-in and scale-out on-demand,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Stela: Enabling stream processing systems to scale-in and scale-out on-demand,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.188935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e09b6c6-f647-4fe1-a565-94e9ca90b6d1 · outbound

This paper cites ContTune: Continuous tuning by conservative bayesian optimization for distributed stream data processing systems,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems ContTune: Continuous tuning by conservative bayesian optimization for distributed stream data processing systems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.179042Z

Source-reported events for the cited work

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Observation 9d1ef984-0523-4633-8e54-e832a5647314 · outbound

This paper cites ZeroTune: Learned zero-shot cost models for parallelism tuning in stream processing,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems ZeroTune: Learned zero-shot cost models for parallelism tuning in stream processing,

Reference 20

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Source-reported events for the cited work

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Observation 1597975f-45e0-4c6e-9bff-03d53f70d322 · outbound

This paper cites A review of generalized zero-shot learning meth- ods,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems A review of generalized zero-shot learning meth- ods,

Reference 21

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Observation 770b22fb-0905-4467-bc01-0e19d95d1b78 · outbound

This paper cites CSI GED: An efficient approach for graph edit similarity computation,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems CSI GED: An efficient approach for graph edit similarity computation,

Reference 22

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Observation 3054004c-1464-414a-a90a-6d955c169070 · outbound

This paper cites Efficient graph similarity search over large graph databases,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Efficient graph similarity search over large graph databases,

Reference 23

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Source-reported events for the cited work

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Observation fbc93b53-71de-43de-8caa-4daa0b9229e3 · outbound

This paper cites A partition-based approach to structure similarity search,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems A partition-based approach to structure similarity search,

Reference 24

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Observation 9c16fa39-0cb1-49c6-9717-f9b2350d00ca · outbound

This paper cites How powerful are graph neural networks?.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems How powerful are graph neural networks?

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.122927Z

Source-reported events for the cited work

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Observation 841c6d23-8c82-4d20-86ff-015b710a627d · outbound

This paper cites Inductive representation learning on large graphs,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Inductive representation learning on large graphs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.112188Z

Source-reported events for the cited work

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Observation 9b4f9c58-ad0b-4c91-ae4c-68f21e9c424e · outbound

This paper cites Representation learning on graphs with jumping knowledge networks,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Representation learning on graphs with jumping knowledge networks,

Reference 27

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Source-reported events for the cited work

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Observation b7b76abd-cd99-4664-a36e-bb6f311ea02c · outbound

This paper cites Neural message passing for quantum chemistry,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Neural message passing for quantum chemistry,

Reference 28

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Source-reported events for the cited work

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Observation 20e97a09-75e3-42b8-adbe-74fafd12e695 · outbound

This paper cites Meta-gnn: On few-shot node classification in graph meta-learning,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Meta-gnn: On few-shot node classification in graph meta-learning,

Reference 29

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Observation db550ca0-b7a4-4962-bb85-7107213b2ad5 · outbound

This paper cites N-gcn: Multi- scale graph convolution for semi-supervised node classification,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems N-gcn: Multi- scale graph convolution for semi-supervised node classification,

Reference 30

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Observation ea58a47e-f512-4751-80ad-77141e0ecf74 · outbound

This paper cites Evolvegcn: Evolving graph convolutional networks for dynamic graphs,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Evolvegcn: Evolving graph convolutional networks for dynamic graphs,

Reference 31

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Observation b803bcc2-c216-4cd0-bbe3-50592084ae87 · outbound

This paper cites Link prediction based on graph neural net- works,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Link prediction based on graph neural net- works,

Reference 32

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Observation 995f01df-77c2-4d54-bee0-aa7f0af02fb0 · outbound

This paper cites Learning to rep- resent programs with graphs,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Learning to rep- resent programs with graphs,

Reference 33

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Observation 026261f6-6eeb-4913-8a76-7c0ce62de2cf · outbound

This paper cites Text level graph neural network for text classification,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Text level graph neural network for text classification,

Reference 34

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Observation ab64bdfc-d6e1-457a-9166-9f46ce8f9f3d · outbound

This paper cites One model to rule them all: Towards zero- shot learning for databases,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems One model to rule them all: Towards zero- shot learning for databases,

Reference 35

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Observation ea47599f-2ef9-42f8-8d0b-29fbf2ddfba0 · outbound

This paper cites A unified transferable model for ml-enhanced dbms,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems A unified transferable model for ml-enhanced dbms,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.026138Z

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Observation f609fc8e-3e75-4906-8cac-c8f69f92cb7f · outbound

This paper cites LlamaTune: sample-efficient dbms configuration tuning,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems LlamaTune: sample-efficient dbms configuration tuning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.015454Z

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Observation 66a59477-3690-4973-8483-ec6b642d10c4 · outbound

This paper cites The cross entropy method for classification,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems The cross entropy method for classification,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:24.005956Z

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Observation a700b150-9916-4b14-aa9b-8963456ff249 · outbound

This paper cites Certified monotonic neural networks,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Certified monotonic neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.994689Z

Source-reported events for the cited work

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Observation ed37e97f-7f95-42cc-93c9-39015a32ec78 · outbound

This paper cites Constrained monotonic neural networks,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Constrained monotonic neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.984628Z

Source-reported events for the cited work

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Observation 18a0565a-63e2-4687-8e94-e25f5b275cd7 · outbound

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Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Support-vector networks,

Reference 41

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This paper cites A training algorithm for optimal margin classifiers,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems A training algorithm for optimal margin classifiers,

Reference 42

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Source-reported events for the cited work

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This paper cites Xgboost: A scalable tree boosting system,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Xgboost: A scalable tree boosting system,

Reference 43

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Unavailable: canonical work link unavailable.

Observation 58341530-b5bf-4f80-aadc-4e9fbcff3b61 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Lightgbm: A highly efficient gradient boosting decision tree,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.960114Z

Source-reported events for the cited work

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Observation 69b6f2e1-f0d4-49c1-9f05-a1eab0f68aad · outbound

This paper cites A distance measure between attributed relational graphs for pattern recognition,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems A distance measure between attributed relational graphs for pattern recognition,

Reference 45

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Source-reported events for the cited work

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Observation e3e094c6-66a5-4b7b-a9e7-189763a7537b · outbound

This paper cites Boosting graph similarity search through pre-computation,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Boosting graph similarity search through pre-computation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.942648Z

Source-reported events for the cited work

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Observation 40e93a1c-80fb-4138-88c7-828f29a023f5 · outbound

This paper cites Comparing stars: On approximating graph edit distance,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Comparing stars: On approximating graph edit distance,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.929941Z

Source-reported events for the cited work

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Observation 6f4f4b06-220b-42a0-81d3-d4a3b17132cc · outbound

This paper cites Computing similarity between rna structures,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Computing similarity between rna structures,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.918259Z

Source-reported events for the cited work

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Observation 6bf65dac-9134-476f-be40-09abea2ef863 · outbound

This paper cites The k-means algorithm: A comprehensive survey and performance evaluation,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems The k-means algorithm: A comprehensive survey and performance evaluation,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:23.716741Z

Source-reported events for the cited work

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Observation 79a961bf-2e44-412b-9c51-a6bacab39bbe · outbound

This paper cites An median graphs: properties, algorithms, and applications,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems An median graphs: properties, algorithms, and applications,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.902096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c148bf1-d0b2-428e-a60c-37e953656e27 · outbound

This paper cites Speeding up GED verification for graph similarity search,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Speeding up GED verification for graph similarity search,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.892094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 90cb4d86-0742-4dbc-9aca-4b42f7a3c10c · outbound

This paper cites Inves: Incremental partitioning-based verification for graph similarity search.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Inves: Incremental partitioning-based verification for graph similarity search

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.883019Z

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Observation e457a310-25e7-49ec-bd0b-e3055a4cb5f5 · outbound

This paper cites An exact graph edit distance algorithm for solving pattern recognition problems,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems An exact graph edit distance algorithm for solving pattern recognition problems,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.872478Z

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Observation ef512509-5f0e-4713-a3ce-78383b1f4232 · outbound

This paper cites Nexmark benchmark,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Nexmark benchmark,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.861994Z

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Observation b6dbdbfd-a83b-4c5d-9689-2d82cb637009 · outbound

This paper cites The application of cluster analysis in strategic management research: an analysis and critique,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems The application of cluster analysis in strategic management research: an analysis and critique,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.850713Z

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Observation e11f7725-3257-4068-9162-382e10c43892 · outbound

This paper cites DS2 github repository,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems DS2 github repository,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.840125Z

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Observation 581a39df-9f58-4b81-bd68-667ff7142c79 · outbound

This paper cites Gml: effi- ciently auto-tuning flink’s configurations via guided machine learning,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Gml: effi- ciently auto-tuning flink’s configurations via guided machine learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.829593Z

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Observation c0883cea-1f36-4e25-973b-628c5fd9df3d · outbound

This paper cites Automatic database management system tuning through large-scale machine learn- ing,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Automatic database management system tuning through large-scale machine learn- ing,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.818101Z

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Observation 2d509d14-f631-4186-bef4-dd2ce5e827f5 · outbound

This paper cites Restune: Resource oriented tuning boosted by meta-learning for cloud databases,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems Restune: Resource oriented tuning boosted by meta-learning for cloud databases,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.806459Z

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Observation c7f5360a-ae44-420d-b05c-71856558623a · outbound

This paper cites An end-to-end automatic cloud database tuning system using deep reinforcement learning,.

Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems An end-to-end automatic cloud database tuning system using deep reinforcement learning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:23.794517Z

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Pith citing papers

Observation 6adb6a4e-2410-4a70-99fc-bc720f685a9c · inbound

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning cites this paper.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:58:42.984889Z

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