ieee-international-conference-on-data-engineering - SKILL.md Agent Skill

name: ieee-international-conference-on-data-engineering description: Use when targeting IEEE International Conference on Data Engineering (ICDE) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for data engineering.

IEEE International Conference on Data Engineering (ICDE)

Conference positioning

IEEE International Conference on Data Engineering (ICDE) is a top computer-science conference venue for data engineering, database systems, data streams, graph data, cloud data management, and analytics systems. It rewards a data-engineering paper with system or algorithm evidence on realistic data workloads. Treat this skill as a fit / venue-selection / re-framing tool for conference submission strategy, not as a substitute for the current year's CFP, author kit, ethics policy, or submission portal.

Because CS conferences change deadlines, templates, page limits, review workflow, artifact rules, AI-use policy, and rebuttal formats every cycle, always verify the live official instructions before making a submission-ready recommendation. Start from the official source anchor recorded for this venue in ../../resources/conference-roster.md and ../../resources/official-source-map.md.

When to trigger

The author names ICDE / IEEE International Conference on Data Engineering as the target venue.
A manuscript in data engineering needs a conference-fit read before being formatted or submitted.
The paper must be re-framed from journal style or arXiv style into a selective CS conference narrative.
The author needs an evidence-gap, anonymity, artifact, rebuttal, or re-routing diagnosis for this venue.

Scope & topic fit

Core fit: data engineering, database systems, data streams, graph data, cloud data management, and analytics systems.
Best submissions make a precise contribution type visible: algorithm, theorem, system, dataset, benchmark, empirical finding, design artifact, tool, or socio-technical analysis.
The paper should explain why the result matters to ICDE's reviewers, not just why it is interesting to the authors' lab or product context.
Position related work against the most recent conference-cycle papers in this venue and its closest siblings; stale comparisons are a common early-review weakness.
If the contribution is interdisciplinary, state which part is CS research and which part is domain evidence.

Venue-specific calibration

Reviewer lens: Treat ICDE as a data engineering venue whose reviewers expect the scope and evidence to match its own community. Do not submit a generic CS paper until the introduction names the exact subcommunity, contribution type, and proof or empirical standard.
Contribution hook to foreground: the venue-specific contribution bar.
Scope vocabulary to use naturally in the abstract and introduction: data engineering, database systems, data streams, graph data, cloud data management, and analytics systems.
Distinctive fingerprint for reviewer calibration: data, engineering, database, streams, graph, cloud, management, analytics, venue-specific, contribution, ieee-icde.
Official anchor domain: ieee-icde.org. Quote annual rules only after opening that source and the current-year CFP/author kit.

Close-neighbor routing guardrail

Route to ICDE when the contribution is data engineering: scalable data systems, processing, integration, storage, data pipelines, or applied data-management infrastructure.
Do not collapse ICDE into SIGMOD/VLDB or data-mining venues. Pick ICDE when engineering scale and data-system construction are central.

ICDE-specific routing detail

Prefer ICDE when the contribution is data engineering infrastructure: query processing, storage engines, transactions, data integration, streaming, indexing, provenance, or scalable data management.
Route visual/signal recognition methods to ICPR, ontology/knowledge acquisition to K-CAP, and semantic-web/linked-data contributions to ISWC unless the data-system mechanism is central.
ICDE evidence should include system architecture, workload realism, scalability, correctness or consistency properties, and comparisons against mature database/data-management baselines.

Method & evidence bar

Compare against current strong baselines and explain exactly what changes in the algorithm, objective, data, or inference procedure.
Report ablations that isolate the claimed mechanism; do not rely on aggregate benchmark wins alone.
Document data, compute, hyperparameters, model selection, and failure cases so the result can be reviewed as science rather than demo output.
For ICDE, the evidence must support the venue-specific signature: a data-engineering paper with system or algorithm evidence on realistic data workloads.
Include limitations, negative results, compute/resource reporting, data provenance, and ethics details when they affect the claim.

Structure & house style

Frame the contribution as a reusable idea: method, theory, benchmark, dataset, system, or socio-technical finding.
Separate main claims from exploratory results; reviewers at top AI venues punish overclaiming and hidden cherry-picking.
Use the current official template exactly; do not guess page limits, font sizes, supplement rules, anonymity exceptions, or camera-ready requirements from old cycles.
The introduction should answer: problem, why now, what is new, why this venue, and what evidence proves the claim.
Put the strongest result in the main paper, not only in the appendix or supplement; reviewers should not have to reconstruct the contribution.

Official-cycle checklist

Open the live official venue page: https://ieee-icde.org/
Re-check the current cycle's CFP, author kit, submission system, abstract/paper deadlines, page limits, supplementary-material rules, anonymity policy, dual-submission policy, ethics policy, AI-use policy, artifact/code/data expectations, rebuttal/author-response format, and camera-ready requirements.
Confirm the review workflow and portal: OpenReview / CMT / HotCRP / PCS / START or society portal, as specified for the current cycle.
Check whether accepted papers require in-person presentation, separate registration, artifact badges, proceedings copyright, or post-acceptance release forms.
If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

One sentence states why this manuscript belongs at ICDE, using the venue's scope rather than generic "top conference" language.
The claim is calibrated to the evidence: no broader than the datasets, proofs, systems, user studies, deployments, or threat model support.
Related work includes the nearest current-cycle data engineering papers and explains the technical delta.
The paper satisfies the current official template, anonymity, ethics, artifact, and rebuttal requirements.
The main paper is self-contained enough for reviewers to evaluate novelty and correctness without hunting through external links.

Common desk-reject triggers

Leaderboard-only novelty with weak explanation of why the method works.
Unclear data contamination, missing baselines, or evaluation that cannot be reproduced.
Claims about safety, fairness, health, or society without matching evidence and limitations.
Formatting, anonymity, dual-submission, external-link, or supplement violations under the current-year policy.
A contribution framed for a neighboring field while giving ICDE reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses ICDE's bar, compare against neural-information-processing-systems / international-conference-on-machine-learning / international-conference-on-learning-representations / aaai-conference-on-artificial-intelligence. Re-route based on contribution type, not prestige: theory to a theory venue, systems to a systems venue, application-heavy work to a domain venue, and early ideas to workshops or shorter tracks when the official CFP supports them.

Output format

[Fit] High / Medium / Low (one-line reason)
[Target] IEEE International Conference on Data Engineering (ICDE)
[Contribution type] algorithm / theory / system / dataset / benchmark / empirical / design / security / other
[Main evidence gap] <single most important missing proof, experiment, study, artifact, or policy check>
[Official items to re-check] CFP / author kit / deadline / format / anonymity / ethics / AI-use / artifact / rebuttal / camera-ready
[Top rejection risk] <venue-specific risk>
[Re-route suggestion] <better-matched conference or journal if not a fit>