Acm Sigkdd Conference On Knowledge Discovery And Data Mining

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

Conference positioning

ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) is a top computer-science conference venue for data mining, applied data science, scalable learning, knowledge discovery, and impact-oriented analytics. It rewards a data-mining paper with novelty, scale, reproducibility, and clear real-world or scientific payoff. 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 KDD / ACM SIGKDD Conference on Knowledge Discovery and Data Mining as the target venue.
• A manuscript in data mining 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 mining, applied data science, scalable learning, knowledge discovery, and impact-oriented analytics.
• 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 KDD'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: Read reviewers as data-centric ML and discovery specialists. Novelty should appear in mining method, scale, discovery validity, or applied impact.
• Contribution hook to foreground: the venue-specific contribution bar.
• Scope vocabulary to use naturally in the abstract and introduction: data mining, applied data science, scalable learning, knowledge discovery, and impact-oriented analytics.
• Distinctive fingerprint for reviewer calibration: data, mining, applied, scalable, learning, knowledge, discovery, impact-oriented, analytics, venue-specific, contribution.
• Official anchor domain: kdd.org. Quote annual rules only after opening that source and the current-year CFP/author kit.

Close-neighbor routing guardrail

• Use this profile only when the manuscript's central contribution is genuinely in data mining and the author can say why KDD reviewers are the primary audience, not merely a convenient deadline.
• Closest roster neighbors to compare before final routing: conference-on-health-inference-and- learning (CHIL), machine-learning-for-health (ML4H), ieee-international-conference-on- data-mining (ICDM), siam-international-conference-on-data-mining (SDM). Break ties by contribution type, evidence shape, reviewer community, and the current official CFP from kdd.org.

KDD-specific routing detail

• Prefer KDD when the paper contributes a scalable discovery method, applied data-science system, mining benchmark, knowledge-discovery insight, or impact-oriented analytics result that survives strong baselines and real data complexity.
• Use ICAPS only when planning, scheduling, temporal reasoning, or planner search is the central contribution; KDD reviewers expect discovery, prediction, ranking, graph/mining, or data-centric evidence rather than solver engineering alone.
• Compare with WWW/WSDM/SIGIR for web/search/retrieval emphasis, ICDM/SDM for data-mining scope with different community fit, and ML flagships when the novelty is a general learning method rather than data-discovery or applied-scale evidence.

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 KDD, the evidence must support the venue-specific signature: a data-mining paper with novelty, scale, reproducibility, and clear real-world or scientific payoff.
• 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://kdd.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 KDD, 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 mining 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 KDD reviewers too little technical or empirical substance.

Re-routing decision

If the paper misses KDD'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] ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
[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>

Source: brycewang-stanford/Awesome-Journal-Skills → Computer-Science-Conference-Skills/skills/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining/SKILL.md