Skill UI

A comprehensive guide for ensuring the reproducibility of data mining research for top-tier conferences like ICDM. It addresses the unique challenges of triple-blind review and strict page limits by detailing necessary artifacts, such as configuration files, seed lists, and anonymized packages. Learn how to achieve Rerunnable, Rebuildable, or Attested reproducibility tiers to solidify your scientific claims.

This guide details the specialized review mechanics for top data mining conferences like ICDM. It covers the triple-blind process, the unique Accept-as-Short outcome, and the no-rebuttal posture. Learn how to structure your paper and interpret complex decision packets to maximize acceptance chances.

This comprehensive guide outlines the strict requirements and protocols for submitting papers to the IEEE International Conference on Data Mining (ICDM). It details the crucial differences between the Research and Applied tracks, emphasizing the triple-blind anonymity regime. Key points include adhering to the strict 10-page IEEE format (including references and appendices), avoiding identity leaks in the body or metadata, and understanding the single, non-negotiable submission deadline. Failure to follow these guidelines results in automatic rejection.

This guide assists researchers in determining the best fit for their data mining research project. It compares the contribution against major conferences (ICDM, KDD, SDM, etc.) and helps decide the appropriate track (Research, Applied, Blue Sky) based on the nature of the mechanism, evidence, and intended scope for optimal academic impact.

This skill offers a comprehensive project management framework for the end-to-end submission process of data mining research to major conferences like ICDM. It guides users through critical milestones, including topic selection, experimental design, abstract submission, and final camera-ready preparation. It emphasizes backward planning from the final deadline, addressing unique constraints such as no-rebuttal policies, short-paper contingencies, and strict page limits.

A comprehensive guide for revising technical manuscripts intended for premier data mining conferences like ICDM. It teaches authors to shift focus from model-centric claims to data-regime-first narratives, emphasizing measurable evidence, mechanistic detail, and adhering to strict academic constraints (e.g., triple-blind format, page limits).

This guide provides rigorous guidelines for structuring theoretical papers submitted to the International Conference on Database Theory (ICDT). It emphasizes that proofs and formal evidence are the primary contribution, not benchmarks or experiments. It teaches authors how to match their specific theoretical claims (e.g., complexity, expressiveness) to the appropriate academic evidence, ensuring that any proposed experiment is proportional, honest, and merely supplements the core theoretical results.

Provides strict guidelines for writing the Related Work section in theoretical database papers. It teaches authors how to precisely position their novel results against existing theorems (ICDT/PODS canon), covering complex topics like finite model theory, query complexity, and proper academic citation etiquette, ensuring genuine novelty is highlighted.

A comprehensive audit guide for authors preparing submissions for the International Conference on Database Theory (ICDT). This guide covers critical aspects including selecting the correct submission cycle, adhering to the two-step abstract-then-paper deadline, meeting the LIPIcs formatting standards (15 pages excluding references), ensuring complete and verifiable proofs, and maintaining strict anonymity since 2024. Use this before uploading to Microsoft CMT.

A meticulous style guide for authors submitting papers to the International Conference on Database Theory (ICDT). It mandates a rigorous theorem-proof structure, emphasizing precise statement of data models, query languages, and complexity measures. The guide teaches how to structure the introduction to lead with the core result and its data-management implications, adhering to top-tier academic standards.

This guide details the process of packaging artifacts for academic conferences (ICLR), ensuring that research claims are fully reproducible and reviewable. It emphasizes providing minimal reproduction paths, maintaining data provenance, rigorously anonymizing metadata to protect authors' identities, and creating robust, permanent records for the scholarly community.

A comprehensive self-auditing guide designed for authors preparing manuscripts for submission to top-tier academic journals. It systematically addresses recurring, structural, and critical flaws across theory contribution, econometric identification, mechanism explanation, heterogeneity analysis, and writing conventions. This tool helps authors proactively identify and correct potential weaknesses before facing peer review.