Skill UI

This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.

A comprehensive guide to the rigorous, multi-stage peer review process for top-tier data engineering conferences like ICDE. It details the two-round structure, area-chair coordination, single-blind dynamics, and the high standards set by 'builder-heavy' reviewers. Use this to strategize paper submission, understand key evaluation dimensions (novelty, soundness, evaluation), and anticipate common review objections.

A comprehensive audit checklist designed for authors submitting data-engineering systems papers to major conferences like ICDE. It covers critical areas including IEEE formatting standards, round selection strategy, Microsoft CMT conflict declarations, and the requirement for supplemental material, ensuring authors mitigate common desk-reject risks.

A comprehensive guide for authors preparing submissions for ICDE conferences. It details how to properly segment content into the main 12-page body versus the supplementary material, ensuring the core argument remains self-contained while providing reviewers with runnable artifacts, extended experiments, and full technical documentation.

A comprehensive project management guide for submitting data-intensive systems papers to ICDE. It details the two-round submission cycle, setting milestones from initial evidence locking (mechanisms, workloads) to final IEEE Xplore publication. This workflow helps researchers backward-plan the entire data-engineering lifecycle, budgeting time for revisions and mitigating common submission risks.

A comprehensive guide for authors submitting data-intensive systems papers to top-tier conferences (like ICDE). It details how to structure the paper, ensure technical claims are reconstructable, quantify all performance gains with associated costs, and compress complex ideas into a limited page budget while maintaining scholarly rigor.

Provides comprehensive guidelines for packaging research artifacts (code, data, logs) for submission to data mining conferences like ICDM. The focus is on ensuring the artifact is completely anonymous for triple-blind review (scrubbing Git history, paths, and internal names) and maximizing reproducibility, allowing reviewers to verify results with minimal effort.

A comprehensive guide for authors preparing submissions for top-tier data mining conferences (e.g., ICDM). It emphasizes the critical strategy of preemptively addressing potential weaknesses—such as baselines, variance, scaling, and data leakage—directly within the submitted manuscript, as author rebuttals are often not permitted. Also provides strict guidelines for drafting anonymous, high-impact responses if a rebuttal window is available.

This guide outlines the complex process of converting an accepted, triple-blind academic paper into the final, professional camera-ready version required by IEEE conferences (e.g., ICDM). Key steps include de-anonymizing the text by restoring author names, affiliations, and acknowledgements; compressing the content if accepted as a short paper; adhering to strict IEEE formatting and page limits; completing electronic copyright transfer (eCopyright); and registering for mandatory in-person presentation.

A comprehensive guide for researchers designing and auditing empirical evaluations for data mining papers (e.g., ICDM). It teaches how to structure a trustworthy evaluation by defining operational tasks, conducting ablation studies to isolate mechanisms, proving scalability, and implementing known-truth checks to ensure findings are valid and not mere artifacts.

A comprehensive guide detailing how to structure the 'Related Work' section for submissions to top data mining conferences like ICDM. It emphasizes positioning the research based on core mechanisms and mining tasks, rather than simply listing results. The guide also provides critical advice on maintaining triple-blind anonymity, verifying citation venues (avoiding misattribution traps), and ensuring the novelty claim is distilled into a single, quotable mechanism-contrast sentence.

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.