Skill UI

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.

A comprehensive guide for structuring and refining high-quality database systems research papers for top-tier conferences like SIGMOD. It covers essential components such as establishing a clear page-one contract, integrating running examples, scoping measured system claims, adopting third-person voice, and adhering to the 12-page ACM template structure for maximum technical impact.

This comprehensive guide outlines best practices for achieving high levels of scientific reproducibility in systems papers, especially for cloud measurement studies. It teaches authors how to map every claim in their paper to verifiable evidence, detailing the entire testbed setup, reproducing tail latency and costs accurately, and maintaining consistency between reported numbers and artifacts.

This guide details the structured process for preparing and evaluating research artifacts for major academic conferences like SOSP. It covers badge targeting (Functional, Reusable), cooperative evaluation expectations, and best practices for packaging system-level artifacts, including defining tiers (runnable, provided-hardware, documented-only) and conducting 'kick-the-tires' smoke tests.

A comprehensive methodology for rigorous scientific research in operating systems and complex systems. This guide details how to pin environments (OS, firmware, hardware), automate data capture, and script all figures to ensure that experimental results are auditable and fully reproducible, protecting findings against review challenges.

A comprehensive guide for authors submitting to high-impact scientific journals. It outlines the essential principles for structuring experiments and presenting evidence, emphasizing claims-evidence matching, fair benchmarking, differentiating result sources (simulation, hardware, theory), and ensuring statistical rigor. Covers best practices across computer science, control, communication, and microelectronics.

This guide assists researchers in determining the optimal venue for their formal verification project. It helps decide whether the work belongs at TACAS (ETAPS) or a sibling conference (like CAV or VMCAI), depending on the emphasis (tool vs. theory), and guides the selection among the four distinct contribution categories (Research, Tool, Case-study, Demonstration).

This comprehensive guide is designed to help authors assess the methodological and theoretical suitability of their manuscript for Transportation Research Part B. It emphasizes that the core contribution must be a generalizable method, model, or theorem (e.g., in traffic flow, network optimization, or choice modeling), rather than a simple applied case study. Use this tool for venue selection, manuscript re-framing, and ensuring rigorous analytical justification.

A comprehensive guide for optimizing computer science manuscripts targeting the USENIX Annual Technical Conference (ATC). It covers core systems research fields (OS, networking, storage, security), defining the required evidence bar (built artifacts, clean evaluation), and re-framing the paper's contribution to align with top-tier systems venue expectations.

A specialized guide for assessing whether a storage systems or file system manuscript fits the USENIX FAST conference. It covers venue positioning, required evidence (realistic workloads, fault models), appropriate framing, and comparison methodologies necessary for top-tier systems conference submissions.

A rigorous guide for drafting high-stakes, technical security papers for the USENIX Security Symposium. It mandates a threat-model-first structure, requiring authors to quantify all security claims with measurable data to avoid hyperbole. The guide also details integrating responsible disclosure narratives and adhering to strict academic formatting rules.

This comprehensive guide helps researchers determine if their project qualifies as a core data-management primitive, which specific PVLDB category (Regular, EA&B, Scalable Data Science, Vision) it belongs to, and whether a major data conference like VLDB or SIGMOD is the optimal submission venue. It provides a rigorous primitive test and detailed routing advice for top-tier data systems research.