VLDB Paper Topic Selection
Evaluates database research projects for PVLDB fit, categorizes paper tracks, and routes submissions across top database and systems venues.
Guides computer science and data engineering researchers through topic selection and venue routing for the International Conference on Very Large Data Bases (PVLDB/VLDB). By applying the data-management primitive test, it ensures projects present a reusable data-layer mechanism rather than purely application or model results. The skill assists authors in choosing the optimal PVLDB track—such as Regular Research, EA&B, Scalable Data Science, or Vision—and evaluates whether alternative venues like SIGMOD, ICDE, CIDR, PODS, or systems conferences offer a higher probability of publication success.
主な機能
01Categorizes papers across PVLDB tracks including Regular, EA&B, Scalable Data Science, and Vision
02Identifies mid-project re-route triggers to rescue drifting research efforts
03Applies the data-management primitive test to verify core database contributions
04Performs comparative venue routing against SIGMOD, ICDE, CIDR, PODS, and systems conferences
051,090 GitHub stars
06Assesses page budgets, reproducibility mandates, and evaluation metrics prior to writing
ユースケース
01Planning project milestones and evidence collection around PVLDB monthly submission windows
02Selecting between PVLDB Regular Research and Experiments, Analysis & Benchmark (EA&B) tracks
03Determining whether an ML or systems project fits PVLDB or should route to SIGMOD, KDD, or OSDI