Vldb topic selection

VLDB Topic Selection

Use this before a line is written. Two decisions hide in "let's send it to VLDB": whether the work is a data-management contribution at all, and which PVLDB category gives it the friendliest reviewer expectations.

The primitive test

VLDB rewards work whose core object is a data-management primitive: storage layout, index, query optimization or execution, transaction and consistency machinery, data integration and cleaning, streaming state, or the data infrastructure under ML. Two probes:

• Strip the application narrative. Is what remains a reusable mechanism for managing data at scale? If what remains is a model architecture or an application result, the primitive is missing.
• Would the evaluation chapter naturally measure throughput, latency, scalability, or result quality on data systems? If the natural evaluation is task accuracy alone, an ML or applied venue fits better.

Category routing inside PVLDB

Your situation	Category	Watch out
New mechanism + built system + systems evidence	Regular Research (12 pp)	The default; full evaluation burden
Rigorous measurement of existing systems, no new system	EA&B (12 pp)	Reproducibility evaluation is mandatory; conclusions must generalize
Scale-forward data-science pipeline, practice first	Scalable Data Science (8 pp)	Must still show the data-management lesson, not just an application win
Argued agenda without a full system yet	Vision (6 pp)	Small budget; needs a genuinely new direction, not a survey

Category budgets and continuation for the live volume: verify on the guidelines page before committing (see the source map's 待核实 ledger).

Neighborhood routing

Signal in the project	Better home
Quarterly-round rhythm preferred; identical scope	SIGMOD (PACMMOD rounds) — the closest sibling; pick by calendar fit and portfolio, not prestige folklore
Formal results: complexity, expressiveness, bounds	PODS or ICDT
Provocative architecture argument, prototype-grade evidence	CIDR
Solid engineering contribution, broader engineering scope	ICDE or EDBT
Mining/learning contribution where data infra is incidental	KDD or an ML venue
OS/network mechanism that happens to touch storage	SOSP/OSDI, NSDI, EuroSys
Outgrown 12 pages; wants archival depth	The VLDB Journal or TODS
Deployed production system, lessons-forward	VLDB industrial track (separate call)

The practical VLDB-vs-SIGMOD tiebreaker in this collection's experience: PVLDB's monthly gate and three-month revision suit projects whose evidence matures unpredictably; SIGMOD's fixed rounds suit groups that plan in quarters. Scope overlap is nearly total.

Commitment checklist

[ ] Primitive named in one sentence, no application words needed
[ ] Category chosen; its page budget fits the evidence plan
[ ] The one plot that would convince a builder is specified
[ ] Nearest three prior systems identified (see vldb-related-work)
[ ] If EA&B: willing and able to hand everything to the repro committee
[ ] Live volume's topics-of-interest list scanned for explicit fit

Re-route triggers mid-project

• The system never gets built → Vision now, or CIDR.
• The interesting output became the measurement study → EA&B, embrace it.
• The contribution drifted into the model, not the data path → ML venue.
• Twelve pages cannot hold the proofs → PODS split or journal lane.

Output format

[Primitive] <one sentence> / absent (re-route)
[Category] regular / EA&B / SDS / vision — with page-budget check
[Venue ranking] <top choice + two alternates, one reason each>
[Convincer plot] <the decisive figure, described>
[Risk] <novelty / evidence scale / fit — the one that kills it>
[Next action] <build, measure, reframe, or switch venue>