Icdt writing style

ICDT Writing Style

Write for a referee who will read the proof. An ICDT paper is judged on whether a precise theorem is correctly established and matters for data management, so the house style is the theorem-proof style of mathematical writing, disciplined into the lipics-v2021 15-page budget (excluding references) with the full details in a marked appendix. Exposition that hides the exact statement, the model, or the assumptions reads as imprecision at this venue.

The ICDT first-page arc

Lead with the result, not a survey. A strong ICDT introduction moves:

1. The data-management problem, stated precisely — what query language / constraint class / data model, and what question about it (expressiveness? evaluation complexity? decidability?).
2. Why the current state is unsatisfactory — the exact gap: an open bound, a case the prior dichotomy missed, a model whose complexity was unknown.
3. The contribution as formal statements — "We prove that evaluation for [class] is [complexity], and that this is tight," ideally with the main theorem paraphrased in the intro.
4. The technique in one breath — the proof idea a theorist can recognize (a reduction, a game argument, a normal form, a semiring homomorphism).
5. The consequence — what changes for querying, integration, or reasoning over data.

Put the formal theorem statements early and label them; a referee should find "Theorem 1" and know your result before page three.

State the models before the theorems

The commonest avoidable ICDT weakness is an under-specified setting. Before the first theorem, pin:

• The data model — relational, graph/RDF, semistructured, probabilistic, inconsistent, incomplete — and whether structures are finite (they usually are; finite model theory is the default over databases).
• The query/constraint language — CQs, UCQs, Datalog, FO, MSO, regular path queries, TGDs/EGDs — with its exact syntax and semantics, not a hand-wave.
• The computation and complexity measure — combined vs data vs query complexity, the machine model, and the complexity classes in play. "Data complexity" vs "combined complexity" is a distinction ICDT referees will hold you to.

Theorem-proof discipline within 15 pages

• Body: definitions, theorem statements, and the ideas of the proofs — enough that a referee can judge correctness and see the structure. Full proofs may be sketched here.
• Appendix (marked): the complete proofs, read at the PC's discretion. Write the body so a referee who never opens the appendix still believes the result is provable; write the appendix so one who does can certify it.
• Do not pad the body with restated related work to fill space, and do not shrink the font or margins to reclaim it — lipics-v2021 is fixed; compress by editing.

Precision conventions a referee expects

• Every symbol is introduced before use; overloaded notation across sections is a correctness hazard, not a style nit.
• Quantify claims exactly — "for every schema," "there exists a database," "in the worst case." A theory referee reads quantifier order literally.
• Match bounds explicitly — if you claim tightness, state and prove both the upper and the lower bound, and say which is which.
• Distinguish conjecture from theorem — "we conjecture" and "it is easy to see" are read literally; only write "it is easy to see" when it truly is, and give the argument if a referee might not.
• Use standard names — call it a UCQ, a guarded TGD, a semiring provenance annotation, using the community's established terminology so the referee maps you to known results.

Common failure modes

• Model-last writing — theorems before the data model is fixed, so the referee cannot tell what was proved.
• One-sided bounds sold as tight — an upper bound with no matching lower bound presented as a complete answer.
• Proof-by-citation — "the argument is similar to [12]" where the difference is exactly the hard part.
• Survey-as-introduction — three pages of background before the first formal statement, burning the 15-page budget.
• Undefined asymptotics — big-O over an unspecified parameter (data size? query size? both?).

Output format

[First-page arc] problem -> gap -> formal contribution -> technique -> data consequence: present?
[Models fixed] data model / query language / complexity measure stated before Theorem 1? yes/no
[Budget] body <=15 pages excl. refs; full proofs in the marked appendix? yes/no
[Precision] quantifiers exact / bounds matched / notation introduced: pass/fix
[Fix queue] <ordered edits>