Skip to content

UnivMon accuracy rule: use the paper guarantee (1±ε, failure probability 0.3) #788

Open

Description

@zzylol

Problem, by example

ASAPPlanner certifies UnivMon outputs with bounds derived for the library's earlier estimators (crates/logical-asap-optimization/src/accuracy/estimators/univmon.rs):

  • an L2 bound: a layer-0 Count Sketch Chebyshev bound plus a binomial row-failure bound (L2_ROW_FAILURE, l2_relative_error, l2_failure_probability, l2_shape);
  • a distinct-count bound: DISTINCT_VARIANCE, DISTINCT_MIN_HEAP, distinct_chebyshev, distinct_guarantee;
  • an incomplete-top-layer probability bound (incomplete_top_layer_bound, incomplete_top_layer_probability).

asap_sketchlib #221 makes UnivMon follow the SIGCOMM 2016 paper's Algorithm 2 for every query. It documents the guarantee only as the paper's source states it, Braverman–Ostrovsky (arXiv:1011.2571, Algorithm 4.1, Lemma 3.8, Theorem 4.2): the G-sum estimate is within (1 ± ε) with probability at least 0.7, i.e. failure probability at most 0.3, provided that

  • the number of layers is at least about log₂ n + 1;
  • the heap size is at least φ³/ε², where φ is the number of layers;
  • the Count Sketch estimates are within (1 ± ε).

The library takes no median of copies. The Planner's derived bounds, which give smaller failure probabilities, no longer describe what the library guarantees.

Expected

  • UnivMon's accuracy rule states exactly the paper's guarantee. It applies to L1, L2, cardinality and entropy (Stream-PolyLog g): relative error ε, failure probability 0.3, under the conditions above.
  • The sizing rule follows those conditions:
    • layers from the distinct-count bound n;
    • heap ≥ φ³/ε²;
    • a Count Sketch shape giving (1 ± ε) at that heap.
  • A target asking for failure probability below 0.3 is not met by UnivMon. The derived bounds are removed.
  • Tests and examples that select UnivMon use accuracy targets with failure probability at least 0.3.

Acceptance

  • A test plans an L2 or entropy query with ε = 0.1 and failure probability 0.3. The UnivMon candidate is valid, sized by the paper's conditions, and carries a guarantee of relative error 0.1 with failure probability 0.3.
  • The same query with failure probability 0.05 rejects UnivMon with NoAccuracyGuarantee.

Depends on asap_sketchlib #221 for the library's documented guarantee.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Metadata

Metadata

Assignees

No one assigned

    Labels

    accuracyAccuracy requirements, guarantees, and propagationenhancementNew feature or requesterror-estimation

    Type

    No type

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions