For insert-only IVM of self-join-free conjunctive queries over semirings without additive inverses (natural, provenance, covariance, tropical), amortized constant update time plus constant delay holds iff the query is α-acyclic p-hierarchical.
Recent increments in incremental view maintenance
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
4
Pith papers citing it
1
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 4roles
background 1polarities
background 1representative citing papers
ExplAIner is a layered first-order logic that expresses major XAI explanation queries over Boolean models with evaluation in the Boolean hierarchy and computation in FP^NP.
The paper develops a general incremental maintenance technique for arbitrary join queries that achieves update times bounded by an optimizable maintenance width using heavy-light partitioning.
Proposes Governed Evolving Memory (GEM) as a state-trajectory workload for long-term AI agent memory using four operators and six correctness conditions that record-level systems cannot satisfy.
citing papers explorer
-
The Role of Semirings in Incremental View Maintenance
For insert-only IVM of self-join-free conjunctive queries over semirings without additive inverses (natural, provenance, covariance, tropical), amortized constant update time plus constant delay holds iff the query is α-acyclic p-hierarchical.
-
ExplAIner: A Declarative Query Language for Explaining Classification Models
ExplAIner is a layered first-order logic that expresses major XAI explanation queries over Boolean models with evaluation in the Boolean hierarchy and computation in FP^NP.
-
Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations
The paper develops a general incremental maintenance technique for arbitrary join queries that achieves update times bounded by an optimizable maintenance width using heavy-light partitioning.
-
Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory
Proposes Governed Evolving Memory (GEM) as a state-trajectory workload for long-term AI agent memory using four operators and six correctness conditions that record-level systems cannot satisfy.