Research map / Learning theory and algorithms / Combinatorial and search optimization
Modular Materialisation of Datalog Programs
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use modular materialisation with specialised transitive and symmetric-transitive closure algorithms | Datalog materialisation · Claros-LE | materialisation time (seconds): 733.55 -> 3593.32 | Helped |
| Use modular materialisation with specialised transitive and symmetric-transitive closure algorithms | Datalog materialisation · LUBM-LE | materialisation time (seconds): 291.90 -> 1100.22 | Helped |
| Use modular materialisation with specialised transitive and symmetric-transitive closure algorithms | Datalog materialisation · DBpedia-SKOS | materialisation time (seconds): 103.23 -> 3623.37 | Helped |
| Use modular materialisation with specialised transitive and symmetric-transitive closure algorithms | Datalog materialisation · DAG-R | materialisation time (seconds): 29.60 -> 3238.86 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · Claros-LE, small deletions | incremental maintenance time (seconds): 0.93 -> 1035.28 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · LUBM-LE, small deletions | incremental maintenance time (seconds): 0.32 -> 3.87 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · DBpedia-SKOS, small deletions | incremental maintenance time (seconds): 21.77 -> 691.32 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · DAG-R, small deletions | incremental maintenance time (seconds): 64.92 -> 3005.11 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · Claros-LE, small insertions | incremental maintenance time (seconds): 0.17 -> 0.80 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · LUBM-LE, small insertions | incremental maintenance time (seconds): 0.01 -> 0.01 | No clear effect |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · DBpedia-SKOS, small insertions | incremental maintenance time (seconds): 0.20 -> 2.78 | Helped |
| Use modular incremental maintenance with specialised closure algorithms | DRed^c · DAG-R, small insertions | incremental maintenance time (seconds): 14.56 -> 116.78 | Helped |
Closest papers by meaning
- Materialisation-Based Reasoning in DatalogMTL with Bounded Intervals (AAAI 2023 · similarity 0.91)
- Optimised Storage for Datalog Reasoning (AAAI 2024 · similarity 0.91)
- Goal-Driven Reasoning in DatalogMTL with Magic Sets (AAAI 2025 · similarity 0.85)
- Computing the Why-Provenance for Datalog Queries via SAT Solvers (AAAI 2024 · similarity 0.84)
- Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problems (ICLR 2026 · similarity 0.83)
- ORACLE: Optimizing Reasoning Abilities of Large Language Models via Constraint-Led Synthetic Data Elicitation (AAAI 2026 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.