Reasoning over Streaming Data in Metric Temporal Datalog
Authors: Przemysław Andrzej Wałęga, Mark Kaminski, Bernardo Cuenca Grau3092-3099
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a sound and complete stream reasoning algorithm that is applicable to a fragment datalog MTLFP of datalog MTL, in which propagation of derived information towards past time points is precluded. We finally study the data complexity of standard query answering, which is in known to be in EXPSPACE and P-hard for datalog MTL (Brandt et al. 2018). We show tight P and PSPACE bounds for datalog MTLFP rule sets without punctual intervals under the assumption that numbers are encoded in unary and binary, respectively. |
| Researcher Affiliation | Academia | Przemysław Andrzej Wał ega,1,2 Mark Kaminski,1 Bernardo Cuenca Grau1 1Department of Computer Science, University of Oxford, UK 2Institute of Philosophy, University of Warsaw, Poland |
| Pseudocode | Yes | Algorithm 1 A generic stream reasoning algorithm. Parameters: Query (Π, Q), discrete X Q 0, step Q>0 Input: Stream I 1: t := step current time point 2: Set w to the maximal number in Π window size 3: W := and H := memory 4: loop: 5: if succ I(t) t + step then 6: tnext := succ I(t) and W := W I(tnext) 7: else 8: tnext := t + step 9: Exhaustively apply the rules from Table 2 10: for Q(c)@ϱ W do 11: if t ϱ (t, tnext] X then 12: stream out Q(c)@t 13: Exhaustively apply the rules from Table 3 14: t := tnext |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | The paper uses abstract concepts of 'stream I' and 'dataset D' for theoretical analysis and does not mention or provide access information for any specific publicly available dataset. |
| Dataset Splits | No | The paper focuses on theoretical algorithms and complexity analysis, and therefore does not include specific dataset split information (e.g., train/validation/test splits). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical algorithms and complexity analysis, and therefore does not include specific experimental setup details like hyperparameter values or training configurations. |