arXiv:2312.14530
ZodiacEdge: a Datalog Engine With Incremental Rule Set Maintenance
원문 초록
In this paper, we tackle the incremental maintenance of Datalog inference materialisation when the rule set can be updated. This is particularly relevant in the context of the Internet of Things and Edge computing where smart devices may need to reason over newly acquired knowledge represented as Datalog rules. Our solution is based on an adaptation of a stratification strategy applied to a dependency hypergraph whose nodes correspond to rule sets in a Datalog program. Our implementation supports recursive rules containing both negation and aggregation. We demonstrate the effectiveness of our system on real and synthetic data.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in LLMs Across Logical Transformations and Question Answering Tasks
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
CROC: Evaluating and Training T2I Metrics with Pseudo- and Human-Labeled Contrastive Robustness Checks
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.