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Pan Hu

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9 papers
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9

AAAI Conference 2026 Conference Paper

Incremental Maintenance of DatalogMTL Materialisations

  • Kaiyue Zhao
  • Dingqi Chen
  • Shaoyu Wang
  • Pan Hu

DatalogMTL extends the classical Datalog language with metric temporal logic (MTL), enabling expressive reasoning over temporal data. While existing reasoning approaches, such as materialisation-based and automata-based methods, offer soundness and completeness, they lack support for handling efficient dynamic updates—a crucial requirement for real-world applications that involve frequent data updates. In this work, we propose DRedMTL, an incremental reasoning algorithm for DatalogMTL with bounded intervals. Our algorithm builds upon the classical Delete/Rederive (DRed) algorithm, which incrementally updates the materialisation of a Datalog program. Unlike a Datalog materialisation which is in essence a finite set of facts, a DatalogMTL materialisation has to be represented as a finite set of facts plus periodic intervals indicating how the full materialisation can be constructed through unfolding. To cope with this, our algorithm is equipped with specifically designed operators to efficiently handle such periodic representations of DatalogMTL materialisations. We have implemented this approach and tested it on several publicly available datasets. Experimental results show that DRedMTL often significantly outperforms rematerialisation, sometimes by orders of magnitude.

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AAAI Conference 2025 Conference Paper

Goal-Driven Reasoning in DatalogMTL with Magic Sets

  • Shaoyu Wang
  • Kaiyue Zhao
  • Dongliang Wei
  • Przemysław Andrzej Wałęga
  • Dingmin Wang
  • Hongming Cai
  • Pan Hu

DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applications, including tasks from industrial and financial sectors. However, due its high computational complexity, practical reasoning in DatalogMTL is highly challenging. To address this difficulty, we introduce a new reasoning method for DatalogMTL which exploits the magic sets technique—a rewriting approach developed for (non-temporal) Datalog to simulate top-down evaluation with bottom-up reasoning. We have implemented this approach and evaluated it on publicly available benchmarks, showing that the proposed approach significantly and consistently outperformed state-of-the-art reasoning techniques.

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AAAI Conference 2024 Conference Paper

Optimised Storage for Datalog Reasoning

  • Xinyue Zhang
  • Pan Hu
  • Yavor Nenov
  • Ian Horrocks

Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts. However, storing all materialised facts may be infeasible in practice, especially when the rules are complex and the given set of facts is large. We observe that for certain combinations of rules, there exist data structures that compactly represent the reasoning result and can be efficiently queried when necessary. In this paper, we present a general framework that allows for the integration of such optimised storage schemes with standard materialisation algorithms. Moreover, we devise optimised storage schemes targeting at transitive rules and union rules, two types of (combination of) rules that commonly occur in practice. Our experimental evaluation shows that our approach significantly improves memory consumption, sometimes by orders of magnitude, while remaining competitive in terms of query answering time.

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IJCAI Conference 2023 Conference Paper

Enhancing Datalog Reasoning with Hypertree Decompositions

  • Xinyue Zhang
  • Pan Hu
  • Yavor Nenov
  • Ian Horrocks

Datalog reasoning based on the seminaive evaluation strategy evaluates rules using traditional join plans, which often leads to redundancy and inefficiency in practice, especially when the rules are complex. Hypertree decompositions help identify efficient query plans and reduce similar redundancy in query answering. However, it is unclear how this can be applied to materialisation and incremental reasoning with recursive Datalog programs. Moreover, hypertree decompositions require additional data structures and thus introduce nonnegligible overhead in both runtime and memory consumption. In this paper, we provide algorithms that exploit hypertree decompositions for the materialisation and incremental evaluation of Datalog programs. Furthermore, we combine this approach with standard Datalog reasoning algorithms in a modular fashion so that the overhead caused by the decompositions is reduced. Our empirical evaluation shows that, when the program contains complex rules, the combined approach is usually significantly faster than the baseline approach, sometimes by orders of magnitude.

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AAAI Conference 2022 Conference Paper

MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

  • Dingmin Wang
  • Pan Hu
  • Przemysław Andrzej Wałęga
  • Bernardo Cuenca Grau

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a. k. a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.

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AIJ Journal 2022 Journal Article

Modular materialisation of Datalog programs

  • Pan Hu
  • Boris Motik
  • Ian Horrocks

Answering queries over large datasets extended with Datalog rules plays a key role in numerous data management applications, and it has been implemented in several highly optimised Datalog systems in both academic and commercial contexts. Many systems implement reasoning via materialisation, which involves precomputing all consequences of the rules and the dataset in a preprocessing step. Some systems also use incremental reasoning algorithms, which can update the materialisation efficiently when the input dataset changes. Such techniques allow queries to be processed without any reference to the rules, so they are often used in applications where the performance of query answering is critical. Existing materialisation and incremental reasoning techniques enumerate all possible ways to apply rules to the data in order to derive all relevant consequences. This, however, can be inefficient because derivations of rules commonly used in practice are redundant; for example, rules axiomatising a binary predicate as symmetric and transitive can have a cubic number of applications, yet they can derive at most a quadratic number of facts. Such redundancy can be a significant source of overhead in practice and can prevent Datalog systems from successfully processing large datasets. To address this issue, in this paper we present a novel framework for modular materialisation and incremental reasoning. Our key idea is that, for certain combinations of rules commonly used in practice, all consequences can be derived using specialised procedures that do not necessarily enumerate all possible rule applications. Thus, our framework supports materialisation and incremental reasoning via a collection of modules. Each module is responsible for deriving consequences of a subset of the program, by using either standard rule application or proprietary algorithms. We prove that such an approach is complete as long as each module satisfies certain properties. Our formalisation of a module is very general, and in fact it allows modules to keep arbitrary auxiliary information. We also show how to realise custom procedures for four types of modules: transitivity, symmetry–transitivity, chain rules, and sequencing elements of a total order. Finally, we demonstrate empirically that using our custom procedures can speed up materialisation and incremental reasoning by several orders of magnitude on several well-known benchmarks. Thus, our technique has the potential to significantly improve the scalability of Datalog reasoners.

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AAAI Conference 2019 Conference Paper

Modular Materialisation of Datalog Programs

  • Pan Hu
  • Boris Motik
  • Ian Horrocks

The seminaı̈ve algorithm can be used to materialise all consequences of a datalog program, and it also forms the basis for algorithms that incrementally update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation. We split a datalog program into modules that can be handled using specialised algorithms, and we handle the remaining rules using the seminaı̈ve algorithm. We also present two algorithms for computing the transitive and the symmetric– transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.

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AAAI Conference 2018 Conference Paper

Cellular Network Traffic Scheduling With Deep Reinforcement Learning

  • Sandeep Chinchali
  • Pan Hu
  • Tianshu Chu
  • Manu Sharma
  • Manu Bansal
  • Rakesh Misra
  • Marco Pavone
  • Sachin Katti

Modern mobile networks are facing unprecedented growth in demand due to a new class of traffic from Internet of Things (IoT) devices such as smart wearables and autonomous cars. Future networks must schedule delay-tolerant software updates, data backup, and other transfers from IoT devices while maintaining strict service guarantees for conventional realtime applications such as voice-calling and video. This problem is extremely challenging because conventional traffic is highly dynamic across space and time, so its performance is significantly impacted if all IoT traffic is scheduled immediately when it originates. In this paper, we present a reinforcement learning (RL) based scheduler that can dynamically adapt to traffic variation, and to various reward functions set by network operators, to optimally schedule IoT traffic. Using 4 weeks of real network data from downtown Melbourne, Australia spanning diverse traffic patterns, we demonstrate that our RL scheduler can enable mobile networks to carry 14. 7% more data with minimal impact on existing traffic, and outperforms heuristic schedulers by more than 2×. Our work is a valuable step towards designing autonomous, “selfdriving” networks that learn to manage themselves from past data.

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AAAI Conference 2018 Conference Paper

Optimised Maintenance of Datalog Materialisations

  • Pan Hu
  • Boris Motik
  • Ian Horrocks

To efficiently answer queries, datalog systems often materialise all consequences of a datalog program, so the materialisation must be updated whenever the input facts change. Several solutions to the materialisation update problem have been proposed. The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules ‘backwards’ by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries. We show that this can be a considerable source of overhead even on very small updates. In contrast, the Counting algorithm does not evaluate the rules ‘backwards’, but it can handle only nonrecursive rules. We present two hybrid approaches that combine DRed and B/F with Counting so as to reduce or even eliminate ‘backward’ rule evaluation while still handling arbitrary datalog programs. We show empirically that our hybrid algorithms are usually significantly faster than existing approaches, sometimes by orders of magnitude.

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