A Hybrid Approach to Logic Evaluation

In this thesis, we contribute the hybrid approach – a means of combining the practical advantages of feature-rich logic evaluation in the cloud, with the performance benefits of hand-written, optimized, efficient native code. In the first part of our hybrid approach, we introduce a cloud-based distribution for logic programs, which may be deployed as a service, in standard cloud environments, across cheap commodity hardware. Modern systems are in the cloud; while distributed logic solvers exist, these systems are highly specialized, requiring expensive, resource intensive hardware infrastructures. Our original technique achieves a fully automatic synthesis of cloud infrastructure for logic programs, and includes a range of practical features not present in existing distributed logic solvers. We show that an implementation of the distribution scales effectively within real-world cloud environments, against a distribution over cores of the same machine. We show that our multi-node distribution may be effectively combined with existing multi-threaded techniques to mitigate the network communication cost incurred by distribution. In the second part of our hybrid approach, we introduce extra-logical algorithms, to achieve performance for logic programs that would not be possible within a bottom-up logic evaluation. Modern systems must deliver high performance on big data; however, even the most powerful logic engines, distributed or otherwise, can be beaten by hand-written code on particular problems. We give a novel implementation of a system for the high-impact problem of sink-reachability, designed such that its algorithms may be used in logic programs. A thorough empirical evaluation, across a range of large-scale, real-world datasets, shows our system outperforms the current state of the art for the sink-reachability problem in all cases. Our hybrid approach addresses the two major deficiencies of modern logic systems, providing a practical means of evaluating logic in distributed cloud-based environments, while offering performance gains for specific high-impact problems that would not be possible using logic programming alone

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Redacted by arXiv.Comment: This article has been removed by arXiv due a copyright claim by a 3rd part

A Differential Datalog Interpreter

The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its physical incorporation into the database itself. The de-facto method of computing it, is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization, that is, to adjust the computation to new data, instead of restarting from scratch. One of the major caveats, is that deleting data is notoriously more involved than adding, since one has to take into account all possible data that has been entailed from what is being deleted. Differential Dataflow is a computational model that provides efficient incremental maintenance, notoriously with equal performance between additions and deletions, and work distribution, of iterative dataflows. In this paper we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm, with the same optimizations

Large-Scale Legal Reasoning with Rules and Databases

Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing rangeof scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases