Efficient Tabling of Structured Data with Enhanced Hash-Consing

TL;DR

This paper introduces an improved hash-consing technique for tabling structured data in logic programming, significantly reducing space and time complexity for sequence processing tasks.

Contribution

It presents enhancements to hash-consing with input sharing and hash code memoization, improving scalability in tabling systems.

Findings

Reduces space consumption through hash-consing.

Eliminates extra linear factors in sequence processing.

Experimental results confirm scalability improvements.

Abstract

Current tabling systems suffer from an increase in space complexity, time complexity or both when dealing with sequences due to the use of data structures for tabled subgoals and answers and the need to copy terms into and from the table area. This symptom can be seen in not only B-Prolog, which uses hash tables, but also systems that use tries such as XSB and YAP. In this paper, we apply hash-consing to tabling structured data in B-Prolog. While hash-consing can reduce the space consumption when sharing is effective, it does not change the time complexity. We enhance hash-consing with two techniques, called input sharing and hash code memoization, for reducing the time complexity by avoiding computing hash codes for certain terms. The improved system is able to eliminate the extra linear factor in the old system for processing sequences, thus significantly enhancing the scalability of applications such as language parsing and bio-sequence analysis applications. We confirm this improvement with experimental results.