Space‐efficient data structures for the inference of subsumption and disjointness relations

José Fuentes‐Sepúlveda, Diego Gatica, Gonzalo Navarro, M. Andrea Rodríguez, Diego Seco
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Abstract

Conventional database systems function as static data repositories, storing vast amounts of facts and offering efficient query processing capabilities. The sheer volume of data these systems store has a direct impact on their scalability, both in terms of storage space and query processing time. Deductive database systems, on the other hand, require far less storage space since they derive new knowledge by applying inference rules. The challenge is how to efficiently obtain the required derivations, compared to having them in explicit form. In this study, we concentrate on a set of predefined inference rules for subsumption and disjointness relations, including their negations. We use compact data structures to store the facts and provide algorithms to support each type of relation, minimizing even further the storage space requirements. Our experimental findings demonstrate the feasibility of this approach, which not only saves space but is often faster than a baseline that uses well‐known graph traversal algorithms implemented on top of a traditional adjacency list representation to derive the relations.
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用于推断归并和不相接关系的空间效率数据结构
传统数据库系统作为静态数据存储库,可存储大量事实并提供高效的查询处理功能。这些系统存储的数据量之大,直接影响了其可扩展性,包括存储空间和查询处理时间。另一方面,演绎法数据库系统所需的存储空间要小得多,因为它们通过应用推理规则获得新知识。与显式推导相比,如何高效地获得所需的推导是一项挑战。在本研究中,我们将重点放在一组预定义的推理规则上,这些规则适用于归并关系和析取关系,包括它们的否定关系。我们使用紧凑的数据结构来存储事实,并提供支持每种关系的算法,从而进一步减少对存储空间的需求。我们的实验结果证明了这种方法的可行性,它不仅节省了空间,而且通常比在传统邻接表之上使用众所周知的图遍历算法来推导关系的基线方法更快。
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