数据库和逻辑程序设计中递归查询的实验 系统

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摘要翻译：
本文研究了对大量（可能是分布式）数据进行推理的问题。目前，已有的方法存在一些局限性：{\em(i)}由于推理一般是在主存中进行的，可同时处理的数据量有限；{\em(ii)}与外部（和独立的）DBMSs的交互并不简单，在某些情况下，根本不允许；{\em(iii)}对于涉及大量数据的复杂推理任务，现有实现的效率仍然不够。本文在这一背景下提供了一个贡献；本文提出了一个新的系统，称为DLV$^{DB}$，旨在解决这些问题。此外，本文还报告了我们在一些经典演绎问题上与几个最先进的系统（逻辑和数据库）进行了深入的实验分析的结果；其他测试的系统是:LDL++、XSB、Smodels和三个顶级商业DBMSs。DLV$^{DB}$在递归查询上的性能甚至明显优于商业数据库系统。出现在逻辑程序设计理论与实践中
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英文标题：
《Experimenting with recursive queries in database and logic programming
  systems》
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作者：
Giorgio Terracina, Nicola Leone, Vincenzino Lio, Claudio Panetta
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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英文摘要：
  This paper considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: {\em (i)} the quantity of data that can be handled contemporarily is limited, due to the fact that reasoning is generally carried out in main-memory; {\em (ii)} the interaction with external (and independent) DBMSs is not trivial and, in several cases, not allowed at all; {\em (iii)} the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This paper provides a contribution in this setting; it presents a new system, called DLV$^{DB}$, which aims to solve these problems. Moreover, the paper reports the results of a thorough experimental analysis we have carried out for comparing our system with several state-of-the-art systems (both logic and databases) on some classical deductive problems; the other tested systems are: LDL++, XSB, Smodels and three top-level commercial DBMSs. DLV$^{DB}$ significantly outperforms even the commercial Database Systems on recursive queries. To appear in Theory and Practice of Logic Programming (TPLP)
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PDF链接：
https://arxiv.org/pdf/0704.3157

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