The term retrieval abstract machine

IWBS Report Pub Date : 1992-06-01 DOI:10.1145/130283.130309
Michael Ley
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引用次数: 1

Abstract

Scans through large collections of complex objects often cannot be avoided. Even if sohphisticated indexing mechanisms are provided, it may be necessary to evaluate simple predicates against data stored on disk for filtering. For traditional record oriented data models i/o and buffer management are the main bottlenecks for this operation, the interpretation of data structures is straightforward and usually not an important cost factor. For heterogeneously shaped complex objects it may become a dominant cost factor. In this paper we demonstrate a technique to make data structure traversal inside of complex objects much cheaper than naive interpretation. We compile navigation necessary to evaluate condition predicates and physical schema information into a program to be executed by a specialized abstract machine. Our approach is demonstrated for the Feature Term Data Model (FTDM), but the technique is applicable to many other complex data models. Main parts of this paper are dedicated to the method we used to design the Term Retrieval Abstract Machine (TRAM) architecture by partial evaluation of a tuned interpreter.
术语检索抽象机
扫描大量复杂对象通常是无法避免的。即使提供了复杂的索引机制,也可能需要根据存储在磁盘上的数据评估简单的谓词,以便进行过滤。对于传统的面向记录的数据模型,i/o和缓冲区管理是该操作的主要瓶颈,数据结构的解释很简单,通常不是一个重要的成本因素。对于异质形状的复杂物体,它可能成为一个主要的成本因素。在本文中,我们演示了一种技术,使复杂对象内部的数据结构遍历比朴素解释便宜得多。我们将计算条件谓词和物理模式信息所需的导航编译到一个程序中,由专门的抽象机器执行。我们的方法是针对Feature Term Data Model (FTDM)进行演示的,但该技术也适用于许多其他复杂的数据模型。本文的主要部分致力于通过对调优解释器的部分求值来设计术语检索摘要机(TRAM)体系结构的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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