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Dynamically Creating Indices for Two Million Cases: A Real World Problem

机译：动态创建索引为200万个案例：一个现实世界问题

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摘要

Efficiently indexing and retrieving cases from a very large case library are major concerns when building a Case-Based Reasoning (CBR) system. Most CBR research has focused on representation of cases, how to identify features that should be used for retrieval; and similarity measurement between values of attributes. In this paper, we propose a method for dynamically creating indices, and, also different similarity-measurement methods for different types of attributes. We also discuss the use of a relational database for representing cases, taxonomy knowledge, and spatial information. Our real world problem domain consists of 2 million incomplete insurance cases, with 30 different attributes. Even though all of these are valid cases, only 10 percent of these policies have lodged claims. These situations create a very complex case base for reasoning and problem solving. In response to this complexity, the approach adopted in building our CBR system involves a considerable amount of statistical pre-analysis of the contents of the case base to generate domain knowledge that could be used by the "Dynamic Index Creation Mechanism". The main contribution of this paper is in describing the techniques used in our CBR system to dynamically create indices for the purpose of effective case retrieval.
机译：从一个非常大的案例库中有效索引和检索案例是在构建基于案例的推理（CBR）系统时的主要问题。大多数CBR研究都集中在案例的代表性上，如何识别应该用于检索的功能;和属性值之间的相似性测量。在本文中，我们提出了一种动态创建指标的方法，以及针对不同类型属性的不同相似性 - 测量方法。我们还讨论了关系数据库以代表案例，分类知识和空间信息。我们的现实世界问题域由200万不完整的保险案件组成，具有30个不同的属性。即使所有这些都是有效的案例，这些政策中只有10％都有提出的索赔。这些情况为推理和解决问题创造了一个非常复杂的案例基础。为了应对这种复杂性，这种方法在建设我们的CBR系统采用涉及相当多的案例库的内容生成可通过“动态创建索引机制”使用领域知识的统计预分析的。本文的主要贡献在于描述了我们CBR系统中使用的技术，以动态创建指标，以便有效地检索。

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