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Adaptable User Profiles for Intelligent Geospatial Queries

机译:适用于智能地理空间查询的适应性用户配置文件

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摘要

The geospatial information user community is becoming increasingly diverse, with numerous users accessing distributed datasets for various types of applications. Currently in GIS, unlike traditional databases, there is a lack of machine learning algorithms to customize information retrieval results. Thus the particular interests of individual users are not taken into account in traditional geospatial queries. In this paper we present a system that adjusts query results based on user requirements and needs. It does so by using a collection of fuzzy functions that express user preference specifically in GIS environments. The focus of this work is on preference learning for one-dimensional, quantitative attributes, and on the customization of geospatial queries using this information. The model used to express user preferences adjusts gradually to the underlying complexity during a training process, starting with fairly simple linear functions and progressing to complex non-linear ones as needed. Our advanced modeling capabilities are demonstrated through an applicability example, and statistical simulations show the robustness of our system.
机译:地理空间信息用户社区正变得越来越多样化,许多用户访问各种类型应用程序的分布式数据集。当前,在GIS中,与传统数据库不同,缺少用于自定义信息检索结果的机器学习算法。因此,在传统的地理空间查询中没有考虑单个用户的特殊兴趣。在本文中,我们提出了一个根据用户需求和需求调整查询结果的系统。它通过使用模糊函数集合来做到这一点,这些函数专门在GIS环境中表达用户的喜好。这项工作的重点是针对一维,定量属性的偏好学习,以及使用此信息进行的地理空间查询的自定义。用于表达用户偏好的模型在训练过程中逐渐适应潜在的复杂性,从相当简单的线性函数开始,然后根据需要逐步发展为复杂的非线性函数。通过一个适用示例演示了我们先进的建模功能,而统计仿真则表明了我们系统的稳定性。

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