使用核心概念转换代数的地理信息程序元数据

IF 1.8 Q2 GEOGRAPHY
Niels Steenbergen, Eric Top, E. Nyamsuren, Simon Scheider
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引用次数: 0

摘要

转换对于处理地理信息至关重要。它们不仅涉及地理数据格式和参考系统之间的转换,还涉及根据某种目的将地理数据转化为有用信息。然而,由于转换可以通过各种格式和工具实现,其功能和目的通常隐藏在工作流程的技术细节之下。因此,为了实现地理信息程序的自动化,我们需要在概念层面上对工作流程实施的转换进行建模,将其作为一种程序知识。虽然空间信息的核心概念在这方面提供了一个有用的描述层次,但我们目前还缺乏一个模型来描述这些概念之间可能的转换空间。在本文中,我们提出了核心概念转换代数(CCT)。它包括一个将核心概念建模为关系类型的类型层次结构,以及一组使用这些类型的函数签名来描述的基本转换。我们通过编译代数工具描述和推断整个工作流中的目标概念,用机器可读的抽象元数据来丰富 GIS 工作流。在本文中,我们将展示如何利用此类程序元数据,根据从地理分析问题中得出的任务描述检索工作流。转换可以独立于其实现或数据格式进行查询。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Procedural metadata for geographic information using an algebra of core concept transformations
Transformations are essential for dealing with geographic information. They are involved not only in the conversion between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relation types, and a set of basic transformations described in terms of function signatures that use such types. We enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions and inferring goal concepts across a workflow. In this article, we show how such procedural metadata can be used to retrieve workflows based on task descriptions derived from geo-analytical questions. Transformations can be queried independently from their implementations or data formats.
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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