Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.

中文翻译：

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使用地理分析概念对 GIS 工作流程进行松散编程


松散编程使分析师能够使用概念而不是过程代码进行编程。数据转换未指定，省略了程序细节并利用了有关函数对数据类型的适用性的知识。为了综合地理分析任务的高质量工作流程，语义类型系统需要在足够深的水平上反映地理信息系统（GIS）的知识，以捕获地理分析概念和意图，但又足够浅以概括地理信息系统实施。最近，提出了空间信息的核心概念和相关的地理分析概念，作为向当前地理数据模型添加所需抽象级别的方法。核心概念数据类型（CCD）本体是一个语义类型系统，可用于约束 GIS 功能以进行工作流合成。然而，迄今为止，尚不清楚精度和工作流程质量的预期收益会如何。在本文中，我们通过使用这些类型注释 GIS 工具来综合工作流程，指定从城市宜居场景中获取的一系列常见分析任务。我们根据常见数据类型生成的基准来衡量自动合成工作流程的质量。结果表明，CCD 概念显着提高了工作流程合成的精度。