Access to large amounts of real-world data has long been a barrier to the development and evaluation of analytics applications for the built environment. Open datasets exist, but they are limited in their span (how much data is available) and context (what kind of data is available and how it is described). Evaluation of such analytics is also limited by how the analytics themselves are implemented, often using hard-coded names of building components, points and locations, or unique input data formats. To advance the methodology for how such analytics are implemented and evaluated, we present Mortar: an open testbed for portable building analytics, currently spanning 90 buildings and containing over 9.1 billion data points. All buildings in the testbed are described using Brick, a recently developed metadata schema, providing rich functional descriptions of building assets and subsystems. We also propose a simple architecture for writing portable analytics applications that are robust to the diversity of buildings and can configure themselves based on context. We demonstrate the utility of Mortar by implementing 11 applications from the literature.

中文翻译：

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砂浆

长期以来，访问大量现实世界数据一直是开发和评估建筑环境分析应用程序的障碍。存在开放数据集，但它们的跨度（有多少数据可用）和上下文（可用的数据类型以及描述方式）受到限制。对此类分析的评估还受到分析本身实施方式的限制，通常使用建筑组件、点和位置的硬编码名称或独特的输入数据格式。为了推进如何实施和评估此类分析的方法，我们展示了 Mortar：一个用于便携式建筑分析的开放式测试平台，目前跨越 90 座建筑物并包含超过 91 亿个数据点。测试平台中的所有建筑物都使用最近开发的元数据模式 Brick 进行描述，提供建筑资产和子系统的丰富功能描述。我们还提出了一种简单的架构，用于编写对建筑物的多样性具有鲁棒性并且可以根据上下文进行自我配置的便携式分析应用程序。我们通过实施文献中的 11 个应用程序来展示 Mortar 的实用性。