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Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data

机译：建筑物的数据挖掘多维数据集，用于构建性能数据的多维分析的通用框架

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摘要

Over the last decade, collecting massive volumes of data has been made all the more accessible, pushing the building sector to embrace data mining as a powerful tool for harvesting the potential of big data analytics. However repetitive challenges still persist emerging from the need for a common analytical frame, effective application-and insight-driven targeted data selection, as well as benchmarked-supported claims. This study addresses these concerns by putting forward a generic stepwise multidimensional data mining framework tailored to building data, leveraging the dimensional-structures of data cubes. Using the open Building Data Genome Project 2 set, composed of 3053 energy meters from 1636 buildings, we provide an online, open access, implementation illustration of our method applied to automated pattern identification. We define a 3-dimensional building cube echoing typical analytical frames of interest, namely, bottom-up, top-down and temporal drill-in approaches. Our results highlight the importance of application and insight driven mining for effective dimensional-frame targeting. Impactful visualizations were developed allowing practical human inspection, paving the path towards more interpretable analytics. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
机译：在过去的十年中，收集了大量数据，已经更加易于访问，推动建筑物部门将数据挖掘作为收获大数据分析的潜力的强大工具。然而，重复挑战仍然仍然存在于对共同的分析帧，有效应用程序和洞察力驱动的目标数据选择的需求，以及基准支持的索赔。本研究通过提出对构建数据定制的通用逐步多维数据挖掘框架来解决这些问题，利用数据多维数据集的尺寸结构。采用开放式建筑数据基因组项目2套装，由1636年建筑物的3053个能量计组成，我们提供了在线，开放式访问，实现了我们的方法的实施例，应用于自动模式识别。我们定义了一个三维建筑立方体回应典型的兴趣分析框架，即自下而上，自上而下和时间钻孔方法。我们的结果突出了应用和洞察驱动挖掘对有效尺寸帧靶向的重要性。开发了有影响力的可视化，允许实际的人体检查，铺平了更可取的分析的路径。 （c）2021提交人。由elsevier b.v发布。这是CC下的开放式访问文章（http://creativecommons.org/licenses/by/4.0/）。

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