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.

在过去十年中，收集大量数据变得更加容易，推动建筑行业将数据挖掘作为挖掘大数据分析潜力的强大工具。然而，由于需要一个通用的分析框架、有效的应用程序和洞察力驱动的目标数据选择以及基准支持的声明，重复性挑战仍然存在。本研究通过提出一个通用的逐步多维数据挖掘框架来解决这些问题，该框架适用于构建数据，利用数据立方体的维结构。使用开放的建筑数据基因组项目 2 集，由来自 1636 座建筑的 3053 个能量计组成，我们提供了一个在线、开放访问、我们的方法应用于自动模式识别的实现说明。我们定义了一个 3 维建筑立方体，呼应了典型的感兴趣的分析框架，即自下而上、自上而下和时间钻取方法。我们的结果强调了应用和洞察驱动挖掘对于有效的维度框架定位的重要性。开发了有影响力的可视化，允许进行实际的人工检查，为更可解释的分析铺平了道路。