Learning socio-organizational network structure in buildings with ambient sensing data

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
A. Sonta, Rishee K. Jain
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引用次数: 5

Abstract

Abstract We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations. Impact Statement The structure of social and organizational relationships in commercial building workplaces is a key component of work processes. Understanding this structure—typically described as a network of relational ties—can help designers of workspaces and managers of workplaces make decisions that promote the success of organizations. These networks are complex, and as a result, our traditional means of measuring them are time and cost intensive. In this paper, we present a novel method, the Interaction Model, for learning these network structures automatically through sensing data. When we compare the learned network to network data obtained through a survey, we find statistically significant correlation, demonstrating the success of our method. Two key strengths of our proposed method are, first, that it uncovers network patterns quickly, requiring just 10 weeks of data, and, second, that it is interpretable, relying on intuitive opportunities for social interaction. Data-driven inference of the structure of human systems within our built environment will enable the design and operation of engineered built spaces that promote our human-centered objectives.
利用环境传感数据学习建筑物中的社会组织网络结构
摘要我们开发了一个模型,该模型使用商业建筑中分布式插塞式能量传感器的环境传感数据成功地学习了社会和组织人类网络结构。商业建筑设计和运营的一个关键目标是支持其内部组织的成功。在现代工作空间中,一个特别重要的目标是协作,它依赖于个人之间的物理交互。因此,了解工人之间真正的社会组织关系可以帮助建筑物和组织的管理者做出改善协作的决策。在本文中,我们介绍了交互模型,这是一种利用分布式插头负载能量传感器的数据来推断人类网络结构的方法。在一个案例研究中,我们将我们的方法与通过调查获得的网络数据进行比较,并将其性能与其他数据驱动工具进行比较。我们发现,与以前的方法不同,我们的方法推断出的网络与调查网络的相关性在统计学上具有显著性(图相关性为0.46,在0.01置信水平下具有显著性)。我们还发现,我们的方法只需要10周的传感数据,就可以实现动态网络测量。通过数据驱动的方式学习人际网络结构可以实现空间的设计和运营,从而鼓励而不是抑制组织的成功。影响声明商业建筑工作场所的社会和组织关系结构是工作流程的关键组成部分。了解这种结构——通常被描述为关系纽带网络——可以帮助工作区的设计者和工作区的管理者做出促进组织成功的决策。这些网络是复杂的,因此,我们传统的测量方法是时间和成本密集型的。在本文中,我们提出了一种新的方法,交互模型,用于通过传感数据自动学习这些网络结构。当我们将学习到的网络与通过调查获得的网络数据进行比较时,我们发现了统计学上显著的相关性,证明了我们方法的成功。我们提出的方法的两个关键优势是,首先,它可以快速发现网络模式,只需要10周的数据;其次,它是可解释的,依赖于直观的社交机会。在我们的建筑环境中,对人类系统结构的数据驱动推理将使工程建筑空间的设计和运营成为可能,从而促进我们以人为中心的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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