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Clustering of designers based on building information modeling event logs

机译：基于建筑信息建模事件日志的设计者聚类

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Abstract A network‐enabled event log mining approach is proposed for a deep understanding of the Building Information Modeling (BIM)‐based collaborative design work. It proposes a novel algorithm termed node2vec‐GMM combining a graph embedding algorithm named node2vec and a clustering method named Gaussian mixture model (GMM) to cluster designers within a network into several subgroups, and then makes cluster analysis. Its superiority lies in the efficient feature learning ability to preserve network structure and the powerful clustering ability to tackle uncertainty and visualize results, which can directly return the cluster embedding. As a case study, a directional network with 68 nodes (designers) and 436 ties (design task transmissions) is constructed based on retrieved data from 4GB real BIM event logs. The node2vec learns and projects the network feature representation into a 128‐dimensional vector, which is learned by GMM to discover three possible clusters owning 15, 26, and 27 closely linked designers. Analysis of each cluster is performed from node importance measurement and link prediction to identify information spreading and designers’ roles within clusters. Our new algorithm node2vec‐GMM is proven to better improve clustering quality than other state‐of‐the‐art methods by at least 6.0% Adjusted Rand Index and 13.4% Adjusted Mutual Information. Overall, the designer clustering process provides managers with data‐driven support in both monitoring the whole course of the BIM‐based design and making reliable decisions to increase collaboration opportunities.
机译：摘要提出了一种网络的事件日志挖掘方法，以深入了解建筑信息建模（BIM）基础的协作设计工作。它提出了一种新的算法，称为Node2Vec-Gmm称为Node2VEC的曲线图嵌入算法和名为Gaussian混合模型（GMM）的群集方法将网络设计人员组合到多个子组中，然后进行集群分析。其优越性在于保持网络结构的有效特征学习能力和强大的聚类能力来解决不确定性和可视化结果，可以直接返回集群嵌入。作为一个案例研究，基于来自4GB真实BIM事件日志的检索数据来构建具有68个节点（设计者）和436系列（设计任务传输）的定向网络。 Node2VEC学习并将网络特征表示到128维向量，该传染媒介是通过GMM学习的，以发现拥有15,26和27个紧密相关的设计人员的三种可能的集群。从节点重要性测量和链路预测执行每个群集的分析，以识别集群内的信息扩展和设计者的角色。我们的新算法Node2Vec-GMM被证明可以更好地提高聚类质量，而不是其他最先进的方法，至少6.0％调整的rand指数和13.4％调整的相互信息。总的来说，设计者聚类过程提供了管理基于BIM的设计的整个过程和可靠的决策，以增加协作机会的可靠决策，为管理人员提供数据驱动支持。

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