Akademik Veri Yönetim Sistemi
ADVANCED ENGINEERING INFORMATICS, cilt.62, 2024 (SCI-Expanded)
The escalating volume of data in engineering practice necessitates innovative computational approaches for datadriven insights. Existing literature relies on isolated data points, unable to exploit the inherent connectivity in engineering datasets, resulting in suboptimal utilization of data context. This research employs node2vec, a graph-based recommendation system that surpasses existing models by incorporating an efficient walking mechanism to learn from past behaviors and a predictive component that enhances its adaptability. By structuring these activities into a network of budgeted units, person-hours, and earned values, the effectiveness of the node2vec model as a resource recommendation tool was demonstrated across three diverse datasets. Firstly, node2vec efficiently explores diverse neighborhoods within the input network through a flexible biased random walk, enhancing the system's ability to adaptively model complex relationships among various project elements. Secondly, this graph-based approach allows the recommendation models to fully harness relational data. These mechanisms coupled with a predictive neural network component enabled node2vec to learn from and utilize data connectivity, achieving an accuracy rate of 94% across the datasets. Ultimately, by leveraging collected engineering data and recognizing dependencies among records, the system can offer more detailed insights and empower engineering managers to make better-informed decisions.