## 2 Literature Review
### Research Gaps  
All the studies that implemented data acquisition models were specifically designed for the aim of the study. For instance, [PERSON] et al. [29], [PERSON] et al. [31], and [PERSON] and [PERSON] [32] developed labor resource data acquisition models. Also, the models developed by [PERSON] et al. [24], [PERSON] et al. [25], [PERSON] et al. [26], and [PERSON] and [PERSON] [28]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Aspect** & **Traditional WP** & **MDWP** & **Advantage** \\ \hline \multirow{3}{*}{Progress Tracking} & Manual \% complete & Automated, & Eliminates guesswork; \\  & (e.g., 50\% poured) & ROC-weighted WPAs & ties progress to physical \\  & & (Section 4.5) & milestones \\ \multirow{3}{*}{Data Granularity} & Time/cost only & 12 dimensions & Enables AI-driven \\  & & & analytics (Section 6.4) \\ \multirow{3}{*}{Real-Time Updates} & Static (updated & Dynamic (BIM/ERP & Reduces reporting \\  & weekly/monthly) & integrations) & latency \\ \multirow{3}{*}{Risk Mitigation} & Reactive & Proactive (e.g., safety & \\  & (post-incident) & trends per WP) & \\ \multirow{3}{*}{Sustainability} & Not tracked & Embodied carbon, & Supports ESG [1] \\  & & solid waste & reporting \\ \hline \hline \end{tabular} \({}^{1}\)Environmental, Social, and Governance Reporting.  
\end{table}
Table 1: Benefits of MDWPs vs. Traditional WPs.  
focused on decision-making for construction inventory, residential site and equipment management, and sewer infrastructure systems, respectively. Similarly, the data acquisition model proposed by [PERSON] and [PERSON] [30] included the construction project's cost overrun data. The models were perfectly employed, and the results obtained from utilizing them in knowledge discovery were satisfying, but these models lack genericity, and as implementing them in dissimilar cases is either not possible or needs modification. [PERSON] and [PERSON] [27] presented a comprehensive project-oriented data warehouse (PDW) that combines WBS and OBS, but their PDW was dedicated to providing detailed information about management responsibilities. Construction organizations, therefore, require a generic data acquisition model that leads to a consistent data warehouse that can be utilized for different purposes. It should collect data at the project's most efficient detailed work level (the work package level), similar to the models proposed by [PERSON] et al. [29] and [PERSON] and [PERSON] [32]. Also, it should contain emerging dimensions such as greenhouse gas emissions (GHG), solid waste, productivity, risk, safety, social impact, etc.  
This research addresses the gap found in the current literature by implementing a generic integrated data acquisition model that includes data from different dimensions. Moreover, it standardizes terminology by defining standard levels, phases, and subphases to enable the identification of meaningful metadata that can be used to capture data from multiple projects and organizations. In this research, the data acquisition model includes the GHG dimension in addition to time, cost, and resources, while other dimensions can be added in the future through a simple modification of the tool. This study also introduces the novel concept of storing the multi-dimensional data related to the construction work packages in key units. Presenting the data in key units facilitates the process of developing a WBS for future projects.