# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 5 Case Study of Proposed Algorithm
### Verification and Discussion  
The validity of the cost-schedule data integration algorithm based on big data technology is evaluated according to the three requirements discussed in Section 3.3: data integrity and efficiency, efficiency in establishing and transforming data, and practicality. First, data in the established system has its basis on the lowest-level of quantity data; it results in a high degree of data integrity in cost-schedule data integration. Furthermore, all lowest-level quantity data in the 5W1H database have 43 types of variables and attributes and keep relationships with multiple upper-level data via \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Independent Variables**} & \multicolumn{1}{c}{**Dependent Variables**} \\ \hline
**Levels of Dimension** & **Core Unit Attributes** & **Result** \\ \hline
5 Levels of WHAT Dimension & Attribute 1. Unit & Result 1. Calculation \\
6 Levels of WHERE Dimension & Attribute 2. Material Unit Cost & Result 2. Quantity \\
6 Levels of HOW Dimension & Attribute 3. Labor Unit Cost & Result 3. Transformation Quantity \\
8 Levels of WHEN Dimension & Attribute 4. Operation Unit Cost & Result 4. Material Cost \\
5 Levels of WHO Dimension & Attribute 5. Total Unit Cost & Result 5. Labor Cost \\
1 Levels of WHY Dimension & - & Result 6. Operation Cost \\ - & - & Result 7. Total Cost \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of Data in the Implemented Database Based on the 5W1H Schema. Figure 9: Interface of Data Mining and Analysis Modules using Pivot Table in Excel. metadata. This data structure created schedule reports at any level of detail by flexibly combining the related information units at different levels on multiple dimensions. For instance, one schedule item in a monthly schedule report, \"Structure concrete work on the 13-floor of Building 401 (early start time: 4 March, early finish time: 10 March)\", was well subdivided into a weekly schedule by the proposed system. The system automatically generated a corresponding schedule at the week level, \"Concrete Formwork 70A zone on the 13-floor of Building 401 (early start time: 4 March, early finish time: 6 March)\". It is an outcome of combining the query results from search conditions, _WHERE_ L5 (70A zone) and the _HOW_ L4 (Concrete Forming). Although processing time varies from activity number, the system showed five man-min as the average response to time to data synchronization for 100 activities. In this case study, the demonstration of big data consisting of 11,031,220 cells took less than 20 man-min. It is a representative processing time since data building and transformation time differs depending on data engineers' proficiency. However, it can be said that the proposed algorithm with developed modules significantly improves efficiency in integrating cost-schedule data, considering the fact that this task takes at least ten man-h with existing systems. In addition, updating changes in data caused by project changes (i.e., design, quantity, construction methods) requires less than one man-mins by searching metadata. The developed big data system provides Microsoft Excel as its main interface. Users outside of data engineering can retrieve and extract required data from the big data by using the Pivot table in Excel; it allows more than 50,000 types of data extraction with simple mouse drag-and-drop. In the case of big projects, Hadoop or NoSQL can be used instead of Excel to incorporate more amount of data supported by Excel (maximum number of cells: 17,179,870). From the interview with project managers, the high practicality of the proposed system has been identified. After the 10-min instruction, new users are enabled to use the system and show no-burden in adopting this new system to their projects. The overall algorithm, including data mining, parsing, building, extracting, and analyzing, can be applied to various projects with any scale, complexity, and function.