# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 5 Case Study of Proposed Algorithm  
### Summary of Case Study  
The proposed cost-schedule data integration algorithm has been applied to a real construction project of one apartment complex in South Korea with mixed-use (residential and commercial). This research selected this case study project since apartment projects have been ordered continuously  
Figure 6: Interfaces of Module 1, 2, and 3 implemented using Python, (**a**) Module 1 for Automatic Text Mining, (**b**) Module 2 for Data Parsing, and (**c**) Module 3 for Metadata Mapping. over many decades globally [32], as the most common type of construction project. It is considered that the application of the proposed algorithm to this project can show its feasibility of one of the prevalent types of construction projects. The complex involves nine buildings with 774 units. This case study only focuses on the structural part of the buildings, which corresponds to the 03 Concrete in MasterFormat. All data associated with foundation, walls, slabs, stairs, columns, and beams of nine buildings have been demonstrated. Several email discussions and semi-structural interviews with project managers were conducted to enhance understanding of the project data. As sources of data, eight types of project documents (drawings, specifications, quantity-takeoffs, project budget reports, detailed cost estimate report, master schedule, bar chart schedule, and monthly progress curve) were provided. According to project managers' requests, the database was established in Microsoft Excel since it is the primary tool used in project management. In addition, the Individual level of the _WHO_ dimension (L3) was omitted, but its contents were represented in the Labor Unit Cost (Attribute 3). Two levels were incorporated into the _WHAT and HOW_ dimensions, _WHAT_ L5 (Element Type), _HOW_ L6 (Activity Detail), in response to the project organizations' complexity. Module 1 extracted the data associated with six dimensions and 31 levels from all provided documents. The 20,315 pages of quantity take-offs, 28 pages of cost report, overall and monthly schedule report, and project organization chart were used to develop the integrated cost-schedule big data. For instance, data associated with the _WHERE, WHAT, HOW_ dimensions at different LODs were extracted from quantity-takeoffs, together with quantity data (see Figure 7). The collected data are mapped into items in MasterFormat, UniFormat, and project management standard and integrated into the 5W1H data schema using shared data in different documents by Module 2. As represented in Figure 7, the _HOW_, L1 (Work) is addressed in quantity-takeoffs as well as cost estimate reports. Module 2 used this data as a medium to link six types of data from quantity-takeoffs and ten types of data from cost reports. The part of transformed data, which are derived from four data sources (quantity-takeoff, schedule report, cost report, and organization chart) within the data schema, is represented as Figure 8.  
Figure 7: Data Extracting and Mapping from Quantity-takeoffs into the 5W1H Data Schema. Module 3 automatically infers higher-level data of each dimension from the lowest-level data. As represented in Figure 8, the lower-level data is directly achieved from the data sources, while the rest of the higher-level data in the 5W1H data structure are derived from the lower-level. For _WHAT_ and _HOW_ dimensions, the developed rules are in line with UniFormat and MasterFormat, respectively. The rules (1) import the codes of lowest-level data identified Module 2, (2) use them as metadata to retrieve code for upper-level, and (3) map corresponding data into its upper-level data in the 5W1H data schema. Likewise, the rules in line with construction project management standards derive the data for higher levels in other dimensions. The database comprising a total of 11,031,220 cells (43 columns and 256,540 rows) was established by Module 4. It contains all data transformed and inferred data from Module 2 and 3.