## 3 Results and Discussion  
Developed ThinkDairy Data-Mining Hub for Detecting Conditions of Dairy Cows: A Theoretical Framework  
The proposed ThinkDairy data-mining hub (Figure 2) showed how massive data collected from the dairy farms could be utilized in detecting conditions of dairy cows to support the creation of decisions for farmers. It is designed to carry out real-time assistance to farmers by cleaning and integrating data and be used for running multiple analyses that consider numerous relevant factors to improve the precision and accuracy of detection \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Parameters** & **Heat Stress** & **Subclinical Mastitis (SCM)** \\ \hline Milk performance indexes & Milk lactose (ML) & Heat stress reduced ML [40] & SCM reduced ML [45; 48; 49; 54; 51; 54] \\ Milk yield (MY) & Milk yield (MY) & Milk yield decreased 2 days after initiation of heat stress [43] & SCM reduced milk yield [44] \\ \hline Feed & Feed intake (FI) & Feed intake decreased 1 day after initiation of heat stress [43] & SCMreduced feed intake [53] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)of the health and wellness of the animals and ultimately, provide a precise calculation of feeding rations to individual cows according to their specific needs. The data-mining hub consisted of offline and online farm data. Offline farm data pertains to the animal records such as the animal type (heifer, pregnant, calving, lactating, dry-off), breed type (Holstein-Friesian, Jersey, Guernsey), breeding system (straightbred, crossbred), age in months, days in the cycle, days pregnant, days since calving, calving interval, parity (1-7), and lactation stage (early, mid, late). These offline records are used for the calculation of feed rations to dairy cows based on their real-time condition. However, this study focuses on the detection of the real-time condition of the cows only, and the application of the calculation of feed ration to dairy cows will be used for further studies. This section was stated for the sake of giving an overview of the flow of function of the data-mining hub. Online farm data pertains to the records that are measured daily, transferred to the server of the farm, and collected for processing using the proposed data-mining hub. The data collected includes (1) environmental condition: ambient temperature, relative humidity, temperature-humidity index, and wind and sun exposure; (2) physiological status such as body weight, body condition score (BCS), temperature, rumen conditions, breathing, and heart rate; (3) behavioral status: eating, standing, rumination, walking behaviors; (4) milk yield and composition records such as somatic cell count, solids-not-fat (SNF), milk protein, milk fat, milk lactose, protein to fat ratio, and milk urea nitrogen; and (5) feed records. These offline and online data are continuously collected from farms, stored in servers, and go through the customary data warehouse process called ETL, which is extraction, transformation, and loading. The assigned computer contains the files of the farm into specific directory structures determined by, i.e., farm source, data source, and date of acquisition. On the arrival of a new set of data files, the staging area or framework identifies the data files based on the directory structure and implements a predetermined code of one or more programs to extract the information from those files and validate it. Validations during extraction reconcile the records with the source data, separate the source data from unwanted loaded data, check the data type, and clean the source data from duplicates and fragmented data. The process of transforming the data begins after cleaning the extracted real-time data from the source and by data mapping, where a set of functions such as language-processing tool scripts are used to harmonize and standardize the extracted data.