## 2 Materials and Methods
### Selection of Parameters to Be Used in the Data-Mining Hub  
For this study, parameters that directly and indirectly affect heat stress and subclinical mastitis in dairy cows were focused on. The parameters used for the development of the data-mining hub were derived from the five main conditions of the cows: (1) environmental conditions, (2) physiological conditions, (3) behavioral conditions, (4) milk performance, and (5) feed intake, as specified in Table 1.  
_Environmental conditions_ include the ambient temperature (Ta), relative humidity (RH), and temperature-relative humidity index (THI). The THI is composed of the effects of Ta and RH and is used as an important environmental index to assess the state of heat stress and the degree of heat stress in animals. For instance, according to [18], a THI value less than 72 will give an optimal condition for dairy cows, whereas THI ranging from 73 to 79, 80 to 89, and THI greater than 90 will give dairy cows mild, moderate, and severe heat stress,  
Figure 1: The basic format of the data-mining hub in which the cleaned and harmonized data were loaded. The figure illustrates the scheme of data collection and integration format used in the study based on the report of [17]. respectively. In the same way, it was reported that subclinical and clinical mastitis risks in dairy cows are increased when exposed to a hot environment [19; 20]. Therefore, high Ta, RH, and THI should reflect to increase heat stress and subclinical mastitis conditions in the data-mining hub. _Physiological conditions_ include body weight (BW), body condition score (BCS), body temperature, rumen temperature (T\({}_{\rm{rum}}\)), rumen pH, and respiration rate. As far as the research of this study, the body weight and BCS of dairy cows have no direct relation with heat stress conditions. In subclinical mastitis, it was also found that BCS has no significant relation with subclinical mastitistis, but it has been reported that the bodyweight of the dairy cows is positively related to somatic cell count (SCC) in milk and the somatic cell count is a well-established indicator of subclinical mastitistis [21]. Body temperature gives a direct measure of the heat stress level of individual dairy cows [18] and T\({}_{\rm{rum}}\) normally increases with increased ambient temperature [22]. It has also been reported that a high rise in body temperature [23] and T\({}_{\rm{rum}}\)[24] are accompanied by subclinical mastitis. Rumen pH is reduced in heat-stressed cows [25] and also tends to decrease in dairy cows that will be affected by subclinical mastitistis [26]. Dairy cows affected with subclinical mastitis have been reported to increase respiratory rate [27] while dairy cows that are experiencing milk heat stress have been reported to show 61 breaths per minute [28] and 120 breaths per minute for severe heat stress [29]. The physiological regulation in dairy cows is the most critical factor in their productive capacity. Therefore, the combined physiological parameters are an eminent factor for accurate detection of heat stress, subclinical mastitistis, and normal conditions of the cows using the data-mining hub. _Behavioral conditions_ in the data-mining hub include ruminating, standing, lying/sitting, and drinking, which are important indexes to measure the health and welfare of dairy cows. The ruminating time of dairy cows is increased when the THI is increased [30; 31], whereas a reduction in the ruminating time of dairy cows with subclinical mastitistis has been reported [32]. The standing behavior of the dairy cows was reported to be prolonged when heat-stressed [33] and when affected with subclinical mastitistis [32]. Consequently, decreases in the lying time of the dairy cows were observed in animals affected with subclinical mastitistis [24; 32]. Heat-stressed dairy cows, on the other hand, were observed to be restless [34].