Article Preview
TopIntroduction
Today, almost all productive human activity is inseparable from computers and the internet. People use computers more widely and more conveniently, which makes data grow exponentially (Shiri, 2015). This data has huge commercial value, and the irregular storage of big data will increase the storage cost (Wang et al., 2015). The ability to analyze big data cannot keep up with this growth, so while the amount of data is large, the amount of information is still insufficient (Pisarenko, 2017). Analyzing and mining valuable information from big data will create the value. Data mining is a computer science, but it also includes many other disciplines (Pisarenko, 2017). For example, for pattern recognition, visual performance analysis includes statistics and machine models. Data information provides valuable references for decision makers' analysis.
The research of data integration began in the 1970s, and the scope and difficulty of data integration increased from the initial homogeneous data source integration to the heterogeneous data source, followed by semantic-based data integration (Suhane et al., 2016). The existence of heterogeneity in data integration includes four aspects: heterogeneity at the system level, heterogeneity at the syntactic level, heterogeneity at the structural level, and heterogeneity at the semantic level. System-level heterogeneity refers to the heterogeneity of underlying hosts and operating systems; syntax-level heterogeneity refers to the heterogeneity of data representation types and formats. Heterogeneity at the semantic level refers to differences in word meanings in certain domains caused by communication barriers (Fathi & Derakhshan, 2019). Traditional methods cannot yet sufficiently solve the problem of semantic heterogeneity. Currently, ontologies are used to address semantic heterogeneity.
Subjective aspects of human activity such as well-being offer significant potential for information gleaned from big data. Since the beginning of the 21st century, with the improvement of Chinese people’s material conditions and living standards, people have begun to pay attention to spiritual pursuits. Xi Jinping argued at the 18th Congress of the CPC Central Committee that “people's happiness” is a necessary condition for realizing the Chinese dream (Li & Ma, 2018). Subjective well-being can be measured by many indicators; for example, if the aspects of one’s life that are most satisfying are sufficient, one can be evaluated as happy, and vice versa. Self-affirmation is another key indicator of subjective well-being.
Due to increasingly fierce social competition and heavy academic pressure, levels of subjective well-being among middle school students in China are declining. Physical health monitoring shows that students' physical fitness and health level have been in a state of continuous decline in the past 20 years (Cohn et al., 2009). Lung capacity, speed, explosive power, and endurance have further declined. The detection rate of obesity and poor eyesight has increased. The proportion of middle school students remains high among the general population. Middle school is a critical period for developing physical health and mental well-being. However, at this stage, students often bear heavy learning tasks and pressures, and their inner feelings and subjective well-being are often ignored (Starovoytova & Namango, 2016). How to attain the goal of quality education is a significant question since young people represent the future of the nation, and subjective well-being is an area of great concern (Schrier, 2008) . This research responds to questions concerning how to improve school life satisfaction and happiness of middle school students. It builds on research that proposes providing practical and theoretical support for the development of school sports to cultivate middle school students' happiness and promote their physical and mental health (Huang & Liu, 2021).