Enhancing Query Performance Using Simultaneous Execution and Vertical Query Splitting

in recent years, the significance of data mining algorithms has increased with the fast growth of data. Elements are often repeated when dealing with transitions that fetch data. Therefore, one of the most essential issues in data mining is mining common item collections. Here we tackle an algorithm that finds common elements appearing in transitions and tries to split the data accordingly into different tables, called vertical fragmentation. This gives you high time efficiency. Here we use FP growth for the mining phase. Another smart splitting method has been proposed that converts the database into separate tables.