Role of Candidate Key in Metadata for Data Analysis

This proposed research paper focuses on the candidate key to generate a frequent pattern from the large dataset. The functional dependency and candidate key rolling are major activities for creating and collecting the exact pattern to support the decision support system. The functional dependency helps a decision support system to assemble metadata to resolve the uncertainty, unstructured, and unordered data. The large data sets (BigData) can be reassembled through Java programming faster and better by applying combinational algebra. The candidate key is directly proportional to the set of metadata. The large data sets are lively connected to the customer, manufacturer, vendor, order, and product key correctly at the right time. Therefore, the computational mechanism has to develop through the candidate key for better and faster data analysis. The knowledge and decision pattern can be acquired through mapping and integration of candidate key management.