![]() The more advance processing is related to database normalisation techniques. If some column may have some cells that remain empty, it's an optional relationship. On the other side, we see that the same value can be repeated, so you can fine-tune the relationship as one-to-many. primary key in Table1 is unique, so it'll be a one-to-xxx relationship. You then need to find out about the cardinality (i.e. This will allow you to draw the relationships between tables. We can also see that some values in LokaalId are repeated several times in Klaslookal. ![]() Of course, the name is not a guarantee, but cross-checking it appears that every value of Klaslokaal exist in LokaalId. Candidate 1: Table 1, column Klaslokaal seems to refer to table 2, identifying column LokaalId.Obviously, you'd better check if the hypothesis works: Sometimes they have similar names and there is obviously the same values as content. In particular, you'll look for the identifier columns of one table to see if it is used in another table. You then need to find out columns in one table that look like columns in the other table. Table 2: The first column looks like an identifier for a room, and the second column is the name of the campus of that room.Īs a first measure, you can represent each of these table as a distinct entity in your model, with its attributes, and identify the primary key (PK).The remaining columns describe attributes of the course, such as language, timing, day of the week, number of students, etc. Table 1: The first column (ending with id) seems to be the identifier of the courses.The very basic approach is to look at each file, list the columns, and find out if any column uniquely identifies the lines in the table:
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