Creating an inverted index for features of a set of data elements can be quite helpful when the main objective is to have access to the most useful information in a database. If the system is designed correctly, an inverted index can also allow for fast data access.
The key to creating an inverted index for the features of a set of data elements is to have a table that contains an equal number of primary keys that each point to another data element that is related to the primary key. The most appropriate index will contain information on the primary key itself, as well as information on the primary key’s subkeys and other associated values.
There are two types of indexes – the full-text index (TFI) and the clustered index (CLI). The TFI is usually called the inverse index, since it takes the original key and then adds the index key and values into the resulting index. The CLI indexes are also called the inverse index because they combine the original key with the index values.
When choosing an index for a set of data elements, one should consider the features that will need to be indexed and how the data can be accessed by the index. For example, if there is an index on an employee’s birth date, the index will need to contain information about the employee’s birth dates. However, the index will not necessarily need to contain data about employees in the previous or following month. The same is true of indexes for other types of data.
If a database has an index on a full-text google inverted index, it will need to contain information about every occurrence of that term within a text document. An index on a CLID (comparable life events) index will need to include a record of when that term occurred. In a clustered index, the index will contain information on all of the occurrences of a feature in a document. An index on a TFI index will contain a record of when that feature occurred; however, it will not contain a record of when it did not occur.
The advantages of an index of this type are that it contains an updated list of the most recent occurrences of a feature. This is necessary when an index can be used to find the most relevant data and when it can be used to provide more information than the actual date of the occurrence. The disadvantages of this index are that an index can become outdated quickly, and that if the original key was never deleted, a new key must be created.