Indexing techniques for improving structured, semi-structured and unstructured database performance

The main purpose of this thesis is to study the difference between structured, semi-structured and unstructured data. To set up the right database, it is important to know the difference between relational databases, which store structured data, and NoSQL databases, which store semi-structured and unstructured data. Once the various concepts have been assimilated, it is time to set up the appropriate database. Once the database has been chosen and implemented, it is vital to maintain good performance. Since data is a vital commodity these days, it is important to be able to extract and analyse it efficiently. There are many tools available for this purpose, including indexes. Indexes make it possible to extract data efficiently. To do so, it is important to know which indexes to use, depending on the data and queries being executed. There are many different indexes for indexing data in the most appropriate way. In addition, some databases index data automatically, so that it is important to keep track of all the indexes in the database. Moreover, it is important to create only useful indexes, because creating indexes, even though it improves performance, requires storage space. So it is crucial to create them only where they are needed. Through the tests, it is possible to get a partial view of the performance improvement when creating indexes. Last but not least, it is also possible to compare the time improvement between different indexes on the same database column or field, to illustrate the theoretical part. This thesis proposes some tools to give an initial idea of how to set up and analyse indexes in databases based on diverse data models