The purpose of this literature review is to get a clear picture of the major breakthrough and design of automated systems and all the research previously done by other system developers . These research reviews were intended to make it easier for the designer to fully understand what is required in systems development. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Reviews will address the integrity of the data in the computerized system. Data integrity refers to the overall completeness, accuracy, and consistency of the data. This can be indicated by no alterations between two instances or between two updates of a data record, meaning the data is intact and unchanged. Data integrity is usually enforced during the database design phase through the use of standard procedures and rules. Data integrity can be maintained through the use of various error control methods and validation procedures. Boritz, J. (2011) Data integrity is applied in both hierarchical and relational database models. The following three integrity constraints are used in a relational database structure to achieve data integrity: Entity integrity: This concerns the concept of primary keys. The rule states that each table must have its own primary key and that each must be unique and not null. Referential integrity: This is the concept of foreign keys. The rule states that the foreign key value can be in two states. The first state is that the foreign key value would refer to the primary key value of another table or it could be null. Being null could simply mean that no relationships exist or that the relationship is unknown. Domain Integrity: States that all columns in a relational database are in a defined domain. The concept of data integrity ensures that all data in a database can be tracked and linked to other data. This ensures that everything is recoverable and searchable. Having a single, well-defined and well-controlled data integrity system increases stability, performance, reusability and maintainability. The term “data integrity” may mean different things to different people, but the most difficult and pervasive problem facing organizations today is semantic data integrity. As organizations store and process more and more data from disparate sources, ensuring that data is accurate is a monumental, yet sometimes overlooked, undertaking. Ensuring that data is correct requires proper design, processes that meet business requirements, good communication skills and constant vigilance. Semantic data integrity requires a deep understanding of the meaning of the data and the relationships that must be maintained between different types of data. . The DBMS provides options, controls, and procedures to define and ensure the semantic integrity of the data stored in its databases. Examples include triggers and referential integrity, as well as control constraints. 2.1 Data Integrity in Information Retrieval Information retrieval (IR) is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches may be based on full text or other content-based indexing Goodrum, Abby A. (2000) Automatic information retrieval systems are used to reduce what has been called "information overload." Many universities and public libraries use IR systems to provide access to books,.