The purpose of this literature review is to have a clear overview of the main advances and designs of automated systems and all the research that has been previously carried out by d other system developers. These research reviews were intended to make it easier for the designer to become familiar with what is required in systems development. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay Critiques will focus on the integrity of data in a computerized system. Data integrity refers to the overall completeness, accuracy, and consistency of data. This can be indicated by the absence of corruption between two instances or between two updates of a data record, meaning that 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 checking methods and validation procedures. Boritz, J. (2011) Data integrity is enhanced in hierarchical and relational database models. The following three integrity constraints are used in a relational database structure to ensure data integrity: Entity Integrity: This relates to the concept of primary keys. The rule states that each table must have its own primary key and 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 a primary key value from another table, or it may be null. Being zero could simply mean that there is no relationship or that the relationship is unknown. Domain Integrity: This indicates that all columns in a relational database are within a defined domain. The concept of data integrity ensures that all data in a database can be traced and connected 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" can mean different things to different people, but the most difficult and widespread problem facing organizations today is the semantic integrity of data. As organizations store and process more and more data from a variety of disparate sources, ensuring data accuracy is a massive, but sometimes overlooked, undertaking. Ensuring your data is correct requires proper design, processes tailored to your business requirements, good communication skills and constant vigilance. Semantic data integrity requires a deep understanding of the meaning of data and the relationships that should be maintained between different types of data. . The DBMS provides options, controls and procedures to define and ensure the semantic integrity of data stored in its databases. Examples include triggers and referential integrity, as well as verification constraints.2.1 Data Integrity in Information RetrievalInformation retrieval (IR) is the activity of obtaining relevant information resources for a need information from a collection of resources..