Data Integrity in Computerized System Review

The purpose of this literature review is to have a clear background on the major step forward and designs of automated systems and all that research that has been done before by other system developers. These research reviews were to make it easier for the designer to be fully acquainted with what is required in development of systems.

The reviews will be on data integrity in computerized system.

Data integrity refers to the overall completeness, accuracy and consistency of data. This can be indicated by the absence of alteration between two instances or between two updates of a data record, meaning data is intact and unchanged. Data integrity is usually imposed 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 enforced 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 is concerned with the concept of primary keys. The rule states that every table must have its own primary key and that each has to 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 of another table, or it can be null. Being null could simply mean that there are no relationships, or that the relationship is unknown. Domain Integrity: This 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 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 pervasive problem facing organizations these days is the semantic integrity of the data. As organizations store and process more and more data from various disparate sources, ensuring that the data is accurate is a colossal, but sometimes ignored, undertaking. Making sure that your data is correct requires proper design, processes that match your business requirements, good communication skills, and constant vigilance.

Semantic data integrity requires a deep understanding of the meaning of data and relationships that need to be maintained between different types of data. The DBMS provides options, controls and procedures to define and assure the semantic integrity of the data stored within its databases. Examples include triggers and referential integrity, as well as check 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 can be based on full-text or other content-based indexing Goodrum, Abby A. (2000)

Automated 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, journals and other documents.web search engines are the most visible IR application (Jansen, B. J. and Rieh, S. (2010)

An information retrieval process begins when a user enters a query into the system. Queries are formal statements of information needs, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of relevancy.

An object is an entity that is represented by information in a content collection or databases. User queries are matched against the database information. However, as opposed to classical SQL queries of a database, in information retrieval the results returned may or may not match the query, so results are typically ranked. This ranking of results is a key difference of information retrieval searching compared to database searching (Jansen, B. J. and Rieh, S. (2010)

Depending on the application the data objects may be, for example, text documents, images, audio, mind maps or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata Foote, Jonathan (1999)

According to Beel, Jöran; Gipp, Bela; Stiller, Jan-Olaf (200) Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.