BusinessRules and Data Models
Adatabase is defined as an organized set of info, reports,interpretations and other software tools. Data is normally arrangedto model features that aid the processing of information designs.Additionally, a database can also be termed as a set of relatedinformation based on its organization. In this case, DatabaseManagement Systems supports the collection of data and comprises ofincorporated arrays of computer programs. Users can interact with avariety of databases that offer access to all information found incomputer software (Coronel et al, 2016).
Databasessupport internal processes of institutions and companies. Theyreinforce electronic commerce with clients and traders databasesmanage transactions that are carried out by organizations. Forinstance, it holds information of systems such as website and webpagecontents that facilitate administrative operations. Similarly,databases may contain servers that run computer software and supportsnetworking techniques such as email systems that insert and sendmessages.
Databaseentities are things or items in a computersystem thatserves as theoretical levels that are not unique. The forms ofinformation that are kept in a database are called entities and thereexist different types that may include people, things, events andlocations. For instance, when creating a database for a college,students are the people, courses are the things, instructors teachingis the events and the college is the location that needs to beincluded in the database.
Forthe above entities, the courses taken by students could include modeof study and fee associated which each as well as name of theclasses. For the students, the following should be known totalnumber, names and addresses. With the college, one should know thename, location code and its organizational structure. When it comesto tracking instructor teaching, it is required that one should knowwhen and what time did learning took place and which courses weretaught at that particular period (Wang et al, 2013).
Businessrules are needed to define varied aspects of illustrations andprocesses of data. For instance, rules such as programmatic handlingof events through enforcing user-interface controls that managedatabase procedures as well as regulations that control the order ofautomated tasks within the software impact the structure of thedatabases. In this case, if the rules are apprehended, the questionis how to define them in database systems. Therefore, the knowledgeof the business rules is more useful and at the same time affects thestructure of a database.
Conceptualdata model is a summary- level model typically used on plannedinformation projects. Further, it frequently describes a wholeinitiative due to its great intellectual nature. It consists ofentities with no or restricted number of characteristics.Additionally, it comprises of related facts in entities. On the otherhand, the physical data model is total- accredited information whichrelies upon particular forms of data in a technological system. Itdescribes data necessities for a single application as well asincludes physical objects such as basic vital checks, security codesand folder stores (Boons et al, 2013).
Conclusively,business database models can illustrate business rules, terms, factsas well as software entities. Similarly, data presentationsdemonstrate information limits that manage business operations. Incases where business matters are technologically designed, particularprocedures can be taken to represent the rules in a data model.Finally, the performance of the database systems influence businessrules since attributes and entities are portrayed on a database modelas an evidence of sub-category business rule.
Coronel,C., & Morris, S. (2016). Database systems: design,implementation, & management. Cengage Learning.
Wang,S., & Wang, H. (2013). BusinessDatabase Technology: An Integrative Approach to Data SourceManagement with Practical Project Guides to Hands-on Exercises forStudents in Business Programs.Universal-Publishers.
Boons,F., & Lüdeke-Freund, F. (2013). Businessmodels for sustainable innovation: state-of-the-art and steps towardsa research agenda.Journal of Cleaner Production, 45, 9-19.