Conceptual Model Logical Model

Figure 4.2. MICES-Quan DFD Level 1

b. Conceptual Model

Conceptual model explain the concept of database which is the process of constructing a model of the information used in an enterprise. According to Connolly and Begg 2002 explained the conceptual design phase begins with the creation of a conceptual data model of the enterprise, which is entirely independent of implementation details such as the target of DBMS, application program, programming languages, hardware platform, performance issues, or any other physical consideration. A Conceptual model describes the essential semantics of system data. A conceptual model consists of a number of symbols joined up according to certain conventions. Commonly, conceptual modeling use symbols from a modeling method known as entity-relationship analysis. This method was first introduced by Chen in 1976 and now is widely used Hawryszkiewycz, 1994. Conceptual modeling deals with the question on how to describe in a declarative and reusable way the domain information of an application, its relevant vocabulary, and how to constrain the use the data, by understanding what can be drawn from it. The entity relational of MICES-Quan system illustrated in Figure 4.3. The analysis of ER describes three main part such: ♣ Entities, object that have independent physical or conceptual existence. ♣ Relationship, which are meaningful interactions between the entities, ♣ Attributes, which are the properties of the entities and relationship. Figure 4.3. MICES-Quan ERD

c. Logical Model

Logical model explain the logic of database which is the process of constructing a model of information used in an enterprise based on specific data model, but independent of a particular DBMS and other physical consideration. The logical model describes structure database in a particular Data Description Language DDL. According to Shekhar et al 1999, logical modeling phase is related to the actual implementation of the conceptual data model in database management system. Logical model identifies the requirement of data. The logical model of MICES-Quan showed Table 4.1. Table 4.1. MICES-Quan logical data model No Category Entity Attribute reserve_addition Company_id, Year, Quarterly, oreamount_unit, oreamount_plan, oreamount_real production_processing Company_id, Year, Quarterly, minedromore_unit, minedromore_plan, minedromore_real Mining_operation Company_id, Year, Quarterly, openedarea_unit, openedarea_plan, openedarea_real 1 Mining Engineering stockpile Company_id, Year, Quarterly, lowlevelorestock_unit, lowlevelorestock_plan, lowlevelorestock_real air_quality Company_id, Year, Quarterly, sulfuroxide _unit, sulfuroxide _plan, sulfuroxide _real water_quality Company_id, Year, Quarterly, arsen_unit, arsen_plan, arsen_real cutting Company_id, Year, Quarterly, cuttingarea_unit, cuttingarea_plan, cuttingarea_real 2 Environment Mining Monitoring reclamation Company_id, Year, Quarterly, reclamationarea_unit, reclamationarea_plan, reclamationarea_real Standard_airquality sulfuroxide_standard, nitrogendioxide_standard 3 Standard Standard_waterquality merqury_standard, arsen_standard, chloride_standard No Category Entity Attribute Company Company_id, Company_name, Company_Field, Location, Username, Password User User_id, User_group, User_name, Password 4 Other User_Group Number, User_group

d. Physical Model