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