MIS602: Data Modelling & Database Design Assessment 1 Report

This report evaluates a student's data modelling and database design assignment for the MIS602 course, focusing on an enrollment system. The report assesses the suitability of data objects and attributes, highlighting issues with the Entity Relationship Diagram (ERD) notations and inconsistencies across different versions of the database design. It identifies errors such as data deletion instead of updates and inconsistencies in data naming conventions. The report suggests improvements, emphasizing the need for more detailed data, unique identifiers, and standardized naming conventions to ensure data integrity, atomicity, and consistency. The student also provides a brief overview of data, data attributes, and the importance of consistency, integrity, and constraints in database design, with references to relevant research papers.

Running head: DATA MODELLING & DATABASE DESIGN
DATA MODELLING & DATABASE DESIGN
Name of the Student
Name of the University
Author Note

1DATA MODELLING & DATABASE DESIGN
Suitability of the data objects and attributes
Suitability of the data objects and attributes depends on the real world objects. The
Object here is the Enrolment system of a University. Select attributes are Serial Number, Student
Name, courses enrolled and faculty name which is appropriate for the scenario. Serial number is
represented using numeric values properly (Data, 2015). Whereas the student name, course name
and faculty name has been represented using string characters. The summary document includes
all the explanation of the developed versions of database (Shao & Goodson, 2014). However the
modelling of the database design is incorrect. The notations in Entity relationship diagram are
incorrect as per the chen’s foot notation of modelling.
In version 2, the task is to update last two rows then add additional 5 rows of data. The
data which are supposed to be updated, are actually deleted in the sheet. Also all the versions are
continuation of each other in order of version 1 to version 4. Moving from version 2 to version 3
should not recall deleted data in version2. Similarly, in version 4, the data of the deleted columns
should not appear anywhere in the sheet. The highlighting of the deleted columns with yellow
color is for the entire columns.
Future Improvements
For the future Improvements, the main concern should be the data. The more detailed
information will be saved, it can retrieve analytical facts using those data. For the enrolment
scenario, more attributes were required to use such as students contact and academic details
along with unique course names. Atomicity is an important aspect and feature of the databases.
Each record (row) should be unique in its own way. Next, the inconsistency in naming data can
be avoidable by using full names in the database. Full names help in identifying the unique

2DATA MODELLING & DATABASE DESIGN
identifications of the rows. Faculty name also has the inconsistency in the naming of the input.
Some rows have full names and some have only first name. The naming convention should also
be followed for the attributes name, using capitalization of new starting word or using
underscore between two words (Ryabchikova et al, 2015). Sr. no and Course name bring
inconsistency in the naming conventions which will create difficulties in querying. Also unique
IDs such as student_id and faculty_id identifies their respective dependent data in the system.
Hence; it should be defined first rather than dependencies.
Understanding of data and data attributes
Data is an information which provides the power of analysis to the company and
organization. Data objects can be referred as the entity. The entities are inspired from the real
world scenario. On other hand the data attributes are the columns that stores a specific type of
data. The attributes are set to their respective data types and values. Consistency, Integrity and
constraints are the effective way of using office excel work (Vedaldi et al., 2014). It can be
achieved by following the proper chen’s model along with the proper attribute and data types. In
conclusion the overall selection of attribute and data types have been implemented are suitable
for the web page.

3DATA MODELLING & DATABASE DESIGN
References
Data, M. H. (2015). Database Design. Perancangan Basis Data) merupakan salah satu.
Ryabchikova, T., Piskunov, N., Kurucz, R. L., Stempels, H. C., Heiter, U., Pakhomov, Y., &
Barklem, P. S. (2015). A major upgrade of the VALD database. Physica Scripta, 90(5),
054005.
Shao, M., & Goodson, G. R. (2014). U.S. Patent No. 8,793,466. Washington, DC: U.S. Patent
and Trademark Office.
Vedaldi, A., Mahendran, S., Tsogkas, S., Maji, S., Girshick, R., Kannala, J., ... & Taskar, B.
(2014). Understanding objects in detail with fine-grained attributes. In Proceedings of the
IEEE conference on computer vision and pattern recognition (pp. 3622-3629).