DATA MANAGEMENT PROJECT: FOOD AFFORDABILITY

Task 1: Data Management

B. (i) Data- Dictionary

Table 1:
Attributes	Data Type	Constraints
ID	AutoNumber	Primary Key
Race Name	Text	-
US Country Name	Text	-
Region Name	Text	-
Cost per year of food in the named region	Number	-
Median Income	Number	Foreign Key

Table 1: Data- Dictionary

Benefits of Data Dictionary

• Acts as a valuable reference for the organisation from where key information could be retrieved.
• Leads to form establishment of users information acts as a network of communication between system analyst and potential users

Benefits of Documentation

• Making up for the components of the application with up-to-date information and also facilitates the process of developing web services.
• Improving quality and productivity of the process thus reducing requirements of supports system

Discussing query language

For handling this type of database, SQL query language has been used. This query language is used for delivering specific information about the users and its application is based on procedural extensions with other programming languages

Task 2: Analysis and Interpretation

B. Sort median income in descending order

SELECT [Table 1_1].[Median Income]

FROM [Table 1] AS [Table 1_1]

ORDER BY [Table 1_1].[Median Income] DESC;

The user needs to write this query for calculating the median income. Here, Median Income is selected from Table 1 and it is an order in descending by the query “DESC”.

Figure 1: Descending Order results

(Source: Created in MS Access)

 

C. State the maximum and minimum median income

SELECT MAX(salary), MIN(salary)

     FROM Median Income;

Here the user has to use query MAX and MIN for finding the values. Here the maximum salary is 112813 and the minimum salary is 35238.

Figure 2: Maximum and Minimum Salary results

(Source: Created in MS Access)

 

 

 

D. Discuss the ethnic group(s), average family size(s) and region(s) associated with the maximum and minimum median incomes

Income inequality is the measurement of the economic gap between rich and poor. It has risen steadily in the United States. More recently, the issue is trusted in the public with different movements in 2011 and subsequent calls for $15 with the wage (Ahmed et al. 2017). It is an important part of rising inequality and needs to experience within the communities.

The income equality in the U.S is required to move on the greatest part among the Asians. The gap is required for standard size between the top and the bottom of the ladder of the income.

 

E. Discuss the affordability ratio

While the housing cost burden is not the most common factor among the lowest income renters. These are required for less than $15,000 a year for making a major difference. It also increases the income levels in between this time.

 

F. Discuss the amount spent and ethnic group(s) associated

SELECT [Table 1_1].[Cost per year of food in the named region]

FROM [Table 1] AS [Table 1_1]

ORDER BY [Table 1_1].[Cost per year of food in the named region] DESC;

Here the desc function is used to calculate the value for cost per year for food.

Task 3: Transforming Data

A. Recalling

        SELECT *  FROM Median Income;

        SELECT *  FROM Cost per year of food in the named region;

        SELECT * FROM Region Name

 

B. Consider line 38, 111, 253 of the data set

            CREATE TABLE

             (

ColumnName, Datatype, Optional Column Constraint,

ColumnName, Datatype, Optional Column Constraint,

Optional table Constraints

);

It needs to create the table and select the data type for examining different attributes with the functions.

 

C. Rewrite 3 three lines based on Column

Figure 3: Column based view

(Source: Created in MS Access)

The three lines are used in the database and these are in column based format.

Task 4: Unstructured data

Challenges of Unstructured Data  

• Users may face the problem regarding unchecked growth of the data thus leading to the unnecessary pilling-up of undesired variables.
• It makes unethical use of the system’s capacity and sometimes it becomes difficult to search the area where manipulation might have occurred

Benefits of Unstructured Data

• It leaves the space for adding additional variables which may be used in the future for enhancing users’ experience
• It contributes to the integration of various information thus broadening the landscape of doing the operation

 

B. Collecting unstructured data

Potential examples are as follows;

• Eating habits of varying age groups
• Seasonal fluctuations in eating habits
• Varying eating habits based on a number of family numbers

Task 5: Generation and Recommendations

A recommendation would include collecting data on income levels, presence of any health complications, children and infant and maximum age groups of people (Neves & Bordawekar, 2018). Since nutritious food appears to be expensive compared to other groups, it is evident that people belonging to greater income brackets would eat more nutritious food than junk food (Sukthankar et al. 2017). Families having a higher number of children and young people tend to have more junk food compared to nutritious food (Csiszár et al 2016). On the other hand, families with elder people are expected to have more nutritious food than junk food. 

 

 

 

References

Ahmed, S., Zaman, A., Zhang, Z., Alam, K. M. R., & Morimoto, Y. (2017, November). Semi-order Preserving Encryption Technique for Numeric Data to Enhance Privacy. In 2017 Fifth International Symposium on Computing and Networking (CANDAR) (pp. 68-74). IEEE. Retrieved on 14^th January 2020 from: https://www.jstage.jst.go.jp/article/ijnc/9/1/9_111/_pdf

Csiszár, C., Caesar, B., Csonka, B., & Földes, D. (2016). Transportation Information Systems I.: Study-aid for practices in computer laboratory. Retrieved on 15^th January 2020 from: https://www.shaon.org/assets/files/research/sgx-big-matrix.pdf

Neves, J. L., & Bordawekar, R. (2018). Demonstrating AI-enabled SQL Queries over Relational Data using a Cognitive Database. Knowledge Discovery and Data Mining. Retrieved on 15^th January 2020 from: https://www.kdd.org/kdd2018/files/deep-learning-day/DLDay18_paper_20.pdf

Sukthankar, N., Maharnawar, S., Deshmukh, P., Haribhakta, Y., & Kamble, V. (2017, July). nQuery-A Natural Language Statement to SQL Query Generator. In Proceedings of ACL 2017, Student Research Workshop (pp. 17-23). Retrieved on 14^th January 2020 from: https://www.aclweb.org/anthology/P17-3004