We write, we don’t plagiarise! Every answer is different no matter how many orders we get for the same assignment. Your answer will be 100% plagiarism-free, custom written, unique and different from every other student.
I agree to receive phone calls from you at night in case of emergency
Please share your assignment brief and supporting material (if any) via email here at: [email protected] after completing this order process.
No Plagiarism Guarantee - 100% Custom Written
Here, you are to provide a high level data warehouse architecture design for a large stated owned water utility that incorporates big data capture, processing, storage and presentation in a diagram and describe its main aspects. Furthermore, identify and explain the key security privacy and ethical concerns for organisations within a specific industry.
Task 2 (Worth 30 marks)
Research the relevant literature on how big data analytics capability can be incorporated into a data warehouse architecture. Note Chapter 2 Data Warehousing and Chapter 6 Big Data and Analytics of Sharda et al. 2014 Textbook will be particularly useful for a nswering some aspects of Task 2.
Task 2.1 Provide a high level data warehouse architecture design for a large stated owned water utility that incorporates big data capture, processing, storage and presentation in a diagram called Figure 1.1 Big Data Analytics and Data Warehouse Combined.
Task 2.2 Describe and justify the main components of your proposed high level data warehouse architecture design with big data capability incorporated presented in Figure 1.1 with appropriate in-text referencing support (about 750 words).
Task 2.3 Identify and discuss the key security privacy and ethical concerns for organisations within a specific industry that are already using a big data analytics and algorithmic approach to decision making with appropriate in-text referencing support (about 750 words).
Task 3 (Worth 30 marks)
LAPD Crime Analytics Unit would like to have a Crime Events dashboard built with the aim of providing a better understanding of the patterns that are occurring in relation to different crimes across the 21 Police Department areas over time in the City of Los Angeles. In particular, they would like to see if there are any distinct patterns in relation to (1) types of crimes, (2) frequency of each type of crime across each of the 21 Police Department areas for years 2012 through to first quarter of 2016 based on the data set. Note this is a large data set containing over 1 Million records. This Crime Events dashboard will assist LAPD to better manage and coordinate their efforts in catching the perpetrators of these crimes and be more proactive in preventing these crimes from occurring in the first place.
The LAPD Crime Analytics Unit wants the flexibility to visualize the frequency that each type of crime is occurring over time across each of the 21 Police Department areas/districts in the City of Los Angeles. They want to be able to get a quick overview of the crime data in relation to category of crimes, location, date of occurrence and frequency that each crime is
occurring over time and then be able to zoom in and filter on particular aspects and then get further details as required.
LA Crimes Data Set Data Dictionary
variable name
type
Description
year_id
1. character
Original dataset id
date_rptd
2. date
Date crime was reported
dr_no
3. character
Count of Date Reported
date_occ
4. date
Date crime occurred
time_occ
5. date
Time crime occurred on a day
area
6. character
Area Code
area_name
7. character
Area geographical location
rd
8. character
Nearby road identifier
crm_cd
9. character
Crime type code
crm_cd_desc
10. character
Crime type description
Status
11. character
Status code
status_desc
12. character
Status outcome of crime
location
13. character
Nearby address location
cross_st
14. character
Nearby cross street
lat
15. numeric
Latitude of crime event
long
16. numeric
Longitude of crime event
year
17. numeric
Year of crime occurred
month
18. numeric
Month of crime occurred
day_of_month
19. numeric
Day of month crime occurred
hour_of_day
20. numeric
Hour of day crime occurred
month_year
21.
Month and year when crime occurred
day_of_week
22. character
Day of week crime occurred
weekday
23. character
Weekday/weekend classification for crime
event
intersection
24. character
Occurred at an intersection
crime_classification
25. character
subjective binning of crimes
Task 3 requires a Tableau dashboard consisting of four crime event views of the LA Crimes
2012-2016 data set.
Task 3.1 Specific Crimes within each Crime Category for a specific Police Department Area and specific year
Task 3.2 Frequency of Occurrence for a selected crime over 24 hours for a specific Police
Department Area
Task 3.3 Frequency of Crimes within each Crime Classification by Police Department Area and by Time
Task 3.4 Geographical (location) presentation of each Police Department Area for given crime(s) and year. Note for this task you will need to make use of the geo-mapping capability of Tableau Desktop.
You should briefly discuss the key findings for each of these four views in your
Crimes Event Dashboard
