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Statistical Methods for Big Data (MATH97136)
Coursework Assignment
Introduction
This is the coursework assignment associated with the Big Data in Statistics module, M5MS15. The
hand-out date for these instructions is 25th February 2020. A report response is to be submitted by
1800hrs on 1st April 2020. An electronic copy of the report (in Word, PDF or iPython notebook
format) should be submitted via the module’s Blackboard page.
Data
The dataset is a modified version of the VAST 20161 Mini-Challenge 1 data. Some information taken
from the VAST 2016 website is repeated here, to ensure that these instructions are self-contained.
At the end of 2015, a [fictitious] growing organisation, GAStech, moved into a new, state-of-the-art,
three-story building near to their previous location. The new office is built to the highest energy
efficiency standard, but as with any new building, there are still several heating, ventilation, and air
conditioning (HVAC) issues to work out. The building is divided into several HVAC zones. Each zone is
instrumented with sensors that report building temperatures, heating and cooling system status
values, and concentration levels of various chemicals such as Carbon Dioxide (abbreviated CO2) and
Hazium (abbreviated Haz), a recently discovered and possibly dangerous chemical. CEO Sten
Sanjorge Jr. has read about Hazium and requested that these sensors be included. However, they are
very new and very expensive, so GAStech can afford only a small number of sensors.
With their move into the new building, GAStech also introduced new security procedures, which
staff members are not necessarily adopting consistently. Staff members are required to wear
proximity (prox) cards while in the building. The building is instrumented with passive prox card
readers that cover individual building zones. The prox card zones do not generally correspond with
the HVAC zones. When a prox card passes into a new zone, it is detected and recorded. As part of
the deal to entice GAStech to move into this new building, the builders included a free robotic mail
delivery system. This robot, nicknamed Rosie, travels the halls periodically, moving between floors in
a specially designed chute. Rosie is equipped with a mobile prox sensor, which identifies the prox
cards in the areas she travels through.
The building is partitioned into different zones, across three floors, as depicted in the three figures
below.
1 http://vacommunity.org/VAST+Challenge+2016

There are four datasets provided, covering May 31 to June 13, 2016. The data are as follows:
• Fixed proximity sensor data reading employees’ prox cards (prox-fixed.csv);
• Mobile proximity sensor data (from Rosie) reading employees’ prox cards (prox-mobile.csv);
• Environmental conditions of the building (bldg-measurements.csv) – see Annex A for further
details;
• Hazium concentration within the building (f1z8-haz.csv), containing the Hazium
concentration on floor 1, zone 8.
Acquiring the data
These instructions assume that you have successfully completed Exercise 1 of Week 1. If you have not
done so then please complete this exercise before proceeding with the coursework.
Please log on to bazooka. A unique dataset is to be generated for each student, using the following
commands:
\$ cd ~/bd-sp-2017
\$ cd coursework
\$ chmod +x *.py
\$ ./process_data.py /tmp/coursework/prox-fixed.csv \
\$ ./process_data.py /tmp/coursework/prox-mobile.csv \
\$ ./process_data.py /tmp/coursework/bldg-measurements.csv \
\$ ./process_data.py /tmp/coursework/f2z2-haz.csv \
\$ cd ../data
\$ ls -la
You should see four new files in the data directory corresponding to the four data files (proxfixed.csv,
prox-mobile.csv, bldg-measurements.csv, f2z2-haz.csv). Please run the following
commands and record the output of each command at the top of your coursework report
submission.
\$ md5sum prox-fixed.csv
\$ md5sum prox-mobile.csv
\$ md5sum bldg-measurements.csv
\$ md5sum f2z2-haz.csv
Create a folder in HDFS called coursework. You should now upload these four data files to your
coursework folder on HDFS.
Map Reduce
For questions 1-4 below, write a Map Reduce program to compute the required answer. Your
response to each of these questions should consist of three components: (1) your answer to the
question; (2) the Shell command used to execute the Map Reduce program; (3) Python code
developed and used to compute the answer. The code will be checked for execution quality, so
please ensure that the code is self-contained and executable. (Marks will be deducted for code that
does not execute using the commands provided via component (2).)
1. Using both prox-fixed and prox-mobile datasets, produce a diagram that displays the number of
staff members present in the building on each day (i.e. number of unique prox-ids on each day)?
NB: The x-axis may be marked with day number (i.e. 0, 1, 2, …) from the beginning of the
dataset. [8 marks]
2. Using the prox-fixed dataset, what is the (floor, zone) of the most visited location in the
building? [5 marks]
3. Using both datasets, what is the prox-ID of the most active staff member (i.e. the staff member
with the greatest number of prox card readings) on 2nd June 2016? [5 marks]
4. Using the bldg-measurements dataset, produce a time series plot of the average hourly “Total
Electric Demand Power”. (This should be a single plot, with the x-axis denoting hour of day, with
a range of 0hrs-23hrs.) What does this plot indicate about power usage throughout the day? [5
marks]
Spark
With the exception of question 8, for the following questions, write a sequence of Spark commands
(that are executed in the Spark REPL) to compute the required answer. For each question, the full
sequence of Scala commands should be pasted into your submission, together with the computed
answer, and any other information requested. The code will be checked for execution quality, so
please ensure that the code is self-contained and executable. (Marks will be deducted for code that
does not execute using the sequence of commands provided in your coursework submission.)
5. Parse the prox-fixed.csv data fie into an RDD[ProxReading], where ProxReading is defined as:
case class ProxReading(timeStamp: org.joda.time.DateTime, id: String, floorNum:
String, zone: String). In this class, timestamp corresponds to a joda DateTime object2
, id
corresponds to prox-id, floorNum corresponds to the floor number, zone corresponds to the
zone id. [2 marks]
6. Using the prox-fixed dataset, what is the (floor, zone) of the most visited location in the building
across the complete dataset? [3 marks]
7. Using both datasets, what is the prox-ID of the most active staff member (i.e. the staff member
with the greatest number of prox card readings) on 7th June 2016? [3 marks]
8. Provide a concise summary of your experiences writing Map Reduce programmes and Spark
commands for questions 6 and 7. Comment on the differences between the two computational
platforms. [2 marks]
9. Construct an appropriate RDD from the bldg-measurements.csv data, containing the
“Date/Time” column (number 1) and “F_2_Z_1 VAV REHEAT Damper Position” column (number
193). What is the date and time of the first occurrence of the F_2_Z_1 VAV REHEAT Damper
Position being fully open, i.e. the earliest date and time of variable “F_2_Z_1 VAV REHEAT
Damper Position” being set to its maximum value of 1.0? [3 marks]
10. A rogue employee is believed to be increasing the Hazium concentration in the building by
modifying the Reheat Damper position (“F_2_Z_1 VAV REHEAT Damper Position”). By using the
Spark package MLlib3 or other Spark command sequence, demonstrate a statistical association
between the Hazium concentration (from f2z2-haz.csv) and the “F_2_Z_1 VAV REHEAT Damper
Position” variable. Provide a concise summary of your statistical findings, using diagrams where
appropriate.
[10 marks]
11. By using the (fixed) proximity location data, determine the employee IDs for those that entered
the Server Room (the HVAC control location) prior to the sudden increase in Hazium
concentration at the end of the dataset (i.e. employees in the Server Room on 10th June 2016).
[3 marks]
General
12. Write a question, that could appear in next year’s coursework paper, which tests a student’s
understanding of the opportunities and problems with using Big Data technology. [10 marks]
13. Identify, download, and perform a statistical analysis of any suitable data that is available on the
Spark, or both tools. Please note that the data need not be “big” – the question is intended to
assess your approach to the analysis, and how you utilise Big Data technology in performing a
statistical analysis. [20 marks]
a. Write a short (less than one side of A4) synopsis of the paper, extracting the key statistical
contributions of the paper. [15 marks]
b. Discuss how the key points raised in the paper could be relevant (if at all) to the statistical
analysis performed in question 13, linking to other research if and where appropriate. [25
marks]
2 http://joda-time.sourceforge.net/apidocs/org/joda/time/DateTime.html 3 https://spark.apache.org/docs/1.2.1/mllib-guide.html
Annex A – Building measurement information
Field Units Description
F_#_BATH_EXHAUST:Fan
Power
[W]
Power used by the bathroom exhaust fan
F_#_VAV_SYS AIR LOOP
INLET
Mass Flow Rate
[kg/s] Total flow rate of air returning to the HVAC system from all
zones it serves
F_#_VAV_SYS AIR LOOP
INLET
Temperature
[C] Mixed temperature of air returning to the HVAC system from
all zones it serves
F_# VAV Availability
Manager
Night Cycle Control Status
On/off status of the HVAC system during periods when the
system is normally scheduled off. The night cycle manager
cycles the HVAC system to maintain night and weekend set
point temperatures.
F_#_VAV_SYS COOLING
COIL
Power [W] Power used by the HVAC system cooling coil
F_#_VAV_SYS HEATING
COIL
Power
[W] Power used by the HVAC system heating coil
F_#_VAV_SYS SUPPLY FAN
OUTLET
Mass Flow Rate [kg/s]
Total flow rate of air delivered by the HVAC system fan to the
zones it serves
F_#_VAV_SYS SUPPLY FAN
OUTLET
Temperature [C] Temperature of the air exiting the HVAC system fan
F_#_VAV_SYS SUPPLY
FAN:Fan
Power
[W]
Power used by the HVAC system fan
F_#_VAV_SYS Outdoor Air
Flow
Fraction
Percentage of total air delivered by the HVAC system that is
from the outside
F_#_VAV_SYS Outdoor Air
Mass
Flow Rate
[kg/s] Flow rate of outside air entering the HVAC system
COOL Schedule Value
The supply air temperature set point. Air exiting the HVAC
system fan is maintained at this temperature during cooling
operation
DELI-FAN Power [W] Power used by the deli exhaust fan
Drybulb Temperature [C] Drybulb temperature of the outside air
Wind Direction [deg] Direction of wind outside of the building
Wind Speed
[m/s]
Speed of wind outside of the building
HEAT Schedule Value
The supply air temperature set point. Air exiting the HVAC
system fan is maintained at this temperature during heating
operation
Pump Power [W] Power used by the hot water system pump
Water Heater Setpoint Water heater set point temperature
Water Heater Gas Rate
[W]
Rate at which the water heater burns natural gas
Water Heater Tank
Temperature
[C]
Temperature of the water inside the hot water heater
Loop Temp Schedule
Temperature set point of the hot water loop. This is the
temperature at which hot water is delivered to hot water
appliances and fixtures.
Supply Side Inlet Mass
Flow Rate [kg/s] Flow rate of water entering the hot water heater
Supply Side Inlet
Temperature
[C] Temperature of the water entering the hot water heater
Supply Side Outlet
Temperature
[C] Temperature of the water exiting the hot water heater
F_#_Z_# REHEAT COIL
Power
[W]
Power used by the zone air supply box reheat coil
F_#_Z_# RETURN OUTLET
CO2
Concentration
[ppm] Concentration of C02 measured at the zone's return air grille
F_#_Z_# SUPPLY INLET
Mass
Flow Rate
[kg/s] Flow rate of the air entering the zone from its air supply box
F_#_Z_# SUPPLY INLET
Temperature
[C] Temperature of the air entering the zone from its air supply
box
F_#_Z_# VAV REHEAT
Damper
Position
Position of the zone's air supply box damper. 1 corresponds
to fully open, 0 corresponds to fully closed
F_#_Z_#: Equipment
Power
[W] Power used by the electric equipment in the zone
F_#_Z_#: Lights Power [W] Power used by the lights in the zone
F_#_Z_#: Mechanical
Ventilation Mass Flow Rate
[kg/s] Ventilation rate of the zone exhaust fan
F_#_Z_#: Thermostat
Temp
[C] Temperature of the air inside the zone
F_#_Z_#: Thermostat
Cooling
Setpoint
[C] Cooling set point schedule for the zone
F_#_Z_#: Thermostat
Heating
Setpoint [C] Heating set point schedule for the zone
Total Electric Demand
Power
[W] Total power used by the building
HVAC Electric Demand
Power
[W] Total power used by the building's HVAC system including
coils, fans and pumps.