Difference between revisions of "IS428 2016-17 Term1 Assign3 Lim Lai Ho"

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'''Building Data'''
 
'''Building Data'''
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First, I cleaned the the data by removing those unwanted data. Then by using JMP, I have to transpose the column to row based on the timestamp.
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[[File:Stack.png|300px]]

Revision as of 20:28, 23 October 2016

Problem & Motivation

After the successful resolution of the 2014 kidnapping at GAStech’s Abila, Kronos office, GAStech officials determined that Abila offices needed a significant upgrade. At the end of 2015, the growing company moved into a new, state-of-the-art three-story building near their previous location. Even though the employee morale rose somewhat with the excitement of the new building, there are still a few disgruntled employees in the company.

The new office is built to the highest energy efficiency standard, but as with any new building, there are still several HVAC issues to work out. The building is divided into several HVAC (heating, ventilation, and air conditioning) 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.

This assignment aims to explore the following subjects:

  • The movement of employees so that the security department can have better understanding and implement better security measures.

Understanding The Data

Employees Movement Data

In order to explore the movement of the employees, I have to look into the employees' data records. From the proxMobileOut.csv and proxOut.csv files, I realised that the prox ID is constructed from the employee's name and the number of times she/he has obtained the pass.

Employee id.jpg Employee id 2.jpg

Therefore, using the formula as shown above, I added a new column for the employee ID on each row.

Based on proxMobileOut.csv file, in order to match the employee to each location record, I have to split the prox-id into another column. Then, I would use this column to match with the list of employees.

Split.jpg Split 2.jpg

Then I used Tableau to merge these two files (proxMobileOut.csv & employee.csv) by using join based on the employee ID that I have created previously.

Join.jpg

Building Data First, I cleaned the the data by removing those unwanted data. Then by using JMP, I have to transpose the column to row based on the timestamp.

Stack.png