Difference between revisions of "IS480 Team wiki: 2017T1 Team Atoms Final"
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Revision as of 01:56, 18 November 2017
Project Progress Summary
User: demo | Password: demopassword123
Project Management
Project Status
Project Schedule (Plan vs. Actual)
There were afew changes made to the project schedule and scope as the team decided to push themselves and added new refinements to the project. In addition, the sponsor has requested for an additional technical documentation due to their intentions to open source the project and submit a software research publication around the application. The changes made did not affect the timely completion of the project. We have also brought the project handover forward to allow more time for the sponsor the take over the project and provide sufficient time for support.
Planned Project Schedule
Actual Project Schedule
Project Metrics
Change Management
Below is the change log for Iteration 11 to 14 (after Mid-Terms):
Iteration | Date | Type | Change Request | Rationale | Feasibility | Outcome | Priority | Status of Request | Issued By |
---|---|---|---|---|---|---|---|---|---|
11 | 10/10/2017 | Scope | Add interactive GIF tutorial (For first time users) | Supervisor recommended including a interactive tutorial guide for first time users to allow them to easier learn how to use the system | Fits into schedule without any expected delay as a consequence | Accepted | Low | Closed | Supervisor |
12 | 16/10/2017 | Scope | Change denogram implementation for clustering (Distance cut off) | Sponsor requested for a better way to view denogram and allow users to utilise it more effectively through distance cut off | Fits into schedule without any expected delay as a consequence | Accepted | High | Closed | Sponsor |
12 | 16/10/2017 | Scope | Add interactive association visualisation | Team decided to make visualisaiton for association link graph interactive as it adds more value to the visualisation and project | Fits into schedule without any expected delay as a consequence | Accepted | Low | Closed | Team |
12 | 18/10/2017 | Scope | Deployment documentation instructions | Team decided to create an additional set of deployment documentation to facilitate project handover | Fits into schedule without any expected delay as a consequence | Accepted | Low | Closed | Team |
12 | 20/10/2017 | Scope | Technical documentation of system | Sponsor request for technical documentation as they will like to open source the project and submit for software publication | Fits into schedule without any expected delay as a consequence | Accepted | High | Closed | Sponsor |
13 | 30/10/2017 | Schedule | Change implementation for association rules | Lab4 release have to be delayed by 1 week due to changes in association computation/ algorithm | Fits into schedule without any consequence | Accepted | High | Closed | Sponsor |
Project Risks
Existing & Potential Risk
Currently there are no outstanding risk. All identified risk and challenges have been addressed. However, from the period of Mid-Terms till before Finals , we have faced and resolved concerns arising from 1) Client Management Risk as described below:
Risks & Challenges Faced
S/N | Risk Type | Risk Event | Likelihood | Impact | Category | Mitigation Strategy |
---|---|---|---|---|---|---|
1 | Client Management Risk | Team Atoms will face modifications to be made within a short period of time before every lab release. As the team will have to provide a highly customized and similar lab session experience with the existing lab exercises used in class. In addition, the team will also have to provide a new highly customized system user guide with every lab release. | High | High | A | Team will actively engage sponsor as early as possible before each lab release to provide sufficient time for changes required. In addition, the team will also dedicate time to specifically prepare for each lab release. |
Technical Complexity
System Architecture
Recap
A summary from our previous complexities achieved : https://wiki.smu.edu.sg/is480/IS480_Team_wiki%3A_2017T1_Team_Atoms_MidTerm#Technical_Complexity
S/N | Technical Complexity | Risk Event |
---|---|---|
1 | Canvas Graph Traversing Algorithm | Team Atoms designed a graph traversing algorithm to handle all the possible combinations the user could draw in the canvas |
2 | Concurrency issue with Django + Matplotlib | Team Atoms handled concurrency issues causing visualization charts to overlap each other (which becomes unreadable) |
3 | Ensemble Algorithm | Team Atoms wrote our own Ensemble algorithm where we implemented our own Voting Classifier. |
The following are new technical challenges faced by the team from Mid-Terms till Finals:
Frontend
1. Interactive Association Graph
In order to provide a better visualization when rule based mining is applied, we used d3.js to visualize the association rules on an interactive graph instead of a static image visualization. This will enable the users to move the nodes around to get a clearer view of the different rules generated. In addition, we were also able to achieve a high level of customization for the visualization by configuring the line color, node color and even node size based on its confidence and lift levels and items length.
As the team was new to the D3 visualization library, there was a steep learning curve for us. The library was challenging to understand and grasp effectively in a short period of time. In addition, changes also have to be made on the backend where we had to modify the structure of the dataset in order to make it compatible with D3.
Backend
2. Concurrency issue with Django & Matplotlib
- Django’s default architecture handles multiple requests using a built-in load balancer to cater to concurrent users and actions.
- Matplotlib is a library in Python used for plotting charts.
- Standard requests such as read/write operations work out of the box without issue
Problem
- An issue will arise from a common use case shown above, when an Ensemble node runs and triggers multiple Decision Trees
- Each Decision Tree when executed, plots images (Confusion Matrix) - as a result, images appear to be drawn on a same canvas and 3 charts overlap each other (which becomes unreadable)
Solution
- There is limited resources and documentation on this specific topic, therefore we had to find a solution ourselves
- One workaround is to assign a random id to each plot from (0,10000) and have every chart function create a plot on a different Figure object in the backend
- We also found out that each figure has to be closed after saving to prevent further complications (memory leaks)
- As a result, we also had to implement this for every other visualization to prevent the same issue when multiple users run a plot at the same time.
3. Ensemble Algorithm
In Machine Learning (Classification), A hard Voting Classifier Ensemble technique “combines conceptually different machine learning classifiers and use a majority vote” (Sklearn). For most algorithms, we make use of sklearn libraries to perform tasks. However, there is a problem in this particular use case shown below:
Problem
- This scenario means that each Classifier (Decision Tree) is trained before the Ensemble combines the results
- However, sklearn’s VotingClassifier requires each Classifier to be created and trained together as a whole - Once the Ensemble is created to accept different Classifiers it loses its trained state!
- This means that we cannot use this library and would have to implement our own Voting Classifier.
Solution
- Once we understood how and Ensemble Voting works, we had to call each Classifier’s “predict” function and select the most occurring value for each row
- This would mean overriding Ensemble’s predict function for our use case as shown in the code below:
Quality of Product
1. Deployment Script
Manual deployment can lead to multiple human error. Hence, we have created a deployment shell script that partially automates the process of the deployment of our web application. The steps that are automated includes:
1. Stopping/Starting of system services running our web server and our web application
2. Downloading of new source code from git repository
3. Changing file system permission of directories and files
4. Execution of Django specific deployment command
With a frequent deployment rate (every iteration - 2 weeks) the chances of error due to manual deployment is much higher. Hence with the deployment script we will be able to reduce such errors.
2. Bench marking for Visualization
This is a scatter matrix plot for the famous Iris data set. It involves plotting every column against one another. Therefore, the computation is (# of rows) x (# of columns)2
Complexity:
Problem
- This would cause datasets which a large number of columns to take a considerable amount of time, consuming resources for a single user
- If many users execute this chart at the same time, it would result in a very long response time
Solution
- To accurately measure how much time it would take for different dimensions of data sets, we generated datasets of different columns and rows and ran each charting function to see how much time it took.
Findings
- From the benchmark tests, we found out that the number of rows did not affect the performance as much as the number of columns
- From our findings, we implemented validations in place to disallow users to select too many columns (>10) for scatter matrix
3. Secure API - System security
- All backend APIs require user login to prevent unauthorized direct API calls
- For each API request to modify files, there is an implementation to verify if the file belongs to the user before the operation.
- If there is an unauthorized API request to the system, an appropriate error message will be displayed and the request will also be logged for investigation
4. Google Analytics tracking implementation
Google analytics tool can help our sponsor understand how students are interacting with our KDD Labs website, where they’re coming from and how often they visit, what parts of the site are capturing their attention and what parts aren’t sparking interest.
We also keep tracking user's’ behaviour and count number of events triggered in our system. This will allow us to keep a close monitor on functionalities being utilized in our system and assist us with tracking abnormal behavior. Furthermore, this will also be a useful tracking tool for the teaching team to understand the students usage behaviour. There are a total of 32 different activities that have been tracked since the website was announced to the students on the 7th Sep 17. The graph below shows the top 10 activities in our website.
5. System logger
Our system will consistently monitor and log down critical user actions and the problems the user encounters. This will help us to automatically track errors made in the system which will be used for our internal feedback when users utilize the KDD Labs system. We will then analyse these errors further and derive the root cause of such errors to try improve on our system if possible.
Furthermore, to make the logging files easy to locate, we have created a logger that will rotate the logging file twice a day. The file name will be changed to the date and time when the file was last modified.
Intermediate Deliverables
Topic of Interest | Link |
---|---|
Project Management | Project Schedule |
Minutes | |
Metrics | |
Risk Management | |
Change Management | |
Project Overview | Project Overview |
Team's Motivation | |
Project Scope | |
Project Documentation | Diagrams |
Technologies Implemented | |
Low & Mid Fidelity Prototypes | |
Testing | Testing Documentation |
Deployment
To view application, visit
Test server: http://kddlabs.com
Username: demo
Password: demopassword123
Note: This public server is currently being utilized for Live Usage by the IS424 Data Mining and Business Analytics students for their labs and project completion.
Production server: https://kddlabs.cn/
Testing
Internal Testing
We engage in comprehensive manual testing in every iteration. The developers will conduct individual testing before committing their codes on our shared repository, GitHub. We believe in testing the application manually at this level because tests can be specially adjusted to cater to changes in the application, both on the front and back end. Furthermore, manual testing brings about the human factor, allowing us to better discover problems that might surface during real usage due to natural human behavior.
Once the developers have fixed the bugs, the fixed set of codes will be merged and integrated with the other functionalities. Subsequently, the integrated code is then deployed on the test server and the lead quality assurance will run a final check against the set of test cases created. This helps to ensure that the deployed application works with no major incidents.
The team's lead quality assurance then performs regression testing on the test server where previous functionalities developed are tested again. This helps to ensure that existing functionalities in the application are not affected by the integration. Once bugs have been identified, the lead quality assurance will then update the bug-tracking Excel sheet and notify the relevant developers of the issues and the corresponding priority level.
The team’s list of test cases can be found on our private repository here.
User Acceptance Test 1 & 2
Team Atoms has conducted 2 user tests which allowed us to better manage sponsor expectations as well as improve on usability of our application interface.
For more detailed version of Team Atoms user acceptance test results, access it here:
Live Usage
Through our live usage and roll out, IS424 students were able to complete their take home lab assignments on our system. Thus we were able to gather feedback from our end users about KDD Labs system directly. In addition, we were also able to compare the user experience with the existing alternative used in class (SAS EM). From our feedback, we have received positive response about the KDD Labs system stating that the students were able to complete their in class lab exercise. Not only that, they also found that the KDD Labs system was easier to use as compared to SAS EM.
Lab1
Release Date: 01 Sep 2017, Friday
Duration: 2-3 hours per user
Number of Users(s): 45
Lab1 User Guide: Created instructions can be found here
Lab1 Feedback Results: Live user feedback can be found here
Lab2
Release Date: 15 Sep 2017, Friday
Duration: 2-3 hours per user
Number of Users(s): 45
Lab2 User Guide: Created instructions can be found here
Lab2 Feedback Results: Live user feedback can be found here
Lab3
Release Date: 16 Oct 2017, Monday
Duration: 2-3 hours per user
Number of Users(s): 45
Lab3 User Guide: Created instructions can be found here
Lab3 Feedback Results: Live user feedback can be found here
Lab4
Release Date: 6 Nov 2017, Monday
Duration: 2-3 hours per user
Number of Users(s): 45
Reflection
Team Reflection
This journey has proven to be an enriching learning experience for Team Atoms. The project had many new learning points for the team as it was highly technical- we had to understand and grasp the concepts of the data mining process and algorithms within a short period of time. In addition, we also learnt the importance of good stakeholder management which allows us to better react to unforeseen circumstances. Through an active team participation and communication we were able to mitigate existing issues and deliver a quality project on time.
Sponsors' Testimonial
"Team ATOMS is a capable and sincere team, that has done very well in the course of the project. KDD Labs project is very challenging and in particular requires a very diverse set of technical skills. ATOMS have made substantial efforts in acquiring new skills and integrating them to deliver a quality product in a timely manner. They have stoically faced the technical issues and challenging feature/change requests, and demonstrated an excellent work ethic in delivering on their targets. Discussions with them have been thought provoking and rewarding, and have significantly contributed towards improving the product quality" - Sponsor, Doyen Sahoo
Individual Reflections