1. Repository and Mining of Temporal Data
Philip Chan

2. Motivation/Opportunity
Plenty of data, but:
From different sources
In different formats
Not easy to find
Repositories collect different datasets, but:
integrating data from different datasets is not easy
integrating data from different repositories is not easy

3. Sample Scenario 1 Reports in pdf/xls/… on: Variable Granularity Source
Enrollment
semester
Enrollment Management
Retention rates
year
Mid-term failure rates
Academic Support
Degree program outcomes
Department
SAT/ACT scores
Admission
Assignment submission
week/month/…
Instructor
Graduation rates …

4. Sample Scenario 1 (continued)
Consider mid-term failure rates:
Is there significant change this semester?
What are the variables that are strongly correlated?
Assignment submission rates, pre-test scores, class attendance, grades of pre-requisite courses, SAT/ACT? (potential causes)
degree program outcomes, retention, enrollment, graduation (potential effects)

5. Sample Scenario 2 Global warming:
Population, cars, deforestation, industrial emissions, CO2 , … (possible causes)
Extreme storms, sea level rise, …(possible effects)

6. Sample Scenario 3 Approval rating of President Trump
Tweets, executive orders, media coverage, unemployment rate, GDP, stock prices, … (possible causes)
Approval ratings of cabinet members, … (possible effects)

7. Approach Web-based repository of temporal data (ie, with timestamps)
One large virtually integrated dataset
Users
Data providers
e.g. ASC, Admissions, Registrar, Departments, Instructors, …
usually generate/collect data in digital format
Data customers
could be the data providers and/or
data scientists/miners/analysts

8. Data Providers Upload/import data In different spreadsheet formats
initially csv
timestamps could be in the first column or row
initially numeric values
Meta data
Description of each variable (in a data set)
Provider/owner
Access: public (available to all)
Or private (provider/owner only)

9. Data Customers Specify a target variable, tasks include:
Detect significant changes
Identify top-k variables with the highest correlation
Build models to characterize/forecast the target variable
Visualization for the above tasks
Search for variables to be included
Download/export data in spreadsheet formats (for tasks not supported by the system)