1. CS 69995 & CS 79995 ST: Probabilistic Data Management
Xiang Lian
Department of Computer Science
Kent State University
Homepage:

2. Probabilistic Data Management
An Overview of Probabilistic Data Management
Data Uncertainty Model
Probabilistic Query Answering Over Probabilistic and Uncertain Databases
Probabilistic Graph Databases
Data Quality in Probabilistic Databases

3. Background Needed Probability & statistics (math)
Database techniques (e.g., index)
Programming (e.g., C++, Java, or Python etc.)
You need to be able to look up how to get things done (for example, read papers/surveys from online resources, using digital library, Google, Wikipedia, etc.)

4. Skills
This course is a seminar course, in which you need to learn how to do research
Lecture
Literature review (survey)
Project report
Presentations & demonstrations
Research collaborations

5. Study Group Please form a team with 2-3 members
The workload should be distributed evenly to each team member
Each team needs to finish 1 survey + 1 project report + 1 presentation + 1 bonus presentation (optional):
A survey on a selected research topic
A project report (including introduction, problem definition, related work, the proposed approaches, experimental evaluation, and conclusions);
A presentation & demonstration on your research paper
An optional presentation on 1-2 existing research papers in your selected research directions (20-25 minutes)

6. Survey & Research Project
I will post a reading list of papers
It does not include all related works, but only a few typical papers in different research directions
You need to search digital libraries (e.g., ACM portal, IEEE Xplore, etc.) and Google the Web to find more related works in each direction
You need to decide which topics/problems you want to study
Please make an appointment with me to discuss research directions of your teams (within the first 3 weeks of the semester; on or before Sept. 14)

7. Scoring and Grading 5% - Attendance & Questions
50% - 5 Homeworks (10 points each)
15% - 1 Survey on papers for the selected research topics in recent database conferences/journals
20% - Research Project Report
Code and report for the research project in paper format
10% - Presentations & Demonstration
Presentation and demonstration for the proposed research project
5% - Bonus Points, rated by other team members
10% - (Optional) Presentation for 1-2 related works in the selected research direction

8. Scoring and Grading (cont'd)
B =
C =
D =
F = <60
The maximum score you can get is: 115!

9. Use of the Textbook No textbooks!! Reference books
Charu C. Aggarwal. Managing and Mining Uncertain Data. Springer Publishing Company, ISBN: (Print) (Online),
Lei Chen and Xiang Lian. Query Processing over Uncertain Databases. In Synthesis Lectures on Data Management, Vol. 4, No. 6, pages 1-101, Morgan & Claypool Publishers, ISBN: , M033
Dan Suciu, Dan Olteanu, Christopher Re, and Christoph Koch. Probabilistic Databases. In Synthesis Lectures on Data Management, Morgan & Claypool Publishers, ISBN-13: , ISBN-10: , M016

10. Online Resources The only resources are papers!! ACM digital library
IEEE Xplore Digital Library
DBLP
Database Conferences
SIGMOD, PVLDB, ICDE, EDBT, CIKM
Database Journals
TODS, VLDBJ, TKDE

11. The Schedule for the Class
I expect to give lectures and introduce the concepts and techniques of probabilistic data management for the first 2 months (September & October)
Then, each team will submit a survey on related works in the literature (October)
Finally, each team will start to identify research problems and find solutions. You need to write a project report in the paper format, do experiments (comparing with the existing approaches), and present/demonstrate your paper in class (November & December).

12. Advices & Suggestions Editor Tools: Survey Project Report
Latex vs. MS Word
Survey
Check "Related Work" sections in most recent papers, and you can obtain more related papers
Read abstract/introductions of papers, and classify papers into different categories (this will help you later to identify problems that have not been solved before)
Project Report
Even if you are not familiar with some topics, try to read as many related works as possible to understand the general problems and solutions in these topics (you can skip some part, if it is too hard to understand)
Stick to the problem you want to solve, and use any resource you can find to solve the problem (note: DO NOT simply apply previous techniques to your problem, since it is not counted as your contributions!!)

13. Advices & Suggestions (cont'd)
Project Report
Introduction
Related works
Problem definition
Solutions
Experiments
Conclusions
In the project, please add a module to visualize your experimental results

14. Advices & Suggestions (cont'd)
Do not copy from any sources (even for the survey)
Any form of academic dishonesty will be strictly forbidden and will be punished to the maximum extent
Allowing another student to copy one's work will be treated as an act of academic dishonesty, leading to the same penalty as copying

15. Advices & Suggestions (cont'd)
If the resulting surveys and papers are of high quality and novel, I highly recommend you to submit them to database conferences or journals
After this class, self-motivated, hardworking, and creative students with good performance on surveys/papers may have the chance to join my lab (Big Data Science Research Lab)!

16. Examples of Probabilistic Data (1)
Witnessed Person
location
t.p
PID1 A
0.9
PID2 B
0.2
PID3
0.1
Person ID
Zip code
Disease
PID1
44224
(pneumonia,0.3), (flu, 0.7)
PID2
44242
(AIDS, 0.9)

17. Examples of Probabilistic Data (2)
GPS samples
Location data are imprecise

18. Examples of Probabilistic Data (3)
Inaccuracy of the data integration
Unreliability of the data sources
Data inconsistency
… …

19. Queries k nearest neighbor query Range query Top-k query Skyline query…

20. Probabilistic Query Processing on Uncertain Data
How to efficiently answer probabilistic queries over large-scale uncertain data?
How to retrieve accurate query answers with confidence guarantees?