CS 79995-006-12569
& CS 69995-006-12554
ST: Probabilistic
Data Management
Fall 2016
Instructor: Xiang Lian
Office
Location: Mathematics and Computer Science Building, Room 264
Office
Phone Number: (330) 672-9063
Web: http://www.cs.kent.edu/~xlian/index.html
Email: xlian@kent.edu
Course:
ST:
Probabilistic Data Management
Prerequisites: Permission of the instructor
Time: 11:00am - 12:15pm, MW
Classroom Location: White Hall (WTH)
00107
Course Webpage: http://www.cs.kent.edu/~xlian/2016Spring_CS79995/ST_Probabilistic_Data_Management.html
Instructor's
Office Hours: TBA;
or by appointment
Graduate Assistant: N/A
Office: N/A
E-mail: N/A
Phone: N/A
TA's Office Hours: N/A
The official registration deadline for this course is 09/04/2016. University policy
requires all students to be officially registered in each class they are
attending. Students who are not officially registered for a course by published
deadlines should not be attending classes and will not receive credit or a
grade for the course. Each student must confirm enrollment by checking his/her
class schedule (using Student Tools in FlashLine) prior to the deadline indicated. Registration
errors must be corrected prior to the deadline.
For
registration deadlines, enter the requested information for a Detailed Class
Search from the Schedule of Classes Search found at:
https://keys.kent.edu:44220/ePROD/bwlkffcs.P_AdvUnsecureCrseSearch?term_in=201680
After
locating your course/section, click on the Registration Deadlines link on the
far right side of the listing.
Last day to withdraw: 11/06/2016
Resources of Reading Materials
Research papers/surveys
from database conferences/journals (SIGMOD, PVLDB, ICDE, TODS, VLDBJ, and
TKDE).
o
A reading list
will appear here later J
§ Indexing on Probabilistic Data
§ Probabilistic Query Types
§ Probabilistic Graphs
§ Big Uncertain Data (parallel processing)
§ Parallel Probabilistic Graph Processing
§ Probabilistic Data Management in Real Applications
(e.g., sensor networks, road networks, social networks, bioinformatics,
Semantic Web, etc.)
Catalog Description
The
purpose of this course is to learn the fundamental concepts and techniques for
probabilistic data management in the area of databases. Probabilistic data are
pervasive in many real-world applications, such as sensor networks, GPS system,
location-based services, mobile computing, multimedia databases, data
extraction/integration, trajectory data analysis, Semantic Web, privacy
preserving, and so on. It is rather challenging how to efficiently and
effectively manage these large-scale probabilistic data. In this class, we will
cover major research topics such as probabilistic/uncertain data model,
probabilistic queries, probabilistic query answering techniques, data quality
issues in databases, and so on. Students are expected to do a survey on a
selected research direction for papers from recent database
journals/conferences, and write research papers or reports with new problems or
solutions. Students will also give presentations to the class to demonstrate
their outcomes. It is also expected that the resulting surveys/papers can be
extended to database conference/journal papers.
Learning Outcomes
At
the end of this course, the students should be able to:
1.
Explain
real applications of probabilistic and uncertain data management in databases.
2.
Know
the classifications of data uncertainties according to different criteria.
3.
Explain
the causes and importance of studying probabilistic data management.
4.
Describe
data uncertainty models, possible worlds
semantics, correlations in probabilistic data, and probabilistic graph models.
5.
Know
various types of probabilistic queries in probabilistic/uncertain databases.
6.
Describe
the models, problem definitions, and the proposed techniques for each
probabilistic query type in the literature.
7.
Learn
to read/write research papers, and understand the general trend of the research
in probabilistic data management.
8.
Summarize
and analyze the pros and cons of existing works in probabilistic/uncertain
databases.
9.
Identify
one or two future directions in probabilistic databases, which have not been
studied before, or not been extensively studied before, to work on.
10. Write a survey on
related works of probabilistic data management.
11. Propose new
solutions to existing problems or novel solutions to new problems in
probabilistic and uncertain data management.
12. Write a research
report or research project/paper on the proposed problems or solutions.
13. Do experiments on
the proposed ideas in probabilistic data management.
14. Give two
presentations on the survey and report, respectively, to show off the outcome
of the research project/paper.
15. Work in a team
(each with at most 3 members) to collaboratively write the survey and research
papers.
Tentative Schedule
|
Week |
Topic |
Notes1 |
|
Week 1 (Aug. 29) |
|
Please form study groups, each with at
most 3 members, and send your IDs, names, and emails to me (xlian@kent.edu); Due on Sept. 14 |
|
Week 1 (Aug. 31) |
Data Uncertainty Model |
|
|
Week 2 (Sept. 5) |
-- |
Labor Day; No classes |
|
Week 2 (Sept. 7) |
Probabilistic Query Answering Over
Probabilistic/Uncertain Databases |
|
|
Week 3 (Sept. 12) |
|
|
|
Week 3 (Sept. 14) |
Probabilistic Graph Databases |
|
|
Week 4 (Sept. 19) |
|
|
|
Week 4 (Sept. 21) |
Data Quality in Probabilistic Databases |
|
|
Week 5 (Sept. 26) |
|
Presentations for
Selected Research Papers |
|
Week 5 (Sept. 28) |
|
|
|
Week 6 (Oct. 3) |
|
|
|
Week 6 (Oct. 5) |
|
|
|
Week 7 (Oct. 10) |
|
|
|
Week 7 (Oct. 12) |
|
Deadline for submitting the survey (Oct.
12) |
|
Week 8 (Oct. 17) |
|
|
|
Week 8 (Oct. 19) |
|
|
|
Week 9 (Oct. 24) |
|
|
|
Week 9 (Oct. 26) |
|
|
|
Week 10 (Oct. 31) |
|
|
|
Week 10 (Nov. 2) |
|
Presentations for Project Report Last Day to Withdraw: 11/06/2016 |
|
Week 11 (Nov. 7) |
|
|
|
Week 11 (Nov. 9) |
|
|
|
Week 12 (Nov. 14) |
|
|
|
Week 12 (Nov. 16) |
|
|
|
Week 13 (Nov. 21) |
|
|
|
Week 13 (Nov. 23) |
-- |
Nov. 23-27, Thanksgiving Recess; No
classes |
|
Week 14 (Nov. 28) |
|
|
|
Week 14 (Nov. 30) |
|
|
|
Week 15 (Dec. 5) |
|
|
|
Week 15 (Dec. 7) |
|
|
|
Week 16 (Dec. 12-18) |
Final Exam (& Presentation) |
Deadline for submitting the research paper (Dec.
12) |
Academic calendar: https://www.kent.edu/sites/default/files/academic-calendar-2014-2018_0.pdf
Final exam schedule: http://www.kent.edu/registrar/fall-final-exam-schedule
NOTE: Presentation dates and deadlines are
tentative. Exact dates will be announced in class!!!
5% - Attendance & Questions
60% - Research Projects/Papers
o
Survey on papers
for the selected research topics in recent database conferences/journals (25%)
o
Code and report
for the research project in paper format (including introduction, related
works, problem definition, solutions, experiments, and conclusions; 35%)
40% - Presentations
o
Presentation for
1-2 related works in the selected research direction (20%)
o
Presentation and
demonstration for the proposed research project (20%)
5% - Bonus Points, rated by other team members
A
= 90 or higher
B
= 80 - 89
C
= 70 - 79
D
= 60 - 69
F
= <60
Guidelines for Surveys/Papers/Projects
All surveys/papers/projects will be
submitted electronically only. Instructions are given separately.
Ø Assignments must be submitted to Blackboard by the due date.
Ø A survey or paper report turned in within two weeks after the due date will be considered late and will lose 30% of its grade (10% for the first week, and 20% more for the second week).
Ø No assignment will be accepted for grading after two weeks late.
Ø The late submission needs prior consent of the instructor.
Attendance in the lecture is mandatory. Students are expected to attend lectures, study the text, and contribute to discussions. You need to write your name on attendance sheets throughout the course, so please attend every lecture.
Students are expected to attend all scheduled classes and may be dropped from the course for excessive absences. Legitimate reasons for an "excused" absence include, but are not limited to, illness and injury, disability-related concerns, military service, death in the immediate family, religious observance, academic field trips, and participation in an approved concert or athletic event, and direct participation in university disciplinary hearings.
Even though any absence can potentially interfere with the planned development of a course, and the student bears the responsibility for fulfilling all course requirements in a timely and responsible manner, instructors will, without prejudice, provide students returning to class after a legitimate absence with appropriate assistance and counsel about completing missed assignments and class material. Neither academic departments nor individual faculty members are required to waive essential or fundamental academic requirements of a course to accommodate student absences. However, each circumstance will be reviewed on a case-by-case basis.
For more details, please refer to University policy 3-01.2: http://www.kent.edu/policyreg/administrative-policy-regarding-class-attendance-and-class-absence.
No make-up presentation will be given except for university sanctioned excused absences. If you miss a presentation (for a good reason), it is your responsibility to contact me before the presentation, or soon after the presentation as possible.
The University expects a student to maintain a high standard of individual honor in his/her scholastic work. Unless otherwise required, each student is expected to complete his or her assignment individually and independently (even in the team, workload should be distributed to team members to accomplish individually). Although it is encouraged to study together, the work handed in for grading by each student is expected to be his or her own. Any form of academic dishonesty will be strictly forbidden and will be punished to the maximum extent. Copying an assignment from another student (team) in this class or obtaining a solution from some other source will lead to an automatic failure for this course and to a disciplinary action. Allowing another student to copy one's work will be treated as an act of academic dishonesty, leading to the same penalty as copying.
University policy 3-01.8 deals with the problem of academic dishonesty, cheating, and plagiarism. None of these will be tolerated in this class. The sanctions provided in this policy will be used to deal with any violations. If you have any questions, please read the policy at http://www.kent.edu/policyreg/administrative-policy-regarding-student-cheating-and-plagiarism and/or ask.
University policy 3-01.3 requires that students with disabilities be provided reasonable accommodations to ensure their equal access to course content. If you have a documented disability and require accommodations, please contact the instructor at the beginning of the semester to make arrangements for necessary classroom adjustments. Please note, you must first verify your eligibility for these through Student Accessibility Services (contact 330-672-3391 or visit www.kent.edu/sas for more information on registration procedures).
This
course may be used to satisfy the University Diversity requirement. Diversity
courses provide opportunities for students to learn about such matters as the
history, culture, values and notable achievements of people other than those of
their own national origin, ethnicity, religion, sexual orientation, age,
gender, physical and mental ability, and social class. Diversity courses also
provide opportunities to examine problems and issues that may arise from
differences, and opportunities to learn how to deal constructively with them.
This
course may be used to satisfy the Writing Intensive Course (WIC) requirement. The
purpose of a writing-intensive course is to assist students in becoming
effective writers within their major discipline. A WIC requires a substantial
amount of writing, provides opportunities for guided revision, and focuses on writing
forms and standards used in the professional life of the discipline.
This
course may be used to fulfill the university's Experiential Learning
Requirement (ELR) which provides students with the opportunity to initiate
lifelong learning through the development and application of academic knowledge
and skills in new or different settings. Experiential learning can occur
through civic engagement, creative and artistic activities, practical
experiences, research, and study abroad/away.
The instructor reserves the right to alter this syllabus as necessary.