CS 63018 & CS 73018 Probabilistic Data Management
Fall 2023
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: Probabilistic Data Management
CRN: 12333 & 12364
Prerequisites: Permission of the instructor
Time: 2:15pm - 3:30pm, MW
Classroom
Location: Room 107,
Merrill Hall
Course
Webpage: http://www.cs.kent.edu/~xlian/course_archive/2023Fall_CS63018_CS73018.html
Instructor's Office Hours: 9:30am - 12:00pm, MW; or any other convenient time for both
you and the instructor by email appointment (xlian@kent.edu)
Graduate
Assistant: Racheal Mukisa
Office: TBA
E-mail: rmukisa1@kent.edu
Phone: N/A
TA's Office
Hours: TBA
For grading
issues, please contact GA for clarifying the details of the grading. Whenever
you have any questions about the course materials or homework/survey/project, please
feel free to contact me by email (xlian@kent.edu)
to schedule a meeting. You are also encouraged to post commonly-encountered
questions/answers or resources on the discussion board of Canvas which may
benefit your peer classmates.
The official
registration deadline for this course is 08/27/2023. 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.
https://www.kent.edu/academic-calendar
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: 10/29/2023
Reference Books
This course
does not require any textbook, but there are several reference books below that
you can find online or borrow from the Kent State Library.
Charu C.
Aggarwal. Managing and Mining Uncertain Data. Springer Publishing Company,
2009. ISBN: 978-0-387-09689-6 (Print)
978-0-387-09690-2 (Online), https://link.springer.com/book/10.1007%2F978-0-387-09690-2
Lei Chen and
Xiang Lian. Query Processing over Uncertain Databases. In Synthesis Lectures on
Data Management, Vol. 4, No. 6, pages 1-101, Springer, 2012. ISBN:
9781608458929, https://link.springer.com/book/10.1007/978-3-031-01896-1
Dan Suciu,
Dan Olteanu, Christopher Re, and Christoph Koch. Probabilistic Databases. In
Synthesis Lectures on Data Management, Springer, 2011. ISBN-13: 978-1608456802,
ISBN-10: 1608456803, https://link.springer.com/book/10.1007/978-3-031-01879-4
Resources of Reading Materials
In this
course, you need to read some research papers, and most papers are available
through the digital library at Kent State University. You can access them either
through networks on campus or install a VPN (GlobalProtect)
at https://www.kent.edu/tusc/connecting-vpn for off-campus assesses.
Online
resources of research papers/surveys, including database conferences/journals
(SIGMOD, PVLDB, ICDE, TODS, VLDBJ, and TKDE), etc.
o
TODS:
http://dblp.uni-trier.de/db/journals/tods/index.html
o
VLDBJ:
http://dblp.uni-trier.de/db/journals/vldb/
o
TKDE:
http://dblp.uni-trier.de/db/journals/tkde/index.html
o
SIGMOD:
http://dblp.uni-trier.de/db/conf/sigmod/
o VLDB: http://www.vldb.org/pvldb/, or http://dblp.uni-trier.de/db/journals/pvldb/index.html
o ICDE: http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178, or http://dblp.uni-trier.de/db/conf/icde/
o
Indexing:
https://www.slac.stanford.edu/pubs/slacpubs/16250/slac-pub-16460.pdf
o
A survey of probabilistic data management:
http://ieeexplore.ieee.org/document/4597041/
o
A
Survey of Large-Scale Analytical Query Processing in MapReduce: http://link.springer.com/article/10.1007/s00778-013-0319-9
o
A
Survey on Parallel and Distributed Data Warehouses: https://pdfs.semanticscholar.org/4f3e/d0d4dfbd0bf4648a7feda94e3176e33ad088.pdf
o
Datasets
and Source Code
❖ Spatial data sets and index source
code: http://chorochronos.datastories.org/
❖ Road network and stream data: https://www.cs.utah.edu/~lifeifei/datasets.html
❖ U.S.
Government's open data: https://www.data.gov/
❖ DBpedia RDF data: http://www.dbpedia.org
❖ Freebase RDF data: https://developers.google.com/freebase/
❖ YAGO1, YAGO2s, YAGO3 RDF data: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/yago/archive/ (YAGO2 paper: https://people.mpi-inf.mpg.de/~kberberi/publications/2010-mpii-tra.pdf)
o
Apache
Hadoop: http://hadoop.apache.org/
o
Amazon
AWS: https://aws.amazon.com/
o
Tutorial:
https://www.lynda.com/ (Sign in with the organization
portal)
A reading list is here ☺
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:
Tentative Schedule
|
Week |
Topic |
Notes1 |
|
Week 1 (Aug. 21) |
Please form study groups, each with 4-5 members,
and send your names and emails to me (xlian@kent.edu); Due on Aug. 30 |
|
|
Week 1 (Aug. 23) |
|
|
|
Week 2 (Aug. 28) |
|
|
|
Week 2 (Aug. 30) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (1) |
Homework 1 (Due on Sept. 13) |
|
Week 3 (Sept. 4) |
-- |
Labor Day; No classes |
|
Week 3 (Sept. 6) |
|
|
|
Week 4 (Sept. 11) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (2) |
|
|
Week 4 (Sept. 13) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (3) |
Homework 2
(Due on Sept. 27) |
|
Week 5 (Sept. 18) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (4) |
Reading Materials: Index (1)
(2) Deadline to submit a reading list for the survey
(Sept. 18, Monday) |
|
Week 5 (Sept. 20) |
|
|
|
Week 6 (Sept. 25) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (5) |
|
|
Week 6 (Sept. 27) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (6) |
Homework 3 (Due on Oct. 18) |
|
Week 7 (Oct. 2) |
Q/A Session |
|
|
Week 7 (Oct. 4) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (7) |
|
|
Week 8 (Oct. 9) |
Q/A Session |
|
|
Week 8 (Oct. 11) |
Project Report (template) |
|
|
Week 9 (Oct. 16) |
Project Q/A |
|
|
Week 9 (Oct. 18) |
Homework 4
(Due on Nov. 1) Deadline to submit the survey (Oct. 18,
Wednesday) |
|
|
Week 10 (Oct. 23) |
Project Q/A |
|
|
Week 10 (Oct. 25) |
Last Day to Withdraw: 10/29/2023 |
|
|
Week 11 (Oct. 30) |
Project Q/A |
|
|
Week 11 (Nov. 1) |
Q/A Session |
Homework 5 (Due
on Nov. 15) |
|
Week 12 (Nov. 6) |
Project Q/A |
Submission of Sections 1-4 in Project
Report Template (Deadline: 11/6/2023) |
|
Week 12 (Nov. 8) |
Q/A Session |
|
|
Week 13 (Nov. 13) |
Project Q/A |
|
|
Week 13 (Nov. 15) |
Project Q/A |
|
|
Week 14 (Nov. 20) |
Presentations
& Demos for Projects Group #8 Group #1 Group #2 Group #3 Group #4 |
|
|
Week 14 (Nov. 22) |
-- |
Nov. 22 - 26, 2023, Thanksgiving Break; No
classes |
|
Week 15 (Nov. 27) |
Presentations
& Demos for Projects Group #6 Group #7 Group #9 Group #11 |
|
|
Week 15 (Nov. 29) |
Presentations
& Demos for Projects Group #5 Group #10 Group #12 Group #13 Group #19 |
|
|
Week 16 (Dec. 4) |
Presentations
& Demos for Projects Group #14 Group #15 Group #16 Group #17 Group #18 |
Course Evaluation |
|
Week 16 (Dec. 6) |
Presentations
& Demos for Projects Group #20 Group #21 Group #22 Preparation
for Project Reports |
Deadline for submitting the project report (Hard deadline: Dec.
8; only one member of each group
submits to the Canvas the project report, source code, data sets,
presentation slides, and demos in a single zip package) |
|
Week 17 (Dec. 11-17) |
No Final Exam |
|
Academic
calendar: https://www.kent.edu/academic-calendar
Final exam
schedule: https://www.kent.edu/registrar/fall-final-exam-schedule
NOTE: Presentation dates and
deadlines are tentative. Exact dates will be announced in class!!!
50% - 5 Homeworks (10 points each)
20% - Survey
o
A
survey on papers for the selected research topics in recent database
conferences/journals
30% -
Research Projects & Presentations
o
Research
project report (including introduction, related works, problem definition,
solutions, experiments, and conclusions) (20%)
o
Presentation
and demonstration for the proposed research project (10%)
5% - Bonus
Points, rated by other team members
10% - (Optional) Bonus for presenting research papers
A = 90 or higher
B = 80 - 89
C = 70 - 79
D = 60 - 69
F = <60
For homework assignments, please write down the intermediate
steps of your answers. Partial marks will be given for your intermediate steps,
even if the final answers are not correct.
Guidelines for Surveys/Papers/Projects
All surveys/papers/projects will be submitted electronically
only. Instructions are given separately.
➢ Assignments must be submitted to Canvas by the due date.
➢ 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).
➢ No assignment will be accepted for grading after two weeks late.
➢ 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. Feel
free to contact me (xlian@kent.edu)
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 3342-3-01.3
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 University welcomes individuals from all different
faiths, philosophies, religious traditions, and other systems of belief, and
supports their respective practices. In compliance with University
policy and the Ohio Revised Code, the University permits students to request
class absences for up to three (3) days, per semester, in order to participate
in organized activities conducted under the auspices of a religious
denomination, church, or other religious or spiritual organization. Students
will not be penalized as a result of any of these
excused absences.
The request for excusal must be made, in writing, during the
first fourteen (14) days of the semester and include the date(s) of each
proposed absence or request for alternative religious accommodation. The
request must clearly state that the proposed absence is to participate in
religious activities. The request must also provide the particular
accommodation(s) you desire.
You will be notified by me if your request is approved, or,
if it is approved with modification. I will work with you in
an effort to arrange a mutually agreeable alternative arrangement. For
more information regarding this Policy you may contact
the Student Ombuds (ombuds@kent.edu).
Kent
State recognizes many students face challenges and we are committed to
supporting your academic journey when you need help. Please check out
these resources to help as you build your support system:
· What is the first step I should take to get
academic support for this class?
v Reach out to your instructor!
· Where can I get help from another student who
earned a good grade in this class?
v Tutoring
· Where can I go if I need assistance with how
to study and meet my academic goals?
· Who can review my writing and help me properly
cite my work?
· Where should I go when I don’t know where to
go?
v TRIO Student Support Services
v There may be additional resources, just ask.
Kent
State University is committed to the creation and maintenance of equitable and
inclusive learning spaces. This course is a learning environment where all will
be treated with respect and dignity, and where all individuals will have an
equitable opportunity to succeed. The diversity that each student brings to
this course is viewed as a strength and a benefit. Dimensions of diversity and
their intersections include but are not limited to:
race, ethnicity, national origin, primary language, age, gender identity and
expression, sexual orientation, religious affiliation, mental and physical
abilities, socio-economic status, family/caregiver status, and veteran status.
The
instructor reserves the right to alter this syllabus as necessary.