CS 63018 & CS 73018 Probabilistic Data Management
Fall 2022
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: 12334 & 12364
Prerequisites: Permission of the instructor
Time:
2:15pm - 3:30pm, MW
Classroom
Location: Smith Hall
(SMH), Room 111
Course
Webpage: http://www.cs.kent.edu/~xlian/course_archive/2022Fall_CS63018_CS73018.html
Instructor's
Virtual Office Hours: By email
appointment only (preferably 10:00am - 12:00pm, MW; xlian@kent.edu)
Graduate
Assistant: Pavan Kumar Reddy Gunnala
Office:
N/A
E-mail:
pgunnala@kent.edu
Phone:
N/A
TA's Office Hours: N/A
The official
registration deadline for this course is 08/31/2022. 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: 11/02/2022
Reference Books
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, Morgan & Claypool Publishers,
2012. ISBN: 9781608458929, http://www.morganclaypool.com/doi/abs/10.2200/S00465ED1V01Y201212DTM033
Dan Suciu,
Dan Olteanu, Christopher Re, and Christoph Koch.
Probabilistic Databases. In Synthesis Lectures on Data Management, Morgan &
Claypool Publishers, 2011. ISBN-13: 978-1608456802, ISBN-10: 1608456803, http://www.morganclaypool.com/doi/abs/10.2200/S00362ED1V01Y201105DTM016
Resources of Reading Materials
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 2 (Aug. 29) |
Please form study
groups, each with 3-4 members, and send your names and emails to me (xlian@kent.edu); Due on
Sept. 7 |
|
Week 2 (Aug. 31) |
|
|
Week 3 (Sept. 5) |
-- |
Labor Day; No classes |
Week 3 (Sept. 7) |
Homework 1 (Due on Sept. 21) |
|
Week 4 (Sept. 12) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (1) |
|
Week 4 (Sept. 14) |
|
|
Week 5 (Sept. 19) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (2) |
|
Week 5 (Sept. 21) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (3) |
Homework 2
(Due on Oct. 5) |
Week 6 (Sept. 26) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (4) |
|
Week 6 (Sept. 28) |
|
|
Week 7 (Oct. 3) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (5) |
|
Week 7 (Oct. 5) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (6) |
Homework 3 (Due on Oct. 26) |
Week 8 (Oct. 10) |
Q/A Session |
|
Week 8 (Oct. 12) |
Probabilistic Query Answering Over Probabilistic/Uncertain
Databases (7) |
|
Week 9 (Oct. 17) |
Q/A Session |
|
Week 9 (Oct. 19) |
Project Report (template) Deadline to submit the survey
(Oct. 19, Wednesday) |
|
Week 10 (Oct. 24) |
Project Q/A |
|
Week 10 (Oct. 26) |
Homework 4
(Due on Nov. 9) |
|
Week 11 (Oct. 31) |
Project Q/A |
|
Week 11 (Nov. 2) |
Last Day to Withdraw: 11/2/2022 |
|
Week 12 (Nov. 7) |
Project Q/A |
|
Week 12 (Nov. 9) |
Q/A Session |
Homework 5 (Due
on Nov. 28) |
Week 13 (Nov. 14) |
Project Q/A |
Submission of
Sections 1-4 in Project Report Template (Deadline: 11/14/2022) |
Week 13 (Nov. 16) |
Q/A Session |
|
Week 14 (Nov. 21) |
Project Q/A |
|
Week 14 (Nov. 23) |
-- |
Nov. 23 - 27, 2022,
Thanksgiving Break; No classes |
Week 15 (Nov. 28) |
Project Q/A |
|
Week 15 (Nov. 30) |
Presentations
& Demos for Projects |
|
Week 16 (Dec. 5) |
Presentations
& Demos for Projects |
Course Evaluation |
Week 16 (Dec. 7) |
Presentations
& Demos for Projects Preparation
for Project Reports |
Deadline for submitting the
project report (Hard
deadline: Dec. 9; 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. 12-18) |
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
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.
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.