CS 63018 & CS 73018 Probabilistic Data Management
Fall 2019
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: 12677 & 12709
Prerequisites: Permission of the instructor
Time:
2:15pm - 3:30pm, MW
Classroom
Location: Oscar Ritchie Hall (ORH), 340
Course
Webpage: http://www.cs.kent.edu/~xlian/course_archive/2019Fall_CS63018_CS73018.html
Instructor's
Office Hours: 10:00am - 12:30pm, TR; 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 08/28/2019. 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.
http://www.kent.edu/registrar/calendars-deadlines
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: 10/30/2019
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
❖ 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. 26) |
Please form study
groups, each with 3-4 members, and send your IDs, names, and emails to me (xlian@kent.edu); Due on
Sept. 4 |
|
Week 2 (Aug. 28) |
|
|
Week 3 (Sept. 2) |
-- |
Labor Day; No classes |
Week 3 (Sept. 4) |
Homework
1 (Due on Sept. 18) |
|
Week 4 (Sept. 9) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (1) |
|
Week 4 (Sept. 11) |
|
|
Week 5 (Sept. 16) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (2) |
|
Week 5 (Sept. 18) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (3) |
Homework
2 (Due on
Oct. 2) |
Week 6 (Sept. 23) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (4) |
|
Week 6 (Sept. 25) |
|
|
Week 7 (Sept. 30) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (5) |
|
Week 7 (Oct. 2) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (6) |
Homework
3 (Due on Oct. 23) |
Week 8 (Oct. 7) |
|
|
Week 8 (Oct. 9) |
Probabilistic
Query Answering Over Probabilistic/Uncertain Databases (7) |
|
Week 9 (Oct. 14) |
|
|
Week 9 (Oct. 16) |
Project Q/A |
Project Report (template) Deadline to submit the survey
(Oct. 16) |
Week 10 (Oct. 21) |
|
|
Week 10 (Oct. 23) |
|
Homework
4 (Due on Nov.
6) |
Week 11 (Oct. 28) |
|
|
Week 11 (Oct. 30) |
|
Last Day to Withdraw: 10/30/2019 |
Week 12 (Nov. 4) |
|
|
Week 12 (Nov. 6) |
Project Discussion |
Homework
5
(Due on Nov. 20) |
Week 13 (Nov. 11) |
Project Q/A |
|
Week 13 (Nov. 13) |
Project Q/A |
|
Week 14 (Nov. 18) |
Project Q/A |
|
Week 14 (Nov. 20) |
Project Q/A |
|
Week 15 (Nov. 25) |
Presentations
& Demos for Projects Group
#1: Using K-Core on Uncertain Graphs to
Predict Communities on Social Network Group #3: |
|
Week 15 (Nov. 27) |
-- |
Nov. 27 - Dec. 1, 2019,
Thanksgiving Break; No classes |
Week 16 (Dec. 2) |
Presentations
& Demos for Projects Group
#2: Group
#4: Group
#5: |
Course Evaluation |
Week 16 (Dec. 4) |
Presentations
& Demos for Projects Group
#6: Group
#7: Group
#8: Preparation
for Project Reports |
Deadline for submitting the
project report (Hard
deadline: Dec. 5; only one
member of each group submits to the Blackboard the project report,
source code, data sets, presentation slides, and demos in a single zip
package) |
Week 17 (Dec. 9-15) |
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!!!
5% - Attendance & Questions
50% - 5 Homeworks (10 points each)
15% - 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 Blackboard 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.