CS 63016 & CS 73016
Big Data Analytics
Spring 2018
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:
Big Data
Analytics
CRNs: 12665
& 12701
Prerequisites: Permission of the instructor
Time: 7:00pm ~ 8:15pm, TR
Classroom Location: Henderson (HDN), Room
109
Course Webpage: http://www.cs.kent.edu/~xlian/course_archive/2018Spring_CS63016_CS73016.html
Instructor's
Office Hours: Tuesday
and Thursday (1:00pm ~ 3:30pm); or by appointment
Graduate Assistant: TBA
Office: TBA
E-mail: TBA
Phone: N/A
TA's Office Hours: N/A
The official registration deadline for this course is Jan. 21, 2018. 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: Mar. 25, 2018
Reference Books
There are no textbooks.
However, I can provide you with some reference books below for your reading.
Kuan-Ching Li, Hai Jiang, Laurence T. Yang, and
Alfredo Cuzzocrea. Big Data: Algorithms, Analytics, and Applications. Chapman
& Hall/CRC Big Data Series, ISBN 9781482240559, 2015.
Thomas Erl, Wajid Khattak, and Dr. Paul Buhler.
Big Data Fundamentals: Concepts, Drivers & Techniques. The Prentice Hall
Service Technology Series, ISBN-13: 978-0134291079, 2016.
Resources of Reading Materials
A
reading list J (Please let me know if any links are
expired)
o Indexing for Big Data
v (Grid file) P. Rigaux, M. Scholl, and A. Voisard. Spatial
Databases - with application to GIS. Morgan
Kaufmann, San Francisco, 2002. http://bsolano.com/ecci/claroline/backends/download.php/TGlicm9zX2RlX3RleHRvL1NwYXRpYWxEQnNXaXRoQXBwbGljYXRpb25Ub0dJUy5wZGY%3D?cidReset=true&cidReq=CI1314
v (Bitmap index) P. Nagarkar, K. S. Candan, and A. Bhat. Compressed
Spatial Hierarchical Bitmap (cSHB) Indexes for Efficiently Processing Spatial
Range Query Workloads. In PVLDB,
8(12), pages 1382-1393, 2015. http://dl.acm.org/citation.cfm?id=2824038
v (Quad-tree) H.
Samet and R. E. Webber. Storing a collection of polygons using quadtrees. In ACM
Trans. Graph, 1985. https://pdfs.semanticscholar.org/65ee/4429b5509173f12309539e809ac533e84690.pdf
v (K-D-B-tree) J. T. Robinson. The K-D-B-Tree: a search structure for
large multidimensional dynamic indexes. In SIGMOD, 1981. http://repository.cmu.edu/cgi/viewcontent.cgi?article=3451&context=compsci
v (K-D-tree) R. A. Brown. Building a Balanced k-d Tree in O(kn
log n) Time. In Journal of Computer Graphics Techniques (JCGT), vol. 4, no. 1,
50-68, 2015. http://jcgt.org/published/0004/01/03/paper.pdf
v (R-tree) A.
Guttman. R-trees: a dynamic index structure for spatial searching. In SIGMOD,
1984. http://www-db.deis.unibo.it/courses/SI-LS/papers/Gut84.pdf
v (R+-tree) T. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-Tree:
A dynamic index for multi-dimensional objects. In VLDB, 1987. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.3272
v (R*-tree) N. Beckmann, H.-P. Kriegel, R. Schneider, and B.
Seeger. The R*-tree: an efficient and robust access method for points and
rectangles. In SIGMOD, 1990. https://www.cs.umd.edu/class/fall2002/cmsc818s/Readings/rstar-tree.pdf
v (X-tree) S. Berchtold, D. A. Keim, and H.-P. Kriegel. The
X-tree: An Index Structure for High-Dimensional Data. In VLDB, 1996. https://pdfs.semanticscholar.org/83f6/d2b79b68af1115db013907df78b96dd82ea7.pdf
v (SS-tree) D. A. White and R. Jain. Similarity Indexing with the
SS-tree. In ICDE, 1996. http://www.cs.uml.edu/~cchen/580-S06/reading/WJ96.pdf
v (SR-tree) N.
Katayama and S. Satoh. The SR-tree: an index structure for high-dimensional
nearest neighbor queries. In SIGMOD, 1997. https://pdfs.semanticscholar.org/20b5/fc20821968a2e990183ee4613c591951597c.pdf
v (M-tree) P.
Ciaccia, M. Patella, and P. Zezula. M-tree An Efficient Access Method for
Similarity Search in Metric Spaces. In VLDB, 1997. http://www.vldb.org/conf/1997/P426.PDF
v (OMNI-family) R.F.S. Filho, A.
Traina, C. Traina, and C. Faloutsos. Similarity
search without tears: the OMNI-family of all-purpose access methods. In ICDE,
2001. http://repository.cmu.edu/cgi/viewcontent.cgi?article=1565&context=compsci or http://ieeexplore.ieee.org/document/914877/?reload=true
v
(High-dimensional
indexing) C. Faloutsos, M.
Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series
databases. In SIGMOD, 1994. http://dl.acm.org/citation.cfm?id=191925
v (Locality Sensitive Hashing) A. Gionis, P. Indyk, and R.
Motwani. Similarity Search in High Dimensions via Hashing. In VLDB, 1999. http://www.vldb.org/conf/1999/P49.pdf
v H. Samet. Foundations
of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in
Computer Graphics and Geometric Modeling, ISBN: 0123694469, 2005. http://dl.acm.org/citation.cfm?id=1076819
v ...
o Queries Over Big Data
v (Range Query) http://www.bowdoin.edu/~ltoma/teaching/cs340/spring08/Papers/Rtree-chap1.pdf
v (Nearest Neighbor Query; Depth-First) N. Roussopoulos, S. Kelly, and F. Vincent. Nearest
Neighbor Queries. In SIGMOD, 1995. http://www.postgis.org/support/nearestneighbor.pdf
v (Nearest Neighbor Query; Best-First) A. Henrich. A Distance Scan Algorithm for Spatial
Access Structures. In ACM GIS, 1994. https://pdfs.semanticscholar.org/37cc/e942c8f4a7d501f15bbfe41700cf98be2173.pdf
v (k-Nearest Neighbor Query; VoR-Tree) M. Sharifzadeh and C. Shahabi. VoR-Tree: R-trees with
Voronoi Diagrams for Efficient Processing of Spatial Nearest Neighbor Queries.
In VLDB, 2010. http://infolab.usc.edu/papers/VorTree.pdf
v (Group Nearest Neighbor Query) D. Papadias, Q. Shen, Y. Tao, and K. Mouratidis. Group
Nearest Neighbor Queries. In ICDE, 2004. http://www.cs.ust.hk/~dimitris/PAPERS/ICDE04-GNN.pdf
v (Reverse Nearest Neighbor Query; KM) F. Korn and S. Muthukrishnan. Influence Sets Based on
Reverse Nearest Neighbor Queries. In SIGMOD, 2000. https://graphics.stanford.edu/courses/cs468-06-fall/Papers/19%20reverse%202.pdf
v (Reverse Nearest Neighbor Query; YL) C. Yang and K. Lin. An Index Structure for Efficient
Reverse Nearest Neighbor Queries. In ICDE, 2001. http://ieeexplore.ieee.org/document/914862/
v (Reverse Nearest Neighbor Query; SAA) I. Stanoi, D.
Agrawal, and A. Abbadi. Reverse Nearest
Neighbor Queries for Dynamic Databases. In SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery,
2000. http://infolab.usc.edu/csci599/Fall2007/papers/b-2.pdf
v (Reverse Nearest Neighbor Query; TPL) Y. Tao, D. Papadias, and X. Lian. Reverse kNN
Search in Arbitrary Dimensionality. In VLDB, 2004. http://www.cs.kent.edu/~xlian/papers/VLDB04-RNN.pdf
v (Top-k Query; Onion) Y.-C. Chang, L. D. Bergman, V. Castelli, C.-S. Li,
M.-L. Lo, and J. R. Smith. The Onion Technique: Indexing for Linear Optimization
Queries. In SIGMOD, 2000. http://dl.acm.org/citation.cfm?id=335433
v (Top-k Query; PREFER) V. Hristidis, N. Koudas, and Y. Papakonstantinou.
PREFER: A System for the Efficient Execution of Multi-Parametric Ranked Queries.
In SIGMOD, 2001. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.151.6379&rep=rep1&type=pdf
v (Skyline Query; BNL & D&C) S. Börzsönyi, D. Kossmann, and K. Stocker. The Skyline
Operator. In ICDE, 2001. http://infolab.usc.edu/csci599/Fall2007/papers/e-1.pdf
v (Skyline Query; Bitmap & Index) K. Tan, P. Eng, and B. Ooi. Efficient Progressive
Skyline Computation. In VLDB, 2001. http://www.vldb.org/conf/2001/P301.pdf
v (Skyline Query; NN) D.
Kossmann, F. Ramsak, and S. Rost. Shooting Stars in the Sky: an Online
Algorithm for Skyline Queries. In VLDB, 2002. https://pdfs.semanticscholar.org/10fe/ecb5eebbb958439aabb2e10bd56739e315c9.pdf
v (Skyline Query; BBS) D. Papadias, Y. Tao, G. Fu, and B. Seeger. An Optimal
and Progressive Algorithm for Skyline Queries. In SIGMOD, 2003. http://www.cs.ust.hk/~dimitris/PAPERS/SIGMOD03-Skyline.pdf
v (Spatial Skyline) M.
Sharifzadeh and C. Shahabi. The Spatial Skyline Queries. In VLDB, 2006. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.105.858&rep=rep1&type=pdf
v (Multi-Source Skyline) K. Deng, X. Zhou, and H. T. Shen. Multi-Source Skyline
Query Processing in Road Networks. In ICDE, 2007. http://ieeexplore.ieee.org/document/4221728/
v (Metric Skyline) L.
Chen and X. Lian. Dynamic Skyline Queries in Metric Spaces. In EDBT,
2008. http://dl.acm.org/citation.cfm?id=1353386
v (Top-k Dominating Query) M. L. Yiu and N. Mamoulis. Efficient Processing of
Top-k Dominating Queries on Multi-Dimensional Data. In VLDB,
2007. https://pdfs.semanticscholar.org/2817/630b8e919c8ecb61b7397f4dc11ca7d93e91.pdf
v (aR-Tree) I.
Lazaridis and S. Mehrotra. Progressive Approximate Aggregate Queries with a
Multi-Resolution Tree Structure. In SIGMOD, 2001. ftp://ftp.cse.buffalo.edu/users/azhang/disc/SIGMOD/pdf-files/401/250-progressive.pdf
v (Reverse Skyline Query) E. Dellis and B. Seeger. Efficient Computation of
Reverse Skyline Queries. In VLDB, 2007.
v (Inverse Ranking Query) C. Li. Enabling data retrieval: by ranking and beyond.
In Ph.D. Dissertation, University of Illinois at Urbana-Champaign,
2007.
v (Aggregate Query) I.
Lazaridis and S. Mehrotra. Progressive Approximate Aggregate Queries with a
Multi-Resolution Tree Structure. In SIGMOD, 2001.
v (Histogram) M.
Muralikrishna and D. J. DeWitt. Equi-depth multidimensional histograms. In SIGMOD,
1988.
v (Sampling) R.
J. Lipton, J. F. Naughton, and D. A. Schneider. Practical Selectivity
Estimation through Adaptive Sampling. In SIGMOD, 1990.
v (Wavelet) M.
Garofalakis and P. B. Gibbons. Wavelet Synopses with Error Guarantees. In SIGMOD,
2002.
v (Keyword Search; BANK) A. Hulgeri and C. Nakhe. Keyword Searching and
Browsing in Databases using BANKS. In ICDE, 2002.
v (Keyword Search; BANK) V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan,
R. Desai, and H. Karambelkar. Bidirectional expansion for keyword search on
graph databases. In VLDB, 2005.
v (Keyword Search; BLINKS) H. He, H. Wang, J. Yang, and P. S. Yu. BLINKS: ranked
keyword searches on graphs. In SIGMOD, 2007. http://db.cs.duke.edu/papers/2007-SIGMOD-hwyy-kwgraph.pdf
v ...
o Big Graph Data
o Big Probabilistic Data
o Big Data Management in Real Applications (e.g., time-series,
sensor networks, road networks, social networks, bioinformatics, Semantic Web,
etc.)
Research
papers/surveys from database conferences/journals (SIGMOD, PVLDB, ICDE, TODS,
VLDBJ, and TKDE)
o Database Journals
v TODS: http://dblp.uni-trier.de/db/journals/tods/index.html
v VLDBJ: http://dblp.uni-trier.de/db/journals/vldb/
v TKDE: http://dblp.uni-trier.de/db/journals/tkde/index.html
o Database Conferences
v SIGMOD: http://dblp.uni-trier.de/db/conf/sigmod/
v VLDB: http://www.vldb.org/pvldb/, or http://dblp.uni-trier.de/db/journals/pvldb/index.html
v ICDE: http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178, or http://dblp.uni-trier.de/db/conf/icde/
o ACM Computing Surveys
o Samples of surveys:
v Indexing: https://www.slac.stanford.edu/pubs/slacpubs/16250/slac-pub-16460.pdf
v A Survey of Large-Scale Analytical Query Processing in
MapReduce: http://link.springer.com/article/10.1007/s00778-013-0319-9
v A Survey on Parallel and Distributed Data Warehouses: https://pdfs.semanticscholar.org/4f3e/d0d4dfbd0bf4648a7feda94e3176e33ad088.pdf
Online resources
o Datasets and Source Code
v Spatial data sets and index source code: http://chorochronos.datastories.org/
v Road network and stream data: https://www.cs.utah.edu/~lifeifei/datasets.html
v DBpedia RDF data: http://www.dbpedia.org
v Freebase RDF data: https://developers.google.com/freebase/
v 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
o Amazon AWS
o Tutorial
v https://www.lynda.com/ (Sign in with the organization portal)
Survey/research topics (note: You must contact me via my email (xlian@kent.edu) to select the topics below with my permission; first
come first serve)
o Distributed indexing
o Queries over (distributed) spatio-temporal data
o Queries over (distributed) time-series data
o Queries over (distributed) stream data
o Queries over (distributed) graph data (e.g., RDF
graphs, social networks, road networks, biological networks, chemical
compounds, etc.)
o Queries over (distributed) probabilistic/uncertain
data
o Data privacy preserving
v (k-Anonymity) K. LeFevre, D. J. DeWitt, and R. Ramakrishnan.
Incognito: Efficient full-domain k-anonymity.
In Proc. of the ACM SIGMOD International
Conference on Management of Data (SIGMOD), pages 49 - 60, 2005.
v (l-Diversity) A. Machanavajjhala, J. Gehrke, D. Kifer, and M.
Venkitasubramaniam. l-diversity:
Privacy beyond k-anonymity. In IEEE International Conference on Data
Engineering (ICDE), page 24, 2006.
v (t-Closeness) N. Li, T. Li, and S. Venkatasubramanian. t-closeness: Privacy beyond k-anonymity and l-diversity. In IEEE
International Conference on Data Engineering (ICDE), pages 106 - 115, 2007.
o Graph privacy preserving
v Zach Jorgensen, Ting Yu, Graham Cormode. Publishing
Attributed Social Graphs with Formal Privacy Guarantees. In SIGMOD, 2016.
v Wei-Yen Day, Ninghui Li, Min Lyu. Publishing Graph
Degree Distribution with Node Differential Privacy. In SIGMOD, 2016.
v Zhao Chang, Lei Zou, Feifei Li. Privacy Preserving
Subgraph Matching on Large Graphs in Cloud. In SIGMOD, 2016.
o Big data visualization
v Visualization of R*-tree index construction
[required skills: C++ or C#, visual
programming with C++/C#], R*-tree source code: http://chorochronos.datastories.org/
*
N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: an
efficient and robust access method for points and rectangles. In SIGMOD,
1990. https://www.cs.umd.edu/class/fall2002/cmsc818s/Readings/rstar-tree.pdf
v Visualization of queries over spatio-temporal data
(e.g., GPS data, sensory data, etc.) [required
skills: C++ or C#, visual programming with C++/C#], R*-tree source code: http://chorochronos.datastories.org/
v Visualization of time-series data (e.g., stock time
series, or trajectory) [required skills:
C++, C#, Java, other visual programming tools, or mobile programming]
v Visualization of query processing over large-scale
road networks (e.g., trip planner, the shortest traveling time query, and gas
station query) [required skills:
C++, C#, Java, or other visual programming tools]
v Visualization of RDF graph queries in the Semantic Web
(e.g., subgraph matching) [required
skills: C++, C#, Java, or other visual programming tools]
*
K-nearest keyword search: http://www.sciencedirect.com/science/article/pii/S1570826813000371
*
Keyword search over probabilistic RDF graphs: http://ieeexplore.ieee.org/document/6940261/
*
Subgraph matching over probabilistic RDF graphs: http://dl.acm.org/citation.cfm?id=1989341
v Visualization of social networks (including social
network data extraction and keyword search) [required skills: C++, C#, Java, or other visual programming tools]
v Visualization of Web data (including Web crawling and
Web page visualization) [required skills:
C++, C#, Java, or other visual/network programming tools]
o ...
Groups
o Group #1: Queries over (distributed) time-series data
o Group #2: Visualization of social networks
o Group #3: Data privacy preserving
o Group #4: Queries over (distributed) stream data
o Group #5: Visualization of social networks (including
social network data extraction and keyword search)
o Group #6: Queries over (distributed) time-series data
o Group #7: Big data clustering
o Group #8:
o Group #9: Queries over (distributed) graph data
o Group #10: Visualization of social networks
o Group #11: A divide and merge methodology for
clustering
o Group #12: Research on time series analysis over
finance and health care
o Group #13: Time series data visualization
o Group #14: Visualization of query processing over
large-scale road networks
Catalog Description
This course will cover a series of important Big-Data-related
problems and their solutions. Specifically, we will introduce the
characteristics and challenges of the Big Data, state-of-the-art computing
paradigms/platforms (e.g., MapReduce), big data programming tools (e.g.,
Hadoop), big data extraction/integration, big data storage, scalable indexing
for big data, big graph processing, big data stream techniques and algorithms,
big probabilistic data management, big data privacy, big data visualizations,
and big data applications (e.g., spatial, finance, multimedia, medical, health,
and social data).
Tentative Schedule
Week |
Topics |
Notes1 |
Week 1 (Jan. 16) |
|
|
Week 1 (Jan. 18) |
|
|
Week 2 (Jan. 23) |
|
|
Week 2 (Jan. 25) |
|
Homework
1 (Due on Feb. 8) |
Week 3 (Jan. 30) |
|
|
Week 3 (Feb. 1) |
Feb. 1: Deadline to form a study group with 2-4
members CS Research Day (Feb. 2) |
|
Week 4 (Feb. 6) |
|
Submission of a Reading List for the Survey (Due on Feb. 6) |
Week 4 (Feb. 8) |
|
Homework
2 (Due on Feb. 22) |
Week 5 (Feb. 13) |
Please let me know if you want to give a
presentation on some research papers related to big data! [extra 5 bonus points] |
|
Week 5 (Feb. 15) |
|
|
Week 6 (Feb. 20) |
Survey/Project Discussions (no lecture) |
|
Week 6 (Feb. 22) |
|
Homework
3 (Due on Mar. 13) |
Week 7 (Feb. 27) |
Divya Lingwal: Spark: Cluster Computing with Working
Set [paper] [slides] |
Survey
(Due on Feb. 27) |
Week 7 (Mar. 1) |
|
|
Week 8 (Mar. 6) |
|
|
Week 8 (Mar. 8) |
Shaista Gulnaar: [paper] [slides] |
Homework
4 (Due on Apr. 3; extended
to Apr. 11) Please schedule a 15-min meeting with
me, if you want to discuss with me about your project topics |
Week 9 (Mar. 13) |
|
|
Week 9 (Mar. 15) |
Survey/Project Discussions, Q/A |
|
Week 10 (Mar. 20) |
|
|
Week 10 (Mar. 22) |
Homework
5 (Due on Apr. 19) Last Day to Withdraw: 3/25/2018 |
|
Week 11 (Mar. 27) |
-- |
Spring Recess: Mar. 26-Apr. 1; No
Classes |
Week 11 (Mar. 29) |
||
Week 12 (Apr. 3) |
|
|
Week 12 (Apr. 5) |
Preparation for Projects; No class -- |
|
Week 13 (Apr. 10) |
Project
Presentations (Groups #1, #2, #4) Group #1: Wikipedia Traffic Forecasting [slides] Group #2: Sentimental Analysis of Various News
Channels based on Health Tweets [slides] Group #4: Feature Extraction on Twitter Streaming
data using Spark RDD [slides] |
20-min presentation & demo 5-min Q/A |
Week 13 (Apr. 12) |
Project
Presentations (Groups #5, #6, #8) Group #5: Query Processing
and Visualization Using Hive [slides] Group #6: Queries over Time-Series Data [slides] Group #8: Cultural Heritage
and Big Data [slides] |
|
Week 14 (Apr. 17) |
-- |
|
Week 14 (Apr. 19) |
-- |
|
Week 15 (Apr. 24) |
Project
Presentations (Groups #9, #10, #11) Group #9: A Restaurant Recommendation
System Based on Range and Skyline Queries [slides] Group #10: Group #11: Renouncing Hotel's
Data Through Queries Using Hadoop [slides] |
|
Week 15 (Apr. 26) |
Project
Presentations (Groups #3, #7) Group #3: Group #7: |
Course Evaluation |
Week 16 (May 1) |
Project
Presentations
(Groups #12, #13, #14) Group #12: Group #13: Group #14: Visualizing Query Processing
Over Large-Scale Road Networks [slides] |
|
Week 16 (May 3) |
|
Final
Project Report (Due on May 3; Please include project
report, source code, readme file, presentation slides, and all other
documents) |
Week 17 (May 7-13) |
-- |
No Final Exam |
Academic calendar: https://www.kent.edu/sites/default/files/academic-calendar-2014-2018_0.pdf
Final exam schedule: http://www.kent.edu/registrar/spring-final-exam-schedule
NOTE: Presentation dates and deadlines are
tentative. Exact dates will be announced in class!!!
5%
- Attendance
50%
- Assignments
10%
- Survey
25%
- Research Project
10%
- Presentation & Q/A
5%
- Bonus Points, rated by other team members
A
= 90 - 105
B
= 80 - 89
C
= 70 - 79
D
= 60 - 69
F
= <60
Guidelines for Assignments/Surveys/Projects
All assignments/surveys/projects
will be submitted electronically only. Instructions are given separately.
Ø Assignments must be submitted to Blackboard by the due date. Note that, for team assignments (e.g., surveys or projects), only one team member can represent your team to submit the assignments (otherwise, it is not traceable which submission is the correct version).
Ø An assignment/project turned in within two weeks after the due date will be considered late and will lose 30% of its grade.
Ø No assignment will be accepted for grading after two weeks late.
Ø The late submission needs prior consent of the instructor.
For surveys/projects, please form a team with 2-4 team members. In each team, all team members need to do research works (i.e., 1 survey and 1 project). The workload should be distributed evenly to each team member. All team members should participate in the surveys or projects, and receive the same score for survey/project. However, there is an extra bonus points (5 points) for other team members to rate your performance in the team work.
* Please send the full names, student IDs, and emails of all team members to the TA by Feb. 1, 2018, and TA will confirm your team by replying you with your team number.
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.