CS 49995 & CS
63016 ST: Big Data Analytics
Spring 2017
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: Big Data
Analytics
Prerequisites: Permission of the instructor
Time: 7:00pm ~ 8:15pm, TR
Classroom Location: Smith Hall (SMH),
Room 111
Course Webpage: http://www.cs.kent.edu/~xlian/course_archive/2017Spring_CS49995_CS63016.html
Instructor's
Office Hours: Tuesday
and Thursday (1:30pm ~ 4:30pm); or by appointment
Graduate Assistant: Zhiqiang
Wang
Office: N/A
E-mail: zwang22@kent.edu
Phone: N/A
TA's Office Hours: N/A
The official registration deadline for this course is Jan. 22, 2017. 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. 26, 2017
Textbooks and Reference Books
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 will appear here J
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)
Topics for undergraduate teams (note: You must contact me via my email (xlian@kent.edu) to select the topics below with my permission; first
come first serve)
o Visualization of R*-tree index construction
[required skills: C++ or C#, visual
programming with C++/C#], R*-tree source code: http://chorochronos.datastories.org/, unavailable
v 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 UG
Team #19 (Xiangxu's team)
o 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/, unavailable
v Skyline query with R*-tree (BBS algorithm):
https://pdfs.semanticscholar.org/c1ee/b9fc4b58031f71cb6926a470b6cb60646c15.pdf, UG Team # 11 (Jamie's team)
o Visualization of time-series data (e.g., stock time
series, or trajectory) [required skills:
C++, C#, Java, other visual programming tools, or mobile programming], available
v Stock data prediction: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4358952, UG Team #12 (Shane's team)
v Trajectory: https://pdfs.semanticscholar.org/8fcf/68502233c7cbf2360f126781fd14b54d265a.pdf, UG Team #15
o 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], available
v Trip planner: http://ieeexplore.ieee.org/document/6613474/, UG Team #2 (Joshua's team): Query processing over large scale
road networks
o Visualization of RDF graph queries in the Semantic Web
(e.g., subgraph matching) [required
skills: C++, C#, Java, or other visual programming tools], available
v K-nearest keyword search: http://www.sciencedirect.com/science/article/pii/S1570826813000371
v Keyword search over probabilistic RDF graphs: http://ieeexplore.ieee.org/document/6940261/
v Subgraph matching over probabilistic RDF graphs: http://dl.acm.org/citation.cfm?id=1989341
o Visualization of social networks (including social
network data extraction and keyword search) [required skills: C++, C#, Java, or other visual programming tools],
UG Team #3
(Aron's team): Social networks
o Visualization of Web data (including Web crawling and
Web page visualization) [required skills:
C++, C#, Java, or other visual/network programming tools], UG Team #6 (Tyrone' team): Visualization of Web
Data
o ...
Survey/research topics for graduate teams
o Distributed indexing
o Queries over (distributed) spatio-temporal
data
v G Team
#8 (Weichuan's team): Queries over (distributed) spatio-temporal data
o Queries over (distributed) time-series data
o Queries over (distributed) stream data
v G Team
#13 (Gayatri's team): Query Over Distributed Stream
Data
v G Team
#16 (Saikumar's team): Query Over Distributed Stream
Data
v G Team
#5 (Jampana's team): Query Over Distributed Stream
Data (Survey); Continuous NN, taxi data (Project)
o Queries over (distributed) graph data (e.g., RDF
graphs, social networks, road networks, biological networks, chemical
compounds, etc.)
v G Team
#18 (Kyle's team): Queries over graph data
v G Team
#9 (Muhammad's team): Queries over graph data in social networks
o Queries over (distributed) probabilistic/uncertain
data
v G Team
#1 (Niranjan's team): 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. G
Team #? (Surabhee' team): Data privacy
preserving (k-Anonymity)
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
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
and MongoDB), 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. 17) |
|
|
Week 1 (Jan. 19) |
|
|
Week 2 (Jan. 24) |
|
|
Week 2 (Jan. 26) |
|
Homework
1 (Due on Feb. 9) |
Week 3 (Jan. 31) |
|
|
Week 3 (Feb. 2) |
Feb. 2: Deadline to form a study group with 2-4
members |
|
Week 4 (Feb. 7) |
|
|
Week 4 (Feb. 9) |
|
Homework
2 (Due on Feb. 23) |
Week 5 (Feb. 14) |
|
|
Week 5 (Feb. 16) |
|
|
Week 6 (Feb. 21) |
|
|
Week 6 (Feb. 23) |
Survey/Project Discussions, Q/A |
Homework
3 (Due on Mar. 9) |
Week 7 (Feb. 28) |
|
|
Week 7 (Mar. 2) |
|
|
Week 8 (Mar. 7) |
|
|
Week 8 (Mar. 9) |
|
Homework
4 ( |
Week 9 (Mar. 14) |
|
Meetings: 2:30pm - Team #1 3:00pm - Team #8 |
Week 9 (Mar. 16) |
Survey/Project Discussions, Q/A |
Meetings: 3pm - Team #9 3:30pm - Team #5 4pm - Team #10 For graduate teams, please submit a reading list
of survey papers for the topic you choose (due on
Mar. 16). I will give you my comments. |
Week 10 (Mar. 21) |
Meetings: 1:30pm - Team #13 3:00pm - Team #1 8:15pm - Team #16 |
|
Week 10 (Mar. 23) |
|
Homework
5 ( Meetings: 10:00am - Team #9 1:30pm - Surabhee' team Last Day to Withdraw: 3/26/2017 |
Week 11 (Mar. 28) |
-- |
Spring Recess: Mar. 27-Apr. 2; No
Classes |
Week 11 (Mar. 30) |
||
Week 12 (Apr. 4) |
|
|
Week 12 (Apr. 6) |
Preparation for Projects; No class -- |
Project
Report (Sections 1-4) (Due
on Apr. 6; Better to include Section 5) Homework
6 (Due on Apr. 20) |
Week 13 (Apr. 11) |
Project Presentations (Teams #3, #4, #5) UG
Team #3: Sentimental Twitter, demo: http://geczy.tech/bigdata UG
Team #4: Keyword Search over RDF Graphs, demo: https://webdev.cs.kent.edu/~rleppelm/RDF.html G
Team #5: Ride Analytics on New York City Taxi Data |
Please send me slides of your project talks one week before your presentations! |
Week 13 (Apr. 13) |
Project Presentations (Teams #6, #7, #8) UG
Team #6: Data Exploration Of Wikipedia, demo: https://wikinavigation.github.io/ UG
Team #7: Visualization of Spatial-Temporal Data Through R*-Tree G
Team #8: aR-Tree based Hierarchical Clustering: A New Approach of Analyzing Social
Media Data, demo: http://personal.kent.edu/~qliu20/courseprojects/2017spring_bigdata/ |
Survey
(Due on Apr. 13) |
Week 14 (Apr. 18) |
-- |
|
Week 14 (Apr. 20) |
-- |
|
Week 15 (Apr. 25) |
Project Presentations (Teams #1, #2, #9) G
Team #1: Range-Aggregate Query on Distributed Uncertain Database UG
Team #2: Visualization of query processing over large-scale road networks |
|
Week 15 (Apr. 27) |
Project Presentations (Teams #10, #11, #12) G
Team #10: MapReduce: Data Distribution for Reduce UG
Team #11: Skyline query with R*-Tree: Branch and Bound Skyline (BBS) Algorithm Team
#12: |
|
Week 16 (May 2) |
Project Presentations (Teams #13, #15) Team
#13: Sentiment Analysis in Unstructured Text Data Team
#15 & #19: Time Series Data and Moving Object Trajectory |
|
Week 16 (May 4) |
Project Presentations (Teams #16, #17, #18) Team
#16: Video Surveillance Framework Based on BIGDATA Management for Road
Transport System Team
#17: Preserving Private Data Among Social Networking Sites Through k-Anonymity |
Final
Project Report ( Please rate your team members (0 ~ 5) on
Blackboard (by default, you will get 5 bonus points if no other team members
enter the ratings) |
Week 17 (May 8-14) |
-- |
|
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!!!
Undergraduate
students
5% - Attendance
60% - Assignments
25% - Project
10% - Presentation & Q/A
5% - Bonus Points, rated by other team members
Graduate students
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 should be either undergraduate or graduate students (i.e., not a mixed group). The graduate teams need to do more research works (i.e., 1 survey, replacing one homework). 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, emails, and graduate/undergraduate status of all team members to the TA (Zhiqiang Wang, zwang22@kent.edu) by Feb. 2, 2017, 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.