| Note
that this schedule will be altered during the semester.
Please make sure to check it every week. |
|
Week 01 | Jan. 22
Topics
1. Introduction to Big Data
Course Introduction
Readings
[W1R1] Keshav's "How to read a
paper"[Link]
[W1R2] "How to Read and Understand a Scientific Paper: A
Step-by-Step Guide for Non-Scientists"[Link]
Lecture Notes
1/22:
2 slides per page [Download]
6 slides per page [Download]
Notes
1/20 University Holiday (No class)
1/24 End Restricted Drop
1/26 End Add Without Override
2/05 Registration Closes: Last day for dropping courses without record entry, changes in grade option, and tuition and fee adjustment
3/14 Spring Break
3/23 Classes Resume
3/23 End Course Withdrawal ("W") Period
3/23 Repeat/Delete Deadline
5/08 Last Day of Classes: University Withdrawal Deadline
CSU Academic Calendar 2019-20 [Link]
|
|
Week
02 | Jan. 27 | Jan. 29
Topics
2. Data process paradigms for Big
Data
3. Distributed Computing Models for Scalable Batch
Computing
Part 1. MapReduce
Part 2. In-Memory Cluster
Computing- Apache Spark
Readings
[W2R1] Jeffrey
Dean and Sanjay Ghemawat, "MapReduce:Simplified Data
Processing on Large Clusters" In Proceeding
OSDI'04 Proceedings of the 6th conference on Symposium
on Opearting Systems Design & Implementation - Vol.
6, [Link]
Lecture
Notes
1/27:
2 slides per page [Download]
6 slides per page [Download]
1/29:
2 slides per page [Download]
6 slides per page [Download]
Notes
2/05 Registration Closes: Last day for dropping courses without record entry, changes in grade option, and tuition and fee adjustment
3/14 Spring Break
3/23 Classes Resume
3/23 End Course Withdrawal ("W") Period
3/23 Repeat/Delete Deadline
5/08 Last Day of Classes: University Withdrawal Deadline
CSU Academic Calendar 2019-20 [Link]
|
|
Week 03 | Feb. 03 | Feb. 05
Topics
3.
Distributed Computing Models for Scalable Batch
Computing: Part 2. In-Memory Cluster
Computing- Apache Spark
Readings
[W3R1] Matei
Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave,
Justin Ma, Murphy McCauley, Michael J. Franklin, Scott
Shenker, Ion Stoica, Resilient Distributed Datasets: A
Fault-Tolerant Abstraction for In-Memory Cluster
Computing, USENIX on NSDI 2012, [Link]
Lecture Notes
2/3:
2 slides per page [Download]
6 slides per page [Download]
2/5:
2 slides per page [Download]
6 slides per page [Download]
Notes
|
|
Week 04 | Feb. 10 | Feb. 12
Topics
3. Distributed Computing Models
for Scalable Batch Computing: Part 2. In-Memory
Cluster Computing- Apache Spark
4.
Real-time Streaming Computing Models: Apache Storm and
Twitter Heron
Readings
[W4R1] Ankit Toshniwal, Siddarth Taneja, Amit Shukla,
Karthik Ramasamy, Jignesh M. Patel, Sanjeev Kulkarni,
Jason Jackson, Krishna Gade, Maosong Fu, Jake Donham,
Nikunj Bhagat, Sailesh Mittal, and Dmitriy Ryaboy.
2014. Storm@twitter. In Proceedings of the 2014 ACM
SIGMOD International Conference on Management of Data
(SIGMOD '14). ACM, New York, NY, USA, 147-156. DOI:
https://doi.org/10.1145/2588555.2595641 [Link]
[W4R2]
Sanjeev Kulkarni, Nikunj Bhagat, Maosong Fu, Vikas
Kedigehalli, Christopher Kellogg, Sailesh Mittal,
Jignesh M. Patel, Karthik Ramasamy, and Siddarth
Taneja. 2015. Twitter Heron: Stream Processing at
Scale. In Proceedings of the 2015 ACM SIGMOD
International Conference on Management of Data (SIGMOD
'15). ACM, New York, NY, USA, 239-250. DOI:
https://doi.org/10.1145/2723372.2742788 [Link]
Lecture Notes
2/10:
2 slides per page [Download]
6 slides per page [Download]
2/12:
2 slides per page [Download]
6 slides per page [Download]
Notes
|
[GEAR
Session I] Week 05 |
Feb. 17 | Feb. 19
Guided Exploration for Big Data Analytics Research (GEAR) [Visit the GEAR Session page]
The CS535 Guided Exploration for Big Data Analytics Research (GEAR) Sessions are designed to provide a guided learning environment for advanced topics in Big Data analytics research. GEAR involves active participation from students. The class will involve lectures (up to 75%) discussing fundamental concepts of the targeted topic and about 25% of the class will be based on student-led research discussions. Students will provide a critical review of cutting-edge research papers. These discussions provide students an opportunity to extend their knowledge and concepts covered in the lectures to real-world problems, and further explore future research directions. More info
Topics
GEAR
Session I: Peta-scale Storage Systems
5. Scalable Distributed
File Systems: Google File System I and II
Readings (These are for the lectures)
[GEAR-I-1]
Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung, "The
Google file system" Proceedings of SOSP 2003:
29-43 [Link]
[GEAR-I-2] C.K.P. Clarke, Reed-Solomon Error Correction [Link]
Lecture Notes
2/17:
2 slides per page [Download]
6 slides per page [Download]
2/29:
2 slides per page [Download]
6 slides per page [Download]
Notes
|
|
[GEAR
Session I] Week 06 | Feb. 24 | Feb. 26
Topics
GEAR
Session I: Peta-scale Storage Systems
6. Scalable NoSQL Systems: DHT based key-value storage
Readings (for lectures)
[GEAR-I-3] Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek, and Hari Balakrishnan. 2001. Chord: A scalable peer-to-peer lookup service for internet applications. In Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications (SIGCOMM '01). ACM, New York, NY, USA, 149-160. DOI=http://dx.doi.org/10.1145/383059.383071
[GEAR-I-4] Lakshman, Avinash & Malik, Prashant. (2010). Cassandra — A Decentralized Structured Storage System. Operating Systems Review. 44. 35-40. 10.1145/1773912.1773922.
Lecture
Notes
2/24:
2 slides per page [Download]
6 slides per page [Download]
2/26: Please submit your slides at least 2 hours before the presentation via canvas.
Notes
|
|
[GEAR
Session II] Week 07 | Mar. 02 | Mar. 04
Topics
GEAR
Session II: Machine Learning for Big Data
7. Large-scale Clustering
8. Deep Learning
Readings (for lectures)
[GEAR-II-1] Bahmani, B., Moseley, B., Vattani, A., Kumar, R. and Vassilvitskii, S., 2012. Scalable k-means++. arXiv preprint arXiv:1203.6402.
[GEAR-II-2] Jeffrey
Dean and Greg S. Corrado and Rajat Monga and Kai Chen
and Matthieu Devin and Quoc V. Le and Mark Z. Mao and
Marc’Aurelio Ranzato and Andrew Senior and Paul Tucker
and Ke Yang and Andrew Y. Ng, Large Scale Distributed
Deep Networks, 2012, NIPS
[GEAR-II-3] Martin Abadi and
Paul Barham and Jianmin Chen and Zhifeng Chen and Andy
Davis and Jeffrey Dean and Matthieu Devin and Sanjay
Ghemawat and Geoffrey Irving and Michael Isard and
Manjunath Kudlur and Josh Levenberg and Rajat Monga and
Sherry Moore and Derek G. Murray and Benoit Steiner and
Paul Tucker and Vijay Vasudevan and Pete Warden and
Martin Wicke and Yuan Yu and Xiaoqiang Zheng,
TensorFlow: A system for large-scale machine learning,
12th USENIX Symposium on Operating Systems Design and
Implementation (OSDI 16), 2016
Lecture Notes
3/02:
2 slides per page [Download]
6 slides per page [Download]
3/04:
2 slides per page [Download]
6 slides per page [Download]
Notes
|
|
[GEAR
Session II]
Week 08 | Mar. 09 | Mar. 11
Topics
GEAR
Session II: Machine Learning for Big Data
8. Deep Learning with TensorFlow
Readings (for lectures)
[GEAR-II-1] Bahmani, B., Moseley, B., Vattani, A., Kumar, R. and Vassilvitskii, S., 2012. Scalable k-means++. arXiv preprint arXiv:1203.6402.
[GEAR-II-2] Jeffrey
Dean and Greg S. Corrado and Rajat Monga and Kai Chen
and Matthieu Devin and Quoc V. Le and Mark Z. Mao and
Marc’Aurelio Ranzato and Andrew Senior and Paul Tucker
and Ke Yang and Andrew Y. Ng, Large Scale Distributed
Deep Networks, 2012, NIPS
[GEAR-II-3] Martin Abadi and
Paul Barham and Jianmin Chen and Zhifeng Chen and Andy
Davis and Jeffrey Dean and Matthieu Devin and Sanjay
Ghemawat and Geoffrey Irving and Michael Isard and
Manjunath Kudlur and Josh Levenberg and Rajat Monga and
Sherry Moore and Derek G. Murray and Benoit Steiner and
Paul Tucker and Vijay Vasudevan and Pete Warden and
Martin Wicke and Yuan Yu and Xiaoqiang Zheng,
TensorFlow: A system for large-scale machine learning,
12th USENIX Symposium on Operating Systems Design and
Implementation (OSDI 16), 2016
Lecture Notes
3/09:
2 slides per page [Download]
6 slides per page [Download]
Notes
3/14 Spring Break
3/23 Classes Resume
3/23 End Course Withdrawal ("W") Period
3/23 Repeat/Delete Deadline
5/08 Last Day of Classes: University Withdrawal Deadline
CSU Academic Calendar 2019-20 [Link]
|
[GEAR
Session II] Week 9 | Mar. 23 | Mar. 25
Topics
GEAR
Session II: Machine Learning for Big Data
9. Deep Learning with PyTorch
Readings (for lectures)
[GEAR-II-4]
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L. and Desmaison, A., 2019. PyTorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (pp. 8024-8035)
Lecture Notes
3/23: No Class
3/25:
2 slides per page [Download]
6 slides per page [Download]
Notes
3/14 Spring Break
3/23 Classes Resume --> 3/25 Classes Resume
CSU Academic Calendar 2019-20 [Link]
|
[GEAR
Session III] Week 10 | Mar. 30| Apr. 01
Topics
GEAR
Session III: Big Graph Analysis
Readings (for lectures)
[GEAR-III-1]
Grzegorz Malewicz, Matthew H. Austern, Aart J.C. Bik,
Names C. Dehnert, Ilan Horn, Naty Leiser, Grzegorz
Czajkowski, “Pregel: a system for large-scale graph
processing”, Proceedings of the 2010 ACM SIGMOD
International Conference on Management of Data, pp.
135-146
[GEAR-III-2] Joseph E.
Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw,
Michael J. Franklin, and Ion Stoica. 2014. GraphX: graph
processing in a distributed dataflow framework. In
Proceedings of the 11th USENIX conference on Operating
Systems Design and Implementation (OSDI'14). USENIX
Association, Berkeley, CA, USA, 599-613.
Lecture Notes
3/30:
2 slides per page [Download]
6 slides per page [Download]
4/2: postponed to 4/6
Notes
|
[GEAR
Session IV] Week 11 | Apr. 06 | Apr. 08
Topics
Workshop IV: Large Scale Recommendation Systems and Social Media
Readings (for lectures)
[GEAR-III-2] Joseph E.
Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw,
Michael J. Franklin, and Ion Stoica. 2014. GraphX: graph
processing in a distributed dataflow framework. In
Proceedings of the 11th USENIX conference on Operating
Systems Design and Implementation (OSDI'14). USENIX
Association, Berkeley, CA, USA, 599-613.
[GEAR IV-1] Greg Linden, Brent Smith, and Jeremy York, “Amazon.com Recommendations, Item-to-Item Collaborative Filtering” IEEE Internet Computing, 2003
Lecture Notes
4/06: Vertex-cut Graph Processing Model: GraphX
2 slides per page [Download]
6 slides per page [Download]
4/08: Large Scale Recommendation Systems
2 slides per page [Download]
6 slides per page [Download]
|
[GEAR
Session IV] Week 12| Apr. 13 | Apr. 15
Topics
Workshop IV: Large Scale Recommendation Systems and Social Media
Readings
(for lectures)
[GEAR IV-2] Koren, Y., Bell, R. and Volinsky, C., 2009. Matrix factorization techniques for recommender systems. Computer, 42(8), pp.30-37.
Lecture Notes
4/13:
2 slides per page [Download]
6 slides per page [Download]
4/15: GEAR session IV presentation: video clips are available in Canvas
| Apr. 17
|
[GEAR
Session V] Week 13 | Apr. 20| Apr. 22
Topics
Workshop V: Algorithmic Techniques for Big Data
Readings (for lectures)
[GEAR IV-3] Rajaraman, A. and Ullman, J.D., 2011. Mining of massive datasets. Cambridge University Press. Chapter 10. Mining Social Networks
[GEAR V-1] Rajaraman, A. and Ullman, J.D., 2011. Mining of massive datasets. Cambridge University Press. Chapter3.
Finding Similar Items
Lecture Notes
4/20:
2 slides per page [Download]
6 slides per page [Download]
4/22:
2 slides per page [Download]
6 slides per page [Download]
|
[GEAR
Session V] Week 14 | Apr. 27| Apr. 29
Topics
Workshop V: Algorithmic Techniques for Big Data
Readings (for lectures)
[GEAR V-1] Rajaraman, A. and Ullman, J.D., 2011. Mining of massive datasets. Cambridge University Press. Chapter3. Finding Similar Items
Lecture Notes
4/27:
2 slides per page [Download]
6 slides per page [Download] |
[Presentation Week] Week 15 | May 04| May 06
Schedule
TBA
|
