# THU201606X 高级大数据系统（学堂在线）课外阅读资料

> 作者：zning（https://ning.ae） · 发布：2017/01/10 18:38 下午 (UTC+8) · 原文：https://zning.me/14840447285917.html

> 摘自本课课程公告，以备后用

为方便同学们对本课程的课外扩展，特此整理了近年来一系列重要的topic，我们给每个topic都提供了一些经典参考论文，以供大家阅读。

**Topic**

1. **MapReduce / Hadoop**
    1. Dean, Jeffrey, and Sanjay Ghemawat. "MapReduce: simplified data processing on large clusters." *Communications of the ACM* 51.1 (2008): 107-113.
    2. Dean, Jeffrey, and Sanjay Ghemawat. "MapReduce: a flexible data processing tool." *Communications of the ACM* 53.1 (2010): 72-77.
    3. Isard, Michael, et al. "Dryad: distributed data-parallel programs from sequential building blocks." *ACM SIGOPS Operating Systems Review*. Vol. 41\. No. 3\. ACM, 2007.
2. **In-Memory Processing / Spark**
    1. Zaharia, Matei, et al. "Spark: cluster computing with working sets." *HotCloud*10 (2010): 10-10.
    2. Zaharia, Matei, et al. "Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing." *Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation*. USENIX Association, 2012.
    3. Zaharia M, Chowdhury M, Franklin M J, et al. Spark: cluster computing with working sets[J]. HotCloud, 2010, 10: 10-10.
3. **Graph Processing**
    1. Low, Yucheng, et al. "Distributed GraphLab: a framework for machine learning and data mining in the cloud." *Proceedings of the VLDB Endowment*5.8 (2012): 716-727.
    2. Kyrola, Aapo, Guy Blelloch, and Carlos Guestrin. "GraphChi: large-scale graph computation on just a PC." *Presented as part of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 12)*. 2012.
    3. Malewicz, Grzegorz, et al. "Pregel: a system for large-scale graph processing." *Proceedings of the 2010 ACM SIGMOD International Conference on Management of data*. ACM, 2010.
    4. Low, Yucheng, et al. "Graphlab: A new framework for parallel machine learning." *arXiv preprint arXiv:1408.2041* (2014).
4. **Streaming data proccesing**
    1. Toshniwal, Ankit, et al. "Storm@ twitter." *Proceedings of the 2014 ACM SIGMOD international conference on Management of data*. ACM, 2014.
    2. Zaharia, Matei, et al. "Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters." *Presented as part of the*. 2012.
    3. Zaharia, Matei, et al. "Discretized streams: Fault-tolerant streaming computation at scale." *Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles*. ACM, 2013.
    4. Namiot, Dmitry. "On Big Data Stream Processing." *International Journal of Open Information Technologies* 3.8 (2015): 48-51.
5. **Big Data Machine Learning System**
    1. Meng, Xiangrui, et al. "Mllib: Machine learning in apache spark." *JMLR* 17.34 (2016): 1-7.
    2. Lin, Jimmy, and Alek Kolcz. "Large-scale machine learning at twitter."*Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data*. ACM, 2012.
    3. Wu X, Zhu X, Wu G Q, et al. Data mining with big data[J]. IEEE transactions on knowledge and data engineering, 2014, 26(1): 97-107.
    4. Fan W, Bifet A. Mining big data: current status, and forecast to the future[J]. ACM sIGKDD Explorations Newsletter, 2013, 14(2): 1-5.
6. **Deep learning**
    1. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." *Nature*521.7553 (2015): 436-444.
    2. Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." *Nature* [529.7587](tel:529.7587) (2016): 484-489.
    3. LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
    4. Schmidhuber J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117.
7. **NoSQL**
    1. Chang, Fay, et al. "Bigtable: A distributed storage system for structured data."*ACM Transactions on Computer Systems (TOCS)* 26.2 (2008): 4.
    2. DeCandia, Giuseppe, et al. "Dynamo: amazon's highly available key-value store." *ACM SIGOPS Operating Systems Review* 41.6 (2007): 205-220.
    3. Lakshman, Avinash, and Prashant Malik. "Cassandra: a decentralized structured storage system." *ACM SIGOPS Operating Systems Review* 44.2 (2010): 35-40.
    4. Cattell, Rick. "Scalable SQL and NoSQL data stores." *Acm Sigmod Record*39.4 (2011): 12-27.
8. **Distributed File System: GFS and HDFS**
    1. Ghemawat, Sanjay, Howard Gobioff, and Shun-Tak Leung. "The Google file system." *ACM SIGOPS operating systems review*. Vol. 37\. No. 5\. ACM, 2003.
    2. Shvachko, Konstantin, et al. "The hadoop distributed file system." *2010 IEEE 26th symposium on mass storage systems and technologies (MSST)*. IEEE, 2010.
    3. Lakshman, Avinash, and Prashant Malik. "Cassandra: a decentralized structured storage system." *ACM SIGOPS Operating Systems Review* 44.2 (2010): 35-40.
9. **Cloud Computing**
    1. Armbrust, Michael, et al. "Above the clouds: A berkeley view of cloud computing." (2009).
    2. Foster, Ian, et al. "Cloud computing and grid computing 360-degree compared." *2008 Grid Computing Environments Workshop*. Ieee, 2008.
    3. Cooper, Brian F., et al. "Benchmarking cloud serving systems with YCSB."*Proceedings of the 1st ACM symposium on Cloud computing*. ACM, 2010.
10. **Resource Allocation and Management**
    1. Hindman, Benjamin, et al. "Mesos: A Platform for Fine-Grained Resource Sharing in the Data Center." *NSDI*. Vol. 11\. 2011.
    2. Vavilapalli, Vinod Kumar, et al. "Apache hadoop yarn: Yet another resource negotiator." *Proceedings of the 4th annual Symposium on Cloud Computing*. ACM, 2013.
    3. Hunt, Patrick, et al. "ZooKeeper: Wait-free Coordination for Internet-scale Systems." *USENIX Annual Technical Conference*. Vol. 8\. 2010.
11. **Full-stack Big Data System**
    1. Franklin, Matthew. "The berkeley data analytics stack: Present and future."*Big Data, 2013 IEEE International Conference on*. IEEE, 2013.
    2. Zaharia, Matei, et al. "Fast and interactive analytics over Hadoop data with Spark." *USENIX Login* 37.4 (2012): 45-51.
12. **Data Visualization System**
    1. Schroeder W J, Lorensen B, Martin K. The visualization toolkit[M]. Kitware, 2004.
    2. Bertini E, Tatu A, Keim D. Quality metrics in high-dimensional data visualization: An overview and systematization[J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(12): [2203-2212](tel:2203-2212).
    3. Kehrer J, Hauser H. Visualization and visual analysis of multifaceted scientific data: A survey[J]. IEEE transactions on visualization and computer graphics, 2013, 19(3): 495-513.
    4. Etemadpour R, Motta R, de Souza Paiva J G, et al. Perception-based evaluation of projection methods for multidimensional data visualization[J]. IEEE transactions on visualization and computer graphics, 2015, 21(1): 81-94.
13. **Recommendation System**
    1. McDonald D W, Ackerman M S. Expertise recommender: a flexible recommendation system and architecture[C]//Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM, 2000: 231-240.
    2. Davidson J, Liebald B, Liu J, et al. The YouTube video recommendation system[C]//Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010: 293-296.
    3. Mo Y, Chen J, Xie X, et al. Cloud-based mobile multimedia recommendation system with user behavior information[J]. IEEE Systems Journal, 2014, 8(1): 184-193.
    4. Phelan O, McCarthy K, Smyth B. Using twitter to recommend real-time topical news[C]//Proceedings of the third ACM conference on Recommender systems. ACM, 2009: 385-388.