Key technologies for big data stream computing

Big Data: Algorithms, Analytics, and Applications

Volumn , Issue , 2015, Pages 193-214

  Sun, Dawei ^a   Zhang, Guangyan ^a   Zheng, Weimin ^a   Li, Keqin ^b  

^a TSINGHUA UNIVERSITY   (China)

^b HUNAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTER ARCHITECTURE;

APPLICATION INTERFACES; COMPUTING RESEARCH; DATA-INTENSIVE COMPUTING; DEVELOPMENT STATUS; HIGH AVAILABILITY; MASSIVELY PARALLEL PROCESSING; REAL TIME DATA ANALYSIS; SYSTEM ARCHITECTURES;

BIG DATA;

EID: 84964982859     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (32)

1. Storm. Available at http://storm-project.net/ (accessed July 16, 2013).
2. Neumeyer L, Robbins B, Nair A et al. S4: Distributed stream computing platform, Proc. 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010, Sydney, NSW, Australia, IEEE Press, December 2010, PP 170-177.
3. Zhao Y and Wu J. Dache: A data aware caching for big-data applications using the MapReduce framework, Proc. 32nd IEEE Conference on Computer Communications, INFOCOM 2013, IEEE Press, April 2013, PP 35-39.
4. Shang W, Jiang Z M, Hemmati H et al. Assisting developers of Big Data analytics applica­tions when deploying on Hadoop clouds, Proc. 35th International Conference on Software Engineering, ICSE 2013, IEEE Press, May 2013, PP 402-411.
5. Qian Z P, He Y, Su C Z et al. TimeStream: Reliable stream computation in the cloud, Proc. 8th ACM European Conference on Computer Systems, EuroSys 2013, Prague, Czech Republic, ACM Press, April 2013, PP 1-14.
6. Umut A A and Yan C. Streaming Big Data with self-adjusting computation, Proc. 2013 ACM SIGPLAN Workshop on Data Driven Functional Programming, Co-located with POPL 2013, DDFP 2013, Rome, Italy, ACM Press, January 2013, PP 15-18.
7. Demirkan H and Delen D. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and Big Data in cloud, Decision Support Systems, vol. 55(1), 2013, PP 412-421.
8. Albert B. Mining Big Data in real time, Informatica (Slovenia), vol. 37(1), 2013, PP 15-20.
9. Lu J and Li D. Bias correction in a small sample from Big Data, IEEE Transactions on Knowledge and Data Engineering, vol. 25(11), 2013, PP 2658-2663.
10. Tien J M. Big Data: Unleashing information, Journal of Systems Science and Systems Engineering, vol. 22(2), 2013, PP 127-151.
11. Zhang R, Koudas N, Ooi B C et al. Streaming multiple aggregations using phantoms, VLDB Journal, vol. 19(4), 2010, PP 557-583.
12. Hirzel M, Andrade H, Gedik B et al. IBM streams processing language: Analyzing Big Data in motion, IBM Journal of Research and Development, vol. 57(3/4), 2013, PP 7:1-7:11.
13. Dayarathna M and Toyotaro S. Automatic optimization of stream programs via source program operator graph transformations, Distributed and Parallel Databases, vol. 31(4), 2013, PP 543-599.
14. Farhad S M, Ko Y, Burgstaller B et al. Orchestration by approximation mapping stream programs onto multicore architectures, Proc. 16th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2011, ACM Press, March 2011, PP 357-367.
15. Schneider S, Hirzel M and Gedik B. Tutorial: Stream processing optimizations, Proc. 7th ACM International Conference on Distributed Event-Based Systems, DEBS 2013, ACM Press, June 2013, PP 249-258.
16. Khandekar R, Hildrum K, Parekh S et al. COLA: Optimizing stream processing applications via graph partitioning, Proc. 10th ACM/IFIP/USENIX International Conference on Middleware, Middleware 2009, ACM Press, November 2009, PP 1-20.
17. Scalosub G, Marbach P and Liebeherr J. Buffer management for aggregated streaming data with packet dependencies, IEEE Transactions on Parallel and Distributed Systems, vol. 24(3), 2013, PP 439-449.
18. Malensek M, Pallickara S L and Pallickara S. Exploiting geospatial and chronological characteristics in data streams to enable efficient storage and retrievals, Future Generation Computer Systems, vol. 29(4), 2013, PP 1049-1061.
19. Cugola G and Margara A. Processing flows of information: From data stream to complex event processing, ACM Computing Surveys, vol. 44(3), 2012, PP 15:1-15:62.
20. Storm wiki. Available at http://en.wikipedia.org/wiki/Storm (accessed July 16, 2013).
21. Storm Tutorial. Available at https://github.com/nathanmarz/storm/wiki (accessed July 16, 2013).
22. Chauhan J, Chowdhury S A and Makaroff D. Performance evaluation of Yahoo! S4: A first look, Proc. 7th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2012, Victoria, BC, Canada, IEEE Press, November 2012, PP 58-65.
23. Simoncelli D, Dusi M, Gringoli F et al. Scaling out the performance of service monitoring appli­cations with BlockMon, Proc. 14th International Conference on Passive and Active Measurement, PAM 2013, Hong Kong, China, IEEE Press, March 2013, PP 253-255.
24. S4, distributed stream computing platform. Available at http://incubator.apache.org/s4/ (accessed July 16, 2013).
25. Stream computing StreamBase Yahoo S4 borealis comparis. Available at http://oracle-abc.wikidot.com/zh:stream-computing-streambase-yahoo-s4-borealis-comparison (accessed July 16, 2013).
26. Guo Z Y, Sean M D, Yang M et al. Failure recovery: When the cure is worse than the disease, Proc. 14th USENIX conference on Hot Topics in Operating Systems, USENIX 2013, Santa Ana Pueblo, NM, ACM Press, May 2013, PP 1-6.
27. Ali M, Chandramouli B, Goldstein J et al. The extensibility framework in Microsoft StreamInsight, Proc. IEEE 27th International Conference on Data Engineering, ICDE 2011, Hannover, Germany, IEEE Press, April 2011, PP 1242-1253.
28. Chandramouli B, Goldstein J, Barga R et al. Accurate latency estimation in a distributed event processing system, Proc. IEEE 27th International Conference on Data Engineering, ICDE 2011, Hannover, Germany, IEEE Press, April 2011, PP 255-266.
29. Ali M, Chandramouli B, Fay J et al. Online visualization of geospatial stream data using the WorldWide telescope, VLDB Endowment, vol. 4(12), 2011, PP 1379-1382.
30. Derek G M, Frank M S, Rebecca I et al. Naiad: A timely dataflow system, Proc. the 24th ACM Symposium on Operating Systems Principles, SOSP 2013, Pennsylvania, ACM Press, November 2013, PP 439-455.
31. Garzo A, Benczur A A, Sidlo C I et al. Real-time streaming mobility analytics, Proc. 2013 IEEE International Conference on Big Data, Big Data 2013, Santa Clara, CA, IEEE Press, October 2013, PP 697-702.
32. Zaharia M, Das T, Li H et al. R Discretized streams: Fault-tolerant streaming computation at scale, Proc. the 24th ACM Symposium on Operating Systems Principles, SOSP 2013, Farmington, PA, ACM Press, November 2013, PP 423-438.