Big data analysis-competition and symbiosis of RDBMS and MapReduce

Ruan Jian Xue Bao/Journal of Software

Volumn 23, Issue 1, 2012, Pages 32-45

  Qin, Xiong Pai ^a,b   Wang, Hui Ju ^a,b   Du, Xiao Yong ^a,b   Wang, Shan ^a,b  

^a Beijing Key Laboratory of Big Data Management and Analysis Methods   (China)

^b RENMIN UNIVERSITY OF CHINA   (China)

Author keywords

Big data; Deep analysis; MapReduce; Relational data management technique

Indexed keywords

BIG DATA; DEEP ANALYSIS; HIGH AVAILABILITY; HIGH SCALABILITIES; LARGE DATA; MAP-REDUCE; PARALLEL PROCESSING; PARALLEL TECHNIQUES; RELATIONAL DATA; RELATIONAL TECHNIQUES; WEB SEARCHES;

INDUSTRIAL MANAGEMENT; INFORMATION RETRIEVAL; RELATIONAL DATABASE SYSTEMS; SCALABILITY; WORLD WIDE WEB;

INFORMATION MANAGEMENT;

EID: 84863176940     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1001.2012.04091     Document Type: Article

References (82)

1. Zhou AY. Data intensive computing-challenges of data management techniques. Communications of CCF, 2009, 5(7): 50-53 (in Chinese with English abstract).
2. Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C. MAD skills: New analysis practices for big data. PVLDB, 2009, 2(2): 1481-1492.
3. Schroeder B, Gibson GA. Understanding failures in petascale computers. Journal of Physics: Conf. Series, 2007, 78(1): 1-11. [doi:10.1088/1742-6596/78/1/012022]
4. Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. In: Brewer E, Chen P, eds. Proc. of the OSDI. California: USENIX Association, 2004. 137-150. [doi:10.1145/1327452.1327492]
5. Pavlo A, Paulson E, Rasin A, Abadi DJ, Dewitt DJ, Madden S, Stonebraker M. A comparison of approaches to large-scale data analysis. In: Cetintemel U, Zdonik SB, Kossmann D, Tatbul N, eds. Proc. of the SIGMOD. Rhode Island: ACM Press, 2009. 165-178. [doi:10.1145/1559845.1559865]
6. Chu CT, Kim SK, Lin YA, Yu YY, Bradski G, Ng AY, Olukotun K. Map-Reduce for machine learning on multicore. In: Scholkopf B, Platt JC, Hoffman T, eds. Proc. of the NIPS. Vancouver: MIT Press, 2006. 281-288. [doi:10.1234/12345678]
7. Wang CK, Wang JM, Lin XM, Wang W, Wang HX, Li HS, Tian WP, Xu J, Li R. MapDupReducer: Detecting near duplicates over massive datasets. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 1119-1122. [doi:10.1145/1807167.1807296]
8. Liu C, Guo F, Faloutsos C. BBM: Bayesian browsing model from petabyte-scale data. In: Elder JF IV, Fogelman-Soulié F, Flach PA, Zaki MJ, eds. Proc. of the KDD. Paris: ACM Press, 2009. 537-546. [doi:10.1145/1557019.1557081]
9. Panda B, Herbach JS, Basu S, Bayardo RJ. PLANET: Massively parallel learning of tree ensembles with MapReduce. PVLDB, 2009, 2(2): 1426-1437.
10. Lin J, Schatz M. Design patterns for efficient graph algorithms in MapReduce. In: Rao B, Krishnapuram B, Tomkins A, Yang Q, eds. Proc. of the KDD. Washington: ACM Press, 2010. 78-85. [doi:10.1145/1830252.1830263]
11. Zhang CJ, Ma Q, Wang XL, Zhou AY. Distributed SLCA-based XML keyword search by Map-Reduce. In: Yoshikawa M, Meng XF, Yumoto T, Ma Q, Sun LF, Watanabe C, eds. Proc. of the DASFAA. Tsukuba: Springer-Verlag, 2010. 386-397. [doi:10.1007/ 978-3-642-14589-6_40]
12. Stupar A, Michel S, Schenkel R. RankReduce-Processing K-nearest neighbor queries on top of MapReduce. In: Crestani F, Marchand-Maillet S, Chen HH, Efthimiadis EN, Savoy J, eds. Proc. of the SIGIR. Geneva: ACM Press, 2010. 13-18.
13. Wang GZ, Salles MV, Sowell B, Wang X, Cao T, Demers A, Gehrke J, White W. Behavioral simulations in MapReduce. PVLDB, 2010, 3(1-2): 952-963.
14. Gunarathne T, Wu TL, Qiu J, Fox G. Cloud computing paradigms for pleasingly parallel biomedical applications. In: Hariri S, Keahey K, eds. Proc. of the HPDC. Chicago: ACM Press, 2010. 460-469. [doi:10.1145/1851476.1851544]
15. Delmerico JA, Byrnesy NA, Brunoz AE, Jonesz MD, Galloz SM, Chaudhary V. Comparing the performance of clusters, Hadoop, and active disks on microarray correlation computations. In: Yang YY, Parashar M, Muralidhar R, Prasanna VK, eds. Proc. of the HiPC. Kochi: IEEE Press, 2009. 378-387. [doi:10.1109/HIPC.2009.5433190]
16. Das S, Sismanis Y, Beyer KS, Gemulla R, Haas PJ, McPherson J. Ricardo: Integrating R and Hadoop. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 987-998. [doi:10.1145/1807167.1807275]
17. Wegener D, Mock M, Adranale D, Wrobel S. Toolkit-Based high-performance data mining of large data on MapReduce clusters. In: Saygin Y, Yu JX, Kargupta H, Wang W, Ranka S, Yu PS, Wu XD, eds. Proc. of the ICDM Workshop. Washington: IEEE Computer Society, 2009. 296-301. [doi:10.1109/ICDMW.2009.34]
18. Kovoor G, Singer J, Luján M. Building a Java Map-Reduce framework for multi-core architectures. In: Ayguade E, Gioiosa R, Stenstrom P, Unsal O, eds. Proc. of the HiPEAC. Pisa: HiPEAC Endowment, 2010. 87-98.
19. De Kruijf M, Sankaralingam K. MapReduce for the cell broadband engine architecture. IBM Journal of Research and Development, 2009, 53(5): 1-12. [doi:10.1147/JRD.2009.5429076]
20. Becerra Y, Beltran V, Carrera D, Gonzalez M, Torres J, Ayguade E. Speeding up distributed MapReduce applications using hardware accelerators. In: Barolli L, Feng WC, eds. Proc. of the ICPP. Vienna: IEEE Computer Society, 2009. 42-49. [doi:10.1109/ICPP.2009.59]
21. Ranger C, Raghuraman R, Penmetsa A, Bradski G, Kozyrakis C. Evaluating MapReduce for multi-core and multiprocessor systems. In: Dally WJ, ed. Proc. of the HPCA. Phoenix: IEEE Computer Society, 2007. 13-24. [doi:10.1109/HPCA.2007.346181]
22. Ma WJ, Agrawal G. A translation system for enabling data mining applications on GPUs. In: Zhou P, ed. Proc. of the Supercomputing (SC). New York: ACM Press, 2009. 400-409. [doi:10.1145/1542275.1542331]
23. He BS, Fang WB, Govindaraju NK, Luo Q, Wang TY. Mars: A MapReduce framework on graphics processors. In: Moshovos A, Tarditi D, Olukotun K, eds. Proc. of the PACT. Ontario: ACM Press, 2008. 260-269.
24. Stuart JA, Chen CK, Ma KL, Owens JD. Multi-GPU volume rendering using MapReduce. In: Hariri S, Keahey K, eds. Proc. of the MapReduce Workshop (HPDC 2010). New York: ACM Press, 2010. 841-848. [doi:10.1145/1851476.1851597]
25. Hong CT, Chen DH, Chen WG, Zheng WM, Lin HB. MapCG: Writing parallel program portable between CPU and GPU. In: Salapura V, Gschwind M, Knoop J, eds. Proc. of the PACT. Vienna: ACM Press, 2010. 217-226. [doi:10.1145/1854273.1854303]
26. Jiang W, Ravi VT, Agrawal G. A Map-Reduce system with an alternate API for multi-core environments. In: Chiba T, ed. Proc. of the CCGRID. Melbourne: IEEE Press, 2010. 84-93. [doi:10.1109/CCGRID.2010.10]
27. Liao HJ, Han JZ, Fang JY. Multi-Dimensional index on Hadoop distributed file system. In: Xu ZW, ed. Proc. of the Networking, Architecture, and Storage (NAS). Macau: IEEE Computer Society, 2010. 240-249. [doi:10.1109/NAS.2010.44]
28. Zou YQ, Liu J, Wang SC, Zha L, Xu ZW. CCIndex: A complemental clustering index on distributed ordered tables for multi-dimensional range queries. In: Ding C, Shao ZY, Zheng R, eds. Proc. of the NPC. Zhengzhou: Springer-Verlag, 2010. 247-261. [doi:10.1007/978-3-642-15672-4_22]
29. Zhang SB, Han JZ, Liu ZY, Wang K, Feng SZ. Accelerating MapReduce with distributed memory cache. In: Huang XX, ed. Proc. of the ICPADS. Shenzhen: IEEE Press, 2009. 472-478. [doi:10.1109/ICPADS.2009.88]
30. Dittrich J, Quiané-Ruiz JA, Jindal A, Kargin Y, Setty V, Schad J. Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing). PVLDB, 2010, 3(1-2): 518-529.
31. Chen ST. Cheetah: A high performance, custom data warehouse on top of MapReduce. PVLDB, 2010, 3(1-2): 1459-1468.
32. Iu MY, Zwaenepoel W. HadoopToSQL: A MapReduce query optimizer. In: Morin C, Muller G, eds. Proc. of the EuroSys. Paris: ACM Press, 2010. 251-264. [doi:10.1145/1755913.1755939]
33. Blanas S, Patel JM, Ercegovac V, Rao J, Shekita EJ, Tian YY. A comparison of join algorithms for log processing in MapReduce. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 975-986. [doi:10.1145/1807167.1807273]
34. Zhou MQ, Zhang R, Zeng DD, Qian WN, Zhou AY. Join optimization in the MapReduce environment for column-wise data store. In: Fang YF, Huang ZX, eds. Proc. of the SKG. Ningbo: IEEE Computer Society, 2010. 97-104. [doi:10.1109/SKG.2010.18]
35. Afrati FN, Ullman JD. Optimizing joins in a Map-Reduce environment. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 99-110. [doi:10.1145/1739041.1739056]
36. Sandholm T, Lai K. MapReduce optimization using regulated dynamic prioritization. In: Douceur JR, Greenberg AG, Bonald T, Nieh J, eds. Proc. of the SIGMETRICS. Seattle: ACM Press, 2009. 299-310. [doi:10.1145/1555349.1555384]
37. Hoefler T, Lumsdaine A, Dongarra J. Towards efficient MapReduce using MPI. In: Oster P, ed. Proc. of the EuroPVM/MPI. Berlin: Springer-Verlag, 2009. 240-249. [doi:10.1007/978-3-642-03770-2_30]
38. Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. PVLDB, 2010, 3(1-2): 494-505.
39. Kambatla K, Rapolu N, Jagannathan S, Grama A. Asynchronous algorithms in MapReduce. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 245-254. [doi:10.1109/CLUSTER.2010.30]
40. Polo J, Carrera D, Becerra Y, Torres J, Ayguadé E, Steinder M, Whalley I. Performance-Driven task co-scheduling for MapReduce environments. In: Tonouchi T, Kim MS, eds. Proc. of the IEEE Network Operations and Management Symp. (NOMS). Osaka: IEEE Press, 2010. 373-380. [doi:10.1109/NOMS.2010.5488494]
41. Zaharia M, Konwinski A, Joseph AD, Katz R, Stoica I. Improving MapReduce performance in heterogeneous environments. In: Draves R, van Renesse R, eds. Proc. of the ODSI. Berkeley: USENIX Association, 2008. 29-42.
42. Xie J, Yin S, Ruan XJ, Ding ZY, Tian Y, Majors J, Manzanares A, Qin X. Improving MapReduce performance through data placement in heterogeneous Hadoop clusters. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshop on Heterogeneity in Computing (IPDPS 2010). Atlanta: IEEE Press, 2010. 1-9. [doi:10.1109/IPDPSW.2010.5470880]
43. Polo J, Carrera D, Becerra Y, Beltran V, Torres J, Ayguadé E. Performance management of accelerated MapReduce workloads in heterogeneous clusters. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 653-662. [doi:10.1109/ ICPP.2010.73]
44. Papagiannis A, Nikolopoulos DS. Rearchitecting MapReduce for heterogeneous multicore processors with explicitly managed memories. In: Qin F, Barolli L, Cho SY, eds. Proc. of the ICPP. San Diego: IEEE Press, 2010. 121-130. [doi:10.1109/ICPP. 2010.21]
45. Jiang DW, Ooi BC, Shi L, Wu S. The performance of MapReduce: An in-depth study. PVLDB, 2010, 3(1-2): 472-483.
46. Berthold J, Dieterle M, Loogen R. Implementing parallel Google Map-Reduce in Eden. In: Sips HJ, Epema DHJ, Lin HX, eds. Proc. of the Euro-Par. Delft: Springer-Verlag, 2009. 990-1002. [doi:10.1007/978-3-642-03869-3_91]
47. Verma A, Zea N, Cho B, Gupta I, Campbell RH. Breaking the MapReduce stage barrier. In: Moreira JE, Matsuoka S, Pakin S, Cortes T, eds. Proc. of the CLUSTER. Crete: IEEE Press, 2010. 235-244. [doi:10.1109/CLUSTER.2010.29]
48. Yang HC, Dasdan A, Hsiao RL, Parker DS. Map-Reduce-Merge simplified relational data processing on large clusters. In: Chan CY, Ooi BC, Zhou AY, eds. Proc. of the SIGMOD. Beijing: ACM Press, 2007. 1029-1040. [doi:10.1145/1247480.1247602]
49. Seo SW, Jang I, Woo KC, Kim I, Kim JS, Maeng S. HPMR: Prefetching and pre-shuffling in shared MapReduce computation environment. In: Rana O, Tang FL, Kosar T, eds. Proc. of the CLUSTER. New Orleans: IEEE Press, 2009. 1-8. [doi:10.1109/ CLUSTR.2009.5289171]
50. Babu S. Towards automatic optimization of MapReduce programs. In: Kansal A, ed. Proc. of the ACM Symp. on Cloud Computing (SoCC). New York: ACM Press, 2010. 137-142. [doi:10.1145/1807128.1807150]
51. Olston C, Reed B, Srivastava U, Kumar R, Tomkins A. Pig Latin: A not-so-foreign language for data processing. In: Wang JTL, ed. Proc. of the SIGMOD. Vancouver: ACM Press, 2008. 1099-1110. [doi:10.1145/1376616.1376726]
52. Isard M, Budiu M, Yu Y, Birrell A, Fetterly D. Dryad: Distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operating Systems Review, 2007, 41(3): 59-72. [doi:10.1145/1272996.1273005]
53. Isard M, Yu Y. Distributed data-parallel computing using a high-level programming language. In: Cetintemel U, Zdonik SB, Kossmann D, Tatbul N, eds. Proc. of the SIGMOD. Rhode Island: ACM Press, 2009. 987-994. [doi:10.1145/1559845.1559962]
54. Chaiken R, Jenkins B, Larson PÅ, Ramsey B, Shakib D, Weaver S, Zhou JR. SCOPE: Easy and efficient parallel processing of massive data sets. PVLDB, 2008, 1(2): 1265-1276. [doi:10.1145/1454159.1454166]
55. Condie T, Conway N, Alvaro P, Hellerstein JM, Gerth J, Talbot J, Elmeleegy K, Sears R. Online aggregation and continuous query support in MapReduce. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 1115-1118. [doi:10.1145/1807167.1807295]
56. Thusoo A, Sarma JS, Jain N, Shao Z, Chakka P, Anthony S, Liu H, Wyckoff P, Murthy R. Hive a warehousing solution over a MapReduce framework. PVLDB, 2009, 2(2): 938-941.
57. Ghoting A, Pednault E. Hadoop-ML: An infrastructure for the rapid implementation of parallel reusable analytics. In: Culotta A, ed. Proc. of the Large-Scale Machine Learning: Parallelism and Massive Datasets Workshop (NIPS 2009). Vancouver: MIT Press, 2009. 6.
58. Yang C, Yen C, Tan C, Madden SR. Osprey: Implementing MapReduce-style fault tolerance in a shared-nothing distributed database. In: Li FF, Moro MM, Ghandeharizadeh S, Haritsa JR, Weikum G, Carey MJ, Casati F, Chang EY, Manolescu I, Mehrotra S, Dayal U, Tsotras VJ, eds. Proc. of the ICDE. Long Beach: IEEE Press, 2010. 657-668. [doi:10.1109/ICDE.2010.5447913]
59. Abouzeid A, Bajda-Pawlikowski K, Abadi DJ, Silberschatz A, Rasin A. HadoopDB: An architectural hybrid of MapReduce and DBMS technologes for analytical workloads. PVLDB, 2009, 2(1): 922-933.
60. Abouzied A, Bajda-Pawlikowski K, Huang JW, Abadi DJ, Silberschatz A. HadoopDB in action: Building real world applications. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indiana: ACM Press, 2010. 1111-1114. [doi:10.1145/1807167.1807294]
61. Friedman E, Pawlowski P, Cieslewicz J. SQL/MapReduce: A practical approach to self describing, polymorphic, and parallelizable user defined functions. PVLDB, 2009, 2(2): 1402-1413.
62. Stonebraker M, Abadi D, deWitt DJ, Maden S, Paulson E, Pavlo A, Rasin A. MapReduce and parallel DBMSs: Friends or foes? Communications of the ACM, 2010, 53(1): 64-71. [doi:10.1145/1629175.1629197]
63. Dean J, Ghemawat S. MapReduce: A flexible data processing tool. Communications of ACM, 2010, 53(1): 72-77. [doi:10.1145/ 1629175.1629198]
64. Xu Y, Kostamaa P, Gao LK. Integrating hadoop and parallel DBMS. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 969-974. [doi:10.1145/1807167.1807272]
65. Thusoo A, Shao Z, Anthony S, Borthakur D, Jain N, Sarma JS, Murthy R, Liu H. Data warehousing and analytics infrastructure at facebook. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 1013-1020.
66. Mcnabb AW, Monson CK, Seppi KD. MRPSO: MapReduce particle swarm optimization. In: Ryan C, Keijzer M, eds. Proc. of the GECCO. Atlanta: ACM Press, 2007. 177-185. [doi:10.1145/1276958.1276991]
67. Kang U, Tsourakakis CE, Faloutsos C. PEGASUS: A peta-scale graph mining system-Implementation and observations. In: Wang W, Kargupta H, Ranka S, Yu PS, Wu XD, eds. Proc. of the ICDM. Miami: IEEE Computer Society, 2009. 229-238. [doi:10.1109/ ICDM.2009.14]
68. Kang S, Bader DA. Large scale complex network analysis using the hybrid combination of a MapReduce cluster and a highly multithreaded system. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshops and Phd Forum (IPDPS 2010). Atlanta: IEEE Presss, 2010. 11-19. [doi:10.1109/IPDPSW.2010.5470691]
69. Logothetis D, Yocum K. AdHoc data processing in the cloud. PVLDB, 2008, 1(1): 1472-1475. [doi:10.1145/1454159.1454204]
70. Olston C, Bortnikov E, Elmeleegy K, Junqueira F, Reed B. Interactive analysis of WebScale data. In: DeWitt D, ed. Proc. of the CIDR. Asilomar: 2009. https://database.cs.wisc.edu/cidr/cidr2009/Paper_21.pdf
71. Böse JH, Andrzejak A, Högqvist M. Beyond online aggregation: Parallel and incremental data mining with online Map-Reduce. In: Tanaka K, Zhou XF, Zhang M, Jatowt A, eds. Proc. of the Workshop on Massive Data Analytics on the Cloud (WWW 2010). Raleigh: ACM Press, 2010. 3. [doi:10.1145/1779599.1779602]
72. Kumar V, Andrade H, Gedik B, Wu KL. DEDUCE: At the intersection of MapReduce and stream processing. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Ozcan F, eds. Proc. of the EDBT. Lausanne: ACM Press, 2010. 657-662. [doi:10.1145/1739041.1739120]
73. Abramson D, Dinh MN, Kurniawan D, Moench B, de Rose L. Data centric highly parallel debugging. In: Hariri S, Keahey K, eds. Proc. of the HPDC. Chicago: ACM Press, 2010. 119-129. [doi:10.1145/1851476.1851491]
74. Morton K, Friesen A, Balazinska M, Grossman D. Estimating the progress of MapReduce pipelines. In: Li FF, Moro MM, Ghandeharizadeh S, et al., eds. Proc. of the ICDE. Long Beach: IEEE Press, 2010. 681-684. [doi:10.1109/ICDE.2010.5447919]
75. Morton K, Balazinska M, Grossman D. ParaTimer: A progress indicator for MapReduce DAGs. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD. Indianapolis: ACM Press, 2010. 507-518. [doi:10.1145/1807167.1807223]
76. Lang W, Patel JM. Energy management for MapReduce clusters. PVLDB, 2010, 3(1-2): 129-139.
77. Wieder A, Bhatotia P, Post A, Rodrigues R. Brief announcement: Modelling MapReduce for optimal execution in the cloud. In: Richa AW, Guerraoui R, eds. Proc. of the PODC. Zurich: ACM Press, 2010. 408-409.
78. Zheng Q. Improving MapReduce fault tolerance in the cloud. In: Taufer M, Rünger G, Du ZH, eds. Proc. of the Workshops and PhD Forum (IPDPS 2010). Atlanta: IEEE Presss, 2010. 1-6. [doi:10.1109/IPDPSW.2010.5470865]
79. Groot S. Jumbo: Beyond MapReduce for workload balancing. In: Mylopoulos J, Zhou LZ, Zhou XF, eds. Proc. of the PhD Workshop (VLDB 2010). Singapore: VLDB Endowment, 2010. 7-12.
80. Chatziantoniou D, Tzortzakakis E. ASSET queries: A declarative alternative to MapReduce. SIGMOD Record, 2009, 38(2): 35-41. [doi:10.1145/1815918.1815926]
81. Bu YY, Howe B, Balazinska M, Ernst MD. HaLoop: Efficient iterative data processing on large clusters. PVLDB, 2010, 3(1-2): 285-296.
82. Wang HJ, Qin XP, Zhang YS, Wang S, Wang ZW. LinearDB: A relational approach to make data warehouse scale like MapReduce. In: Yu JX, Kim MH, Unland R, eds. Proc. of the DASFAA. Hong Kong: Springer-Verlag, 2011. 306-320. [doi:10.1007/978-3-642-20152-3_23]