New landscape of data management technologies

Ruan Jian Xue Bao/Journal of Software

Volumn 24, Issue 2, 2013, Pages 175-197

  Qin, Xiong Pai ^a,b,c   Wang, Hui Ju ^a,b,c   Li, Fu Rong ^a,b,c   Li, Cui Ping ^a,b,c   Chen, Hong ^a,b,c   Zhou, Xuan ^a,b,c   Du, Xiao Yong ^a,b,c   Wang, Shan ^a,b,c  

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

^b Sa Shi Xuan Big Data Management and Analytics Research Center (Sino Australia)   (China)

^c RENMIN UNIVERSITY OF CHINA   (China)

Author keywords

Analytic; Big data; New landscape; NoSQL; Operational; RDBMS (relational database management system)

Indexed keywords

ANALYTIC; BIG DATUM; NEW LANDSCAPE; NOSQL; OPERATIONAL; RELATIONAL DATABASE MANAGEMENT SYSTEMS;

DATABASE SYSTEMS; INFORMATION MANAGEMENT; PARALLEL ARCHITECTURES; SCALABILITY; VIRTUAL STORAGE;

TECHNOLOGY;

EID: 84874579152     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1001.2013.04345     Document Type: Article

References (112)

1. Abadi DJ, Boncz PA, Harizopoulos S. Column-Oriented database systems. VLDB 2009 Tutorial, 2009. http://cs-www.cs.yale.edu/homes/dna/talks/ Column_Store_Tutorial_VLDB09.pdf
2. Datta A, Thomas H. Querying compressed data in data warehouses. Journal of Information Technology and Management, 2002, 3(4): 353-386. [doi: 10.1023/A:1019772807859]
3. Bhuiyan MM, Hoque ASML. High performance SQL queries on compressed relational database. Journal of Computers, 2009, 3(12): 1263-1274.
4. O'Connell SJ, Winterbottom N. Performing joins without decompression in a compressed database system. SIGMOD Record, 2003, 32(1): 6-11. [doi: 10.1145/640990.640991]
5. Olofson C. Feature: The database revolution. 2012. http://www.ibm.com/developerworks/data/library/dmmag/ DMMag_2011_Issue1/FeatureHistory/
6. Kallman R, Kimura H, Natkins J, Pavlo A, Rasin A, Zdonik S, Jones EPC, Madden S, Stonebraker M, Zhang Y, Hugg J, Abadi DJ. H-Store: A high-performance, distributed main memory transaction processing system. Proc. of the VLDB Endowment, 2008, 1(2): 1496-1499.
7. Lu HJ, Ng YY, Tian ZP. T-Tree or B-tree: Main memory database index structure revisited. In: Orlowska ME, ed. Proc. of the Australasian Database Conf. 2000. Canberra: IEEE Computer Society, 2000. 65-73. [doi: 10.1109/ADC.2000.819815]
8. Shatdal A, Kant C, Naughton JF. Cache conscious algorithms for relational query processing. In: Bocca JB, Jarke M, Zaniolo C, eds. Proc. of the VLDB'94. Chile: Morgan Kaufmann Publishers, 1994. 510-521.
9. Luan H, Du XY, Wang S. Cache-Conscious data cube computation on a modern processor. Journal of Computer Science and Technology, 2009, 24(4): 708-722.
10. Rao J, Ross KA. Cache conscious indexing for decision-support in main memory. In: Atkinson MP, Orlowska ME, Valduriez P, Zdonik SB, Brodie ML, eds. Proc. of the VLDB'99. Edinburgh: Morgan Kaufmann Publishers, 1999. 78-89.
11. He BS, Luo Q. Cache-Oblivious query processing. In: Hellerstein J, Stonebraker M, Weikum G, eds. Proc. of the CIDR 2007. Asilomar: CIDR Program Committee, 2007. 44-55.
12. He BS, Luo Q. Cache-Oblivious databases: Limitations and opportunities. ACM Trans. on Database Systems, 2008, 33(2): 1-42. [doi: 10.1145/1366102.1366105]
13. Bender MA, Farach-Colton M, Fineman JT, Fogel YR, Kuszmaul BC, Nelson J. Cache-Oblivious streaming B-trees. In: Gibbons PB, Scheideler C, eds. Proc. of the SPAA 2007. Munich: ACM Press, 2007. 81-92. [doi: 10.1145/1248377.1248393]
14. Pandis I, Tozün P, Johnson R, Ailamaki A. PLP: Page latch-free shared-everything OLTP. Proc. of the VLDB Endowment, 2011, 4(10): 610-621.
15. Lee RB, Ding XN, Chen F, Lu QD, Zhang XD. MCC-DB: Minimizing cache conflicts in multi-core processors for databases. Proc. of the VLDB Endowment, 2009, 2(1): 373-384.
16. Bakkum P, Skadron K. Accelerating SQL database operations on a GPU with CUDA. In: Kaeli DR, Leeser M, eds. Proc. of the GPGPU 2010. Pittsburgh: ACM Int'l Conf. Proc. Series, 2010. 94-103. [doi: 10.1145/1735688.1735706]
17. Lin CF, Yuan SM. The design and evaluation of GPU based memory database. In: Watada J, Chung PC, Ho KC, eds. Proc. of the Int'l Conf. on Genetic and Evolutionary Computing 2011. Kinmen, Xiamen: IEEE Computer Society, 2011. 224-231. [doi: 10.1109/ICGEC.2011.61]
18. Govindaraju NK, Gray J, Kumar R, Manocha D. GPUTeraSort: High performance graphics coprocessor sorting for large database management. In: Chaudhuri S, Hristidis V, Polyzotis N, eds. Proc. of the SIGMOD 2006. Chicago: ACM Press, 2006. 325-336. [doi: 10.1145/1142473.1142511]
19. He BS, Yu JX. High Throughput transaction executions on graphics processors. Proc. of the VLDB Endowment, 2011, 4(5): 314-325.
20. Volk PB, Habich D, Lehner W. GPU based speculative query processing for database operations. In: Bordawekar R, Lang CA, eds. Proc. of the VLDB Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures 2010. Singapore: VLDB Endowment, 2010. Article No.7.
21. Lee SW, Moon B. Design of flash-based DBMS: An in-page logging approach. In: Chan CY, Ooi BC, Zhou AY, eds. Proc. of the SIGMOD 2007. Beijing: ACM Press, 2007. 55-66. [doi: 10.1145/1247480.1247488]
22. Koltsidas I, Viglas SD. Flashing up the storage layer. Proc. of the VLDB Endowment, 2008, 1(1): 514-525.
23. Bonnet P, Bouganim L, Koltsidas I, Viglas SD. System co-design and data management for flash devices. VLDB 2011 Tutorial, 2011. http://www.vldb.org/pvldb/vol4/p1504-bonnet-tutorial2.pdf
24. Qin XP, Xiao YQ, Cao W, Wang S. A parallel recovery scheme for update intensive main memory database systems. In: Rountree N, ed. Proc. of the PDCAT 2008. Dunedin: IEEE Computer Society, 2008. 509-516. [doi: 10.1109/PDCAT.2008.69]
25. Lee J, Kim K, Cha SK. Differential logging: A commutative and associative logging scheme for highly parallel main memory database. In: Georgakopoulos D, Buchmann A, eds. Proc. of the ICDE 2001. Heidelberg: IEEE Computer Society, 2001. 173-182. [doi: 10.1109/ICDE.2001.914826]
26. Stonebraker. How to do complex analytics. 2012. http://www.slideshare.net/MassTLC/mike1
27. Ni MX, Luo WM. Technology revolution in the age of data exploding. CCF Communications, 2011, 7(7): 12-20 (in Chinese with English abstract).
28. Brewer EA. Towards robust distributed systems. PODC 2000 Keynote Speech, 2000. http://openstorage.gunadarma.ac.id/~mwiryana/ Kuliah/Database/PODC-keynote.pdf
29. Lynch N, Gilbert S. Brewer's conjecture and the feasibility of consistent, available, partition-tolerant Web services. ACM SIGACT News, 2002, 33(2): 51-59. [doi: 10.1145/564585.564601]
30. FAL Labs. Tokyo cabinet: A modern implementation of DBM. 2012. http://fallabs.com/tokyocabinet/
31. Citrusbyte. Redis database. 2012. http://redis.io/
32. Voldemort Team. Voldemort database. 2012. http://project-voldemort.com/
33. Oracle. Oracle Berkeley DB 11g. 2012. http://www.oracle.com/technetwork/products/ berkeleydb/overview/index.html
34. De Candia G, Hastorun D, Jampani M, Kakulapati G, Lakshman A, Pilchin A, Sivasubramanian S, Vosshall P, Vogels W. Dynamo: Amazon's highly available key-value store. ACM SIGOPS Operating Systems Review, 2007, 41(6): 205-220. [doi: 10.1145/1323293.1294281]
35. Apache Foundation. Apache Cassandra. 2012. http://cassandra.apache.org/
36. Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE. Bigtable: A distributed storage system for structured data. In: Bershad BN, Mogul JC, eds. Proc. of the OSDI 2006. Seattle: USENIX Association, 2006. 15-22.
37. Apache Foundation. Apache HBase. 2012. http://hbase.apache.org/
38. Borthakur D, Gray J, Sarma JS, Muthukkaruppan K, Spiegelberg N, Kuang HR, Ranganathan K, Molkov D, Menon A, Rash S, Schmidt R, Aiyer A. Apache hadoop goes realtime at Facebook. In: Sellis TK, Miller RJ, Kementsietsidis A, Velegrakis Y, eds. Proc. of the SIGMOD 2011. Athens: ACM Press, 2011. 1071-1080. [doi: 10.1145/1989323.1989438]
39. Apache Foundation. CouchDB. 2012. http://couchdb.apache.org/
40. MongoDB Team. MongDB. 2012. http://www.mongodb.org/
41. Riak Team. Riak database. 2012. http://basho.com/products/riak-overview/
42. Neo4j Team. Neo4j database. 2012. http://neo4j.org/
43. NetMesh. InforGrid Web graph database. 2012. http://infogrid.org/trac/
44. Objectivity. InfiniteGraph database. 2012. http://www.infinitegraph.com/
45. HyperGraphDB Team. HyperGraphDB. 2012. http://www.kobrix.com/hgdb.jsp
46. Marcus A. The NoSQL EcoSystem. 2012. http://www.aosabook.org/en/nosql.html
47. Baker J, Bond C, Corbett JC, Furman JJ, Khorlin A, Larson J, Leon JM, Li YW, Lloyd A, Yushprakh V. Megastore: Providing scalable, highly available storage for interactive services. In: Ailamaki A, Franklin M, Hellerstein J, eds. Proc. of the CIDR 2011. Asilomar: Online Proc., 2011. 223-234.
48. Das S, Agrawal D, Abbadi AE. G-Store: A scalable data store for transactional multi key access in the cloud. In: Hellerstein JM, Chaudhuri S, Rosenblum M, eds. Proc. of the SOCC 2010. Indianapolis: ACM Press, 2010. 163-174. [doi: 10.1145/1807128.1807157]
49. Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. In: Brewer E, Chen P, eds. Proc. of the OSDI 2004. San Francisco: USENIX Association, 2004. 137-150.
50. Kaldewey T, Shekita EJ, Tata S. Clydesdale: Structured data processing on MapReduce. In: Rundensteiner EA, Markl V, Manolescu I, Amer-Yahia S, Naumann F, Ari I, eds. Proc. of the EDBT 2012. Berlin: ACM Press, 2012. 15-25. [doi: 10.1145/2247596.2247600]
51. Jindal A, Quiane-Ruiz JA, Dittrich J. Trojan data layouts: Right shoes for a running elephant. In: Chase JS, Abbadi AE, Babu S, Romano P, eds. Proc. of the SOCC. Cascais: ACM Press, 2011. Article No.21. [doi: 10.1145/2038916.2038937]
52. Eltabakh MY, Tian YY, Ozcan F, Gemulla R, Krettek A, McPherson J. CoHadoop: Flexible data placement and its exploitation in Hadoop. Proc. of the VLDB Endowment, 2011, 4(9): 575-585.
53. Ananthanarayanan G, Agarwal S, Kandula S, Greenberg A, Stoica I, Harlan D, Harris E. Scarlett: Coping with skewed content popularity in mapreduce clusters. In: Kirsch CM, Heiser G, eds. Proc. of the EuroSys 2011. Salzburg: ACM Press, 2011. 287-300. [doi: 10.1145/1966445.1966472]
54. Ma Q, Yang B, Qian WN, Zhou AY. Query processing of massive trajectory data based on mapreduce. In: Meng XF, Wang HX, Chen Y, eds. Proc. of the CloudDB 2009. New York: ACM Press, 2009. 9-16. [doi: 10.1145/1651263.1651266]
55. Chandramouli B, Goldstein J, Duan SY. Temporal analytics on big data for Web advertising. In: Kementsietsidis A, Salles MAV, eds. Proc. of the ICDE 2012. Washington: IEEE Computer Society, 2012. 90-101. [doi: 10.1109/ICDE.2012.55]
56. White B, Yeh T, Lin J, Davis L. Web-Scale computer vision using MapReduce for multimedia data mining. In: Hua XS, Ngo CW, eds. Proc. of the KDD Workshop on Multimedia Data Mining 2010. New York: ACM Press, 2010. Article No.9. [doi: 10.1145/1814245.1814254]
57. Neumeyer L, Robbins B, Nair A, Kesari A. S4: Distributed stream computing platform. In: Fan W, Hsu W, Webb GI, Liu B, Zhang CQ, Gunopulos D, Wu XD, eds. Proc. of the ICDM Workshops 2010. Sydney: IEEE Computer Society, 2010. 170-177. [doi: 10.1109/ICDMW.2010.172]
58. Chen QM, Hsu M. Continuous mapreduce for In-DB stream analytics. In: Meersman R, Dillon TS, Herrero P, eds. Proc. of the OTM 2010. Hersonissos: Springer-Verlag, 2010. 16-34. [doi: 10.1007/978-3-642-16961-8_9]
59. Olston C, Chiou G, Chitnis L, Liu F, Han YP, Larsson M, Neumann A, Rao VBN, Sankarasubramanian V, Seth S, Tian C, ZiCornell T, Wang XD. Nova: Continuous Pig/Hadoop workflows. In: Sellis TK, Miller RJ, Kementsietsidis A, Velegrakis Y, eds. Proc. of the SIGMOD 2011. Athens: ACM Press, 2011. 1081-1090. [doi: 10.1145/1989323.1989439]
60. Bu YY, Howe B, Balazinska M, Ernst MD. HaLoop: Efficient iterative data processing on large clusters. Proc. of the VLDB Endowment, 2010, 3(1-2): 285-296.
61. Zhang SB, Han JZ, Liu ZY, Wang K, Feng SZ. Accelerating MapReduce with distributed memory cache. In: Pan Y, ed. Proc. of the ICPADS 2009. Shenzhen: IEEE, 2009. 472-478. [doi: 10.1109/ICPADS.2009.88]
62. 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, Özcan F, eds. Proc. of the EDBT 2010. Lausanne: ACM Int'l Conf. Proc. Series, 2010. 99-110. [doi: 10.1145/1739041.1739056]
63. Afrati FN, Ullman JD. Optimizing multiway joins in a Map-Reduce environment. IEEE Trans. on Knowledge and Data Engineering, 2011, 23(9): 1282-1298. [doi: 10.1109/TKDE.2011.47]
64. Afrati FN, Sarma AD, Menestrina D, Parameswaran A, Ullman JD. Fuzzy joins using MapReduce. Technical Report, #1006, Stanford: Stanford University, InfoLab, 2012.
65. 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 2010. Indianapolis: ACM Press, 2010. 975-986. [doi: 10.1145/1807167.1807273]
66. Okcan A, Riedewald M. Processing theta-joins using MapReduce. In: Sellis TK, Miller RJ, Kementsietsidis A, Velegrakis Y, eds. Proc. of the SIGMOD 2011. Athens: ACM Press, 2011. 949-960. [doi: 10.1145/1989323.1989423]
67. Nykiel T, Potamias M, Mishra C, Kollios G, Koudas N. MRShare: Sharing across multiple queries in MapReduce. Proc. of the VLDB Endowment, 2010, 3(1): 494-505.
68. Chu CT, Kim SK, Lin YA, Yu YY, Bradski G, Ng AY, Olukotun K. Map-Reduce for machine learning on multicore. In: Schölkopf B, Platt JC, Hoffman T, eds. Proc. of the NIPS 2006. Vancouver: MIT Press, 2006. 281-288.
69. Chen R, Chen HB, Zang BY. Tiled-MapReduce: Optimizing resource usages of data-parallel applications on multicore with tiling. In: Salapura V, Gschwind M, Knoop J, eds. Proc. of the PACT 2010. Vienna: ACM Press, 2010. 523-534. [doi: 10.1145/1854273.1854337]
70. 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 2008. Toronto: ACM Press, 2008. 260-269. [doi: 10.1145/1454115.1454152]
71. 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 2010. Vienna: ACM Press, 2010. 217-226. [doi: 10.1145/1854273.1854303]
72. Polo J, Carrera D, Becerra Y, Beltran V, Torres J, Ayguade E. Performance management of accelerated MapReduce workloads in heterogeneous clusters. In: Qin F, ed. Proc. of the ICPP 2010. San Diego: IEEE Computer Society, 2010. 653-662. [doi: 10.1109/ICPP.2010.73]
73. Papagiannis A, Nikolopoulos DS. Rearchitecting MapReduce for heterogeneous multicore processors with explicitly managed memories. In: Qin F, ed. Proc. of the ICPP 2010. San Diego: IEEE Computer Society, 2010. 121-130. [doi: 10.1109/ICPP.2010.21]
74. You HH, Yang CC, Huang JL. A load-aware scheduler for MapReduce framework in heterogeneous cloud environments. In: Chu WC, Wong WE, Palakal MJ, Hung CC, eds. Proc. of the SAC 2011. Taichung: ACM Press, 2011. 127-132. [doi: 10.1145/1982185.1982218]
75. Zaharia M, Borthakur D, Sarma JS, Elmeleegy K, Shenker S, Stoica I. Delay scheduling: A simple technique for achieving locality and fairness in cluster scheduling. In: Morin C, Muller G, eds. Proc. of the EuroSYS 2010. Paris: ACM Press, 2010. 265-278.
76. Chattopadhyay B, Lin L, Liu WR, Mittal S, Aragonda P, Lychagina V, Kwon YH, Wong M. Tenzing a SQL implementation on the MapReduce framework. Proc. of the VLDB Endowment, 2011, 4(4): 1318-1327.
77. 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. Proc. of the VLDB Endowment, 2009, 2(2): 938-941.
78. 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 2008. Vancouver: ACM Press, 2008. 1099-1110. [doi: 10.1145/1376616.1376726]
79. Ghoting A, Krishnamurthy R, Pednault E, Reinwald B, Sindhwani V, Tatikonda S, Tian YY, Vaithyanathan S. SystemML: Declarative machine learning on MapReduce. In: Abiteboul S, Böhm K, Koch C, Tan KL, eds. Proc. of the ICDE 2011. Hannover: IEEE Computer Society, 2011. 231-242. [doi: 10.1109/ICDE.2011.5767930]
80. Apache Foundation. Mahout. 2012. http://mahout.apache.org/
81. Roy I, Ramadan HE, Setty STV, Kilzer A, Shmatikov V, Witchel E. Airavat: Security and privacy for MapReduce. In: Castro M, Snoeren AC, eds. Proc. of the NSDI 2010. San Jose: USENIX Association, 2010. 297-312.
82. Lang W, Patel JM. Energy management for MapReduce clusters. Proc. of the VLDB Endowment, 2010, 3(1-2): 129-139.
83. Qin XP, Wang HJ, Du XY, Wang S. Big data analysis-Competition and symbiosis of RDBMS and MapReduce. Ruanjian Xuebao/Journal of Software, 2012, 23(1): 32-45 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4091.htm [doi: 10.3724/SP.J.1001.2012.04091]
84. Lee KH, Lee YJ, Choi H, Chung YD, Moon BK. Parallel data processing with MapReduce: A survey. SIGMOD Record, 2011, 40(4): 11-20. [doi: 10.1145/2094114.2094118]
85. He YQ, Lee RB, Huai Y, Shao Z, Jain N, Zhang XD, Xu ZW. RCFile: A fast and space-efficient data placement structure in MapReduce-based warehouse systems. In: Abiteboul S, Böhm K, Koch C, Tan KL, eds. Proc. of the ICDE 2011. Hannover: IEEE Computer Society, 2011. 1199-1208. [doi: 10.1109/ICDE.2011.5767933]
86. Floratou A, Patel JM, Shekita EJ, Tata S. Column-Oriented storage techniques for MapReduce. Proc. of the VLDB Endowment, 2011, 4(7): 419-429.
87. Li BD, Mazur E, Diao YL, McGregor A, Shenoy P. A platform for scalable one-pass analytics using MapReduce. In: Sellis TK, Miller RJ, Kementsietsidis A, Velegrakis Y, eds. Proc. of the SIGMOD 2011. Athens: ACM Press, 2011. 985-996. [doi: 10.1145/1989323.1989426]
88. Abelló A, Ferrarons J, Romero O. Building cubes with MapReduce. In: Song IY, Ordonez C, eds. Proc. of the DOLAP 2010. New York: ACM Press, 2010. 17-24. [doi: 10.1145/2064676.2064680]
89. Bose JH, Andrzejak A, Hogqvist M. Beyond online aggregation: Parallel and incremental data mining with online Map-Reduce. In: Nambiar U, McPherson J, Konopnicki D, eds. Proc. of the WWW Workshop on Massive Data Analytics on the Cloud 2010. New York: ACM Press, 2010. Article No.3. [doi: 10.1145/1779599.1779602]
90. Omalley O. See what Yahoo! and Jeopardy! have in common. 2012. http://developer.yahoo.com/blogs/hadoop/posts/2011/02/ i%E2%80%99ll-take-hadoop-for-400-alex/
91. 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 2010. Tsukuba: Springer-Verlag, 2010. 386-397. [doi: 10.1007/978-3-642-14589-6_40]
92. Lin J, Dyer C. Data-Intensive Text Processing with MapReduce. San Rafael: Morgan and Claypool Publishers, 2010. 40-56.
93. Wiley K, Connolly A, Gardner JP, Krughof S, Balazinska M, Howe B, Kwon YC, Bu YY. Astronomy in the cloud: Using MapReduce for image coaddition. Publications of the Astronomical Society of the Pacific, 2011, 123(901): 366-380. [doi: 10.1086/658877]
94. Bahmani B, Kumar R, Vassilvitskii S. Densest subgraph in streaming and MapReduce. Proc. of the VLDB Endowment, 2012, 5(5): 454-465.
95. Gray J, Liu DT, Nieto-Santisteban M, Szalay A, DeWitt DJ, Heber G. Scientific data management in the coming decade. SIGMOD Record, 2005, 34(4): 34-41. [doi: 10.1145/1107499.1107503]
96. Xu Y, Kostamaa P, Gao LK. Integrating Hadoop and parallel DBMS. In: Elmagarmid AK, Agrawal D, eds. Proc. of the SIGMOD 2010. Indianapolis: ACM Press, 2010. 969-974. [doi: 10.1145/1807167.1807272]
97. Abouzeid A, Bajda-Pawlikowski K, Abadi D, Silberschatz A, Rasin A. HadoopDB: An architectural hybrid of MapReduce and DBMS technologies for analytical workloads. Proc. of the VLDB Endowment, 2009, 2(1): 922-933.
98. 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 2010. Indianapolis: ACM Press, 2010. 1111-1114. [doi: 10.1145/1807167.1807294]
99. Bajda-Pawlikowski K, Abadi DJ, Silberschatz A, Paulson E. Efficient processing of data warehousing queries in a split execution environment. In: Sellis TK, Miller RJ, Kementsietsidis A, Velegrakis Y, eds. Proc. of the SIGMOD 2011. Athens: ACM Press, 2011. 1165-1176. [doi: 10.1145/1989323.1989447]
100. Pavlo A, Curino C, Zdonik S. Skew-Aware automatic database partitioning in shared-nothing, parallel OLTP systems. In: Candan KS, Chen Y, Snodgrass RT, Gravano L, Fuxman A, eds. Proc. of the SIMGOD 2012. Scottsdale: ACM Press, 2012. 61-72. [doi: 10.1145/2213836.2213844]
101. Cao Y, Chen C, Guo F, Jiang DW, Lin YT, Ooi BC, Vo HT, Wu S, Xu QQ. ES2: A cloud data storage system for supporting both OLTP and OLAP. In: Abiteboul S, Böhm K, Koch C, Tan KL, eds. Proc. of the ICDE 2011. Hannover: IEEE Computer Society, 2011. 291-302. [doi: 10.1109/ICDE.2011.5767881]
102. Kemper A, Neumann T. HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots. In: Abiteboul S, Böhm K, Koch C, Tan KL, eds. Proc. of the ICDE 2011. Hannover: IEEE Computer Society, 2011. 195-206. [doi: 10.1109/ICDE.2011.5767867]
103. Lin ZY, Lai YX, Lin C, Xie Y, Zou Q. Research on cloud databases. Ruanjian Xuebao/Journal of Software, 2012, 23(5):1148-1166 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4195.htm [doi: 10.3724/SP.J.1001.2012.04195]
104. Wang YJ, Sun WD, Zhou S, Pei XQ, Li XY. Key technologies of distributed storage for cloud computing. Ruanjian Xuebao/Journal of Software, 2012, 23(4): 962-986 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4175.htm [doi: 10.3724/SP.J.1001.2012.04175]
105. Zheng P, Cui LZ, Wang HY, Xu M. A data placement strategy for data intensive applications in cloud. Chinese Journal of Computers, 2010, 33(8): 1472-1480 (in Chinese with English abstract). [doi: 10.3724/SP.J.1016.2010.01472]
106. CompuGreen. Green 500 list. 2012. http://www.green500.org/
107. Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauley M, Franklin MJ, Shenker S, Stoica I. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In: Gribble S, Katabi D, eds. Proc. of the NSDI 2012. San Jose: USENIX, 2012. 2-15.
108. Zhou GL, Chen H. Parallel cube computation on modern CPUs and GPUs. The Journal of Supercomputing, 2012, 61(3): 394-417. [doi: 10.1007/s11227-011-0575-7]
109. He GM, Feng HJ, Li CP, Chen H. Parallel SimRank computation on large graphs with iterative aggregation. In: Rao B, Krishnapuram B, Tomkins A, Yang Q, eds. Proc. of the SIGKDD 2010. Washington: ACM Press, 2010. 543-552. [doi: 10.1145/1835804.1835874]
110. Li CP, Han JW, He GM, Jin X, Sun YZ, Yu YT, Wu TY. Fast computation of SimRank for static and dynamic information networks. In: Manolescu I, Spaccapietra S, Teubner J, Kitsuregawa M, Léger A, Naumann F, Ailamaki A, Özcan F, eds. Proc. of the EDBT 2010. Lausanne: ACM, 2010. 465-476. [doi: 10.1145/1739041.1739098]
111. Wang H, Qin X, Zhang Y, Wang S, Wang Z. LinearDB: A relational approach to make data warehouse scale like MapReduce. In: Yu JX, Kim MH, Unland R, eds. Proc. of the DASFAA 2011. Hong Kong: Springer-Verlag, 2011. 306-320. [doi: 10.1007/978-3-642-20152-3_23]
112. Bloor R. The coming database revolution. 2012. http://www.slideshare.net/Dataversity/thu-0830-bloorrobincolor