Coulon, C., Pacitti, E., and Valduriez, P., “Consistency Management for Partial Replicationin a High Performance Database Cluster”, Proceedings of International Conference on Parallel an
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Salvadores, M., Herrero, P., Pérez, M.S., and Robles, V., “DCP-Grid, a Framework for
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Inter-Tang, F., Li, M., and Cao, J., “A Transaction Model for Grid Computing”, Proceedings of Advanced Parallel Programming Technologies (APPT), pp 382 –386, 2003.
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Informa-CHAPTERS 13 AND 14: GRID DATA REPLICATION
Carman, M., Zini, F., Serafini, L., and Stockinger, K., “Towards an Economy-Based
Optimi-sation of File Access and Replication on a Data Grid”, Proceedings of Cluster Computing and the Grid (CCGRID), pp 340 –345, 2002.
Chakrabarti, A., Dheepak, R.A., and Sengupta, S., “Integration of Scheduling and
Replica-tion in Data Grids”, Proceedings of High Performance Computing (HiPC), pp 375 –385,
2004
Chen, C and Cheng, C.T., “Replication and retrieval strategies of multidimensional data on
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in a High Performance Database Cluster”, Proceedings of International Conference on Parallel and Distributed Systems (ICPADS), pp 809 –815, 2005.
Dullmann, D., Hosckek, W., Jaen-Martinez, J., Segal, B., Samar, A., Stockinger, H.,and Stockinger, K., “Models for Replica Synchronisation and Consistency in a Data
Grid”, Proceedings of 10th IEEE International Symposium on High Performance and Distributed Computing (HPDC), pp 67 –75, August 2001.
Honicky, R.J and Miller, E.L., “A Fast Algorithm for Online Placement and Reorganization
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Huang, C., Xu, F., and Hu, X., “Massive Data Oriented Replication Algorithms for
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Lin, Y., Liu, P., and Wu, J., “Optimal Placement of Replicas in Data Grid Environments
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Multi-objective Approach”, Proceedings of Grid and Cooperative Computing (GCC),
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Ranganathan, K and Foster, I.T., “Identifying Dynamic Replication Strategies for
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Tang, M., Lee, B., Tang, X., and Yeo, C.K., “Combining Data Replication Algorithms and
Job Scheduling Heuristics in the Data Grid”, Proceedings of Euro-Par, pp 381 –390,
2005
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CHAPTER 15: PARALLEL OLAP AND BUSINESS INTELLIGENCE
Akal, F., Böhm, K., and Schek, H., “OLAP Query Evaluation in a Database Cluster: A
Performance Study on Intra-Query Parallelism”, Proceedings of Advances in Databases and Information Systems (ADBIS), pp 218 –231, 2002.
Azharul Hasan, K.M., Tsuji, T., and Higuchi, K., “A Parallel Implementation Scheme of
Relational Tables Based on Multidimensional Extendible Array”, International Journal
of Data Warehousing and Mining, 2(4):66 –85, 2006.
Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “Building Large ROLAP Data Cubes
in Parallel”, Proceedings of International Database Engineering and Application posium (IDEAS), pp 367 –377, 2004.
Sym-Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “Improved data partitioning for
build-ing large ROLAP data cubes in parallel”, Journal of Data Warehousbuild-ing and Minbuild-ing,
2(1):1 –26, 2006.
Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “Parallel ROLAP Data Cube
Con-struction On Shared-Nothing Multiprocessors”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), pp 70, 2003.
Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “Parallel ROLAP Data Cube
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Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “PnP: Parallel And External Memory
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Dehne, F., Eavis, T., and Rau-Chaplin, A., “A Cluster Architecture for Parallel Data
Ware-housing”, Proceedings of Cluster Computing and the Grid (CCGRID), pp 161 –168,
2001
Dehne, F., Eavis, T., and Rau-Chaplin, A., “Coarse Grained Parallel On-Line Analytical
Processing (OLAP) for Data Mining”, Proceedings of International Conference on putational Science, pp 589 –598, 2001.
Com-Dehne, F., Eavis, T., and Rau-Chaplin, A., “Computing Partial Data Cubes for Parallel Data
Warehousing Applications”, Proceedings of the 8th European PVM/MPI Users’ Group
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pp 319 –326, 2001
Dehne, F., Eavis, T., and Rau-Chaplin, A., “Parallel querying of ROLAP cubes in the
pres-ence of hierarchies”, Proceedings of International Workshop on Data Warehousing and OLAP (DOLAP), pp 89 –96, 2005.
Dehne, F., Eavis, T., and Rau-Chaplin, A., “The cgmCUBE project: Optimizing parallel
data cube generation for ROLAP”, Distributed and Parallel Databases, 19(1):29 –62,
2006
Dehne, F., Eavis, T., Hambrusch, S.E., and Rau-Chaplin, A., “Parallelizing the Data Cube”,
Distributed and Parallel Databases, 11(2):181 –201, 2002.
Dehne, F., Eavis, T., Hambrusch, S.E., and Rau-Chaplin, A., “Parallelizing the Data Cube”,
Proceedings of International Conference on Database Theory (ICDT), pp 129 –143,
2001
Fiser, B., Onan, U., Elsayed, I., Brezany, P., and Tjoa, A.M., “On-Line Analytical
Pro-cessing on Large Databases Managed by Computational Grids”, Proceedings of DEXA Workshops, pp 556 –560, 2004.
Gao, H and Li, J., “Parallel Data Cube Storage Structure for Range Sum Queries and
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Gorawski, M and Chechelski, R., “Parallel Telemetric Data Warehouse Balancing
Algo-rithm”, Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (ISDA), pp 387 –392, 2005.
Gorawski, M and Marks, P., “Resumption of Data Extraction Process in Parallel Data
Warehouses”, Proceedings of Parallel Processing and Applied Mathematics (PPAM),
pp 478 –485, 2005
Gorawski, M and Stachurski, K., “On Efficiency and Data Privacy Level of Association
Rules Mining Algorithms within Parallel Spatial Data Warehouse”, Proceedings of the First International Conference on Availability, Reliability and Security (ARES),
pp 936 –943, 2006
Hallmark, G., “Oracle Parallel Warehouse Server”, Proceedings of International ence on Data Engineering (ICDE), pp 314 –320, 1997.
Confer-Hu, K., Ling, C., Jie, S., Qi, G., and Tang, X., “Computing High Dimensional MOLAP
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Jin, R., Vaidyanathan, K., Yang, G., and Agrawal, G., “Communication and Memory
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Com-Jin, R., Yang, G., Vaidyanathan, K., and Agrawal, G., “Communication and Memory
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Par-Kim, J., Lee, B.S., Moon, Y., Ok, S., and Lee, W., “Parallel Consistency Maintenance of
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CHAPTERS 16 AND 17: PARALLEL AND GRID DATA MINING
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Com-Demiriz, A., “webSPADE: A Parallel Sequence Mining Algorithm to Analyze Web
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Eitrich, T and Lang, B., “Data Mining with Parallel Support Vector Machines for
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El-Hajj, M and Zạane, O.R., “Parallel Association Rule Mining with Minimum
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El-Hajj, M and Zạane, O.R., “Parallel Leap: Large-Scale Maximal Pattern Mining in a
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Dis-Fiolet, V and Toursel, B., “Progressive Clustering for Database Distribution on a Grid”,
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Garcke, J and Griebel, M., “On the Parallelization of the Sparse Grid Approach for Data
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Glimcher, L., Zhang, X., and Agrawal, G., “Scaling and Parallelizing a Scientific Feature
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Goda, K., Tamura, T., Oguchi, M., and Kitsuregawa, M., “Run-Time Load Balancing tem on SAN-connected PC Cluster for Dynamic Injection of CPU and Disk Resource - A
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Gorawski, M and Stachurski, K., “On Efficiency and Data Privacy Level of Association
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Guralnik, V., Garg, N., and Karypis, G., “Parallel Tree Projection Algorithm for Sequence
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Holt, J.D and Chung, S.M., “Parallel Mining of Association Rules from Text Databases
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Inoue, H and Narihisa, H., “Parallel and Distributed Mining with Ensemble
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Ishikawa, H., Shioya, Y., Omi, T., Ohta, M., and Katayama, K., “A Peer-to-Peer Approach
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Infor-Jin, D and Ziavras, S.G., “A Super-Programming Approach for Mining Association Rules
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Sarker, B.K., Mori, T., Hirata, T., and Uehara, K., “Parallel Algorithms for Mining
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Trang 12Acid properties of transactions, 301–303atomicity, 302
consistency, 302–303durability, 302–303isolation, 302–303Adaptive Plan Correction (APC), 279–280Amdahl law, 10
Analytical models, 33–46cost models, 33–34cost notations, 34–39communication costs, 38–39data parameters, 34–35query parameters, 37systems parameters, 36time unit costs, 37–38
parallel database, operations in, See
Databases, parallelskew model, 39–43Architectures, grid database, 26–28data-intensive applications working in, 26grid middleware, 27
Architectures, parallel database, 19–26interconnection networks, 24–26shared-disk architectures, 20–21shared-memory architectures, 20–21shared-nothing architecture, 22Association rules/Association rule data mining,
432, 440–450association rules, 444–448association rules generation, 445–448frequent itemset generation, 444–445concepts, 441–444
count distribution-based parallelism for,448–449
data distribution-based parallelism for, 450generation, 445–448
itemset, 441literals, 441
High-Performance Parallel Database Processing and Grid Databases,
by David Taniar, Clement Leung, Wenny Rahayu, and Sushant GoelCopyright 2008 John Wiley & Sons, Inc
Asynchronous protocols, GRAP, 381Atomic commit protocols, 310–314heterogeneous DBMSs, 313–314Homogeneous DBMSs, 310–313
Atomicity property, 302, See also Grid
transaction atomicity and durabilityfor centralized and homogeneous DBMSs,304
for heterogeneous distributed DBMSs, 306Autonomy, 294
Basic data partitioning, 55–60hash, 57–58
range, 58–59round-robin, 56BERD (Bubba’s Extended Range Declustering),67–69
Binary merge sort, parallel, 85–86cost model, 100–101
Binary search, 71–72Bus interconnection network, 24Bushy-tree parallelization, 258Centralized DBMSs
transactions management in, 303–305Atomicity, 304
Consistency, 304solation, 304–305Classification, parallel, 477–495data parallelism for a decision tree, 489–492data set structure, 479–480
decision tree algorithm, 480–481decision tree classification, 477–480processes, 480–488
structure, 478–479result parallelism for the decision tree,492–495