530 BIBLIOGRAPHY Salvadores, M., Herrero, P., Pérez, M.S., and Robles, V., “DCP-Grid, a Framework for Conversational Distributed Transactions on Grid Environments”, Proceedings of Inter- national Conference on Computational Science, pp. 171–178, 2005. Tang, F., Li, M., and Cao, J., “A Transaction Model for Grid Computing”, Proceedings of Advanced Parallel Programming Technologies (APPT), pp. 382–386, 2003. Tang, F., Li, M., and Huang, J.Z., “Automatic Transaction Compensation for Reliable Grid Applications”, J. Comput. Sci. Technol., 21(4):529–536, 2006. Tang, F., Li, M., Cao, J., and Deng, Q., “Coordinating Business Transaction for Grid Ser- vice”, Proceedings of Grid and Cooperative Computing (GCC), pp. 108–114, 2003. Tang, F., Li, M., Huang, J.Z., Cao, L., and Wang, Y., “A Real-Time Transaction Approach for Grid Services: A Model and Algorithms”, Proceedings of Network and Parallel Com- puting (NPC), pp. 57–64, 2004. 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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 parallel disks”, Proceedings of International Conference on Information and Knowledge Management (CIKM), pp. 32–39, 2003. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. BIBLIOGRAPHY 531 Coulon, C., Pacitti, E., and Valduriez, P., “Consistency Management for Partial Replication 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 of Replicated Data”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), pp. 57, 2003. Huang, C., Xu, F., and Hu, X., “Massive Data Oriented Replication Algorithms for Consis- tency Maintenance in Data Grids”, Proceedings of International Conference on Compu- tational Science, pp. 838–841, 2006. Lamehamedi, H., Shentu, Z., Szymanski, B.K., and Deelman, E., “Simulation of Dynamic Data Replication Strategies in Data Grids”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), pp. 100, 2003. 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Ranganathan, K. and Foster, I.T., “Identifying Dynamic Replication Strategies for a High-Performance Data Grid”, Proceedings of International Workshop on Grid Computing (GRID), pp. 75–86, 2001. Sithole, E., Parr, G.P., and McClean, S.I., “Data grid performance analysis through study of replication and storage infrastructure parameters”, Proceedings of Cluster Computing and the Grid (CCGRID), pp. 293–300, 2005. Stockinger, H., Samar, A., Holtman, K., Allcock, W.E., Foster, I.T., and Tierney, B., “File and Object Replication in Data Grids”, Proceedings of IEEE International Symposium on High Performance Distributed Computing (HPDC), pp. 76–86, 2001. 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. Tao, J. and Williams, J., “Concurrency Control and Data Replication Strategies for Large-scale and Wide-distributed Databases”, Proceedings of Database Systems for Advanced Applications (DASFAA), 2001. Vazhkudai, S., Tuecke, S., and Foster, I., “Replica Selection in the Globus Data Grid”, Proceedings of the 1st IEEE/ACM International Conference on Cluster Computing and the Grid (CCGrid), pp. 106–113, May 2001. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. 532 BIBLIOGRAPHY You, X., Chang, G., Chen, X., Tian, C., and Zhu, C., “Utility-Based Replication Strategies in Data Grids”, Proceedings of Grid and Cooperative Computing (GCC), pp. 500–507, 2006. 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 Sym- posium (IDEAS), pp. 367–377, 2004. 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 Warehousing and Mining, 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 Construction on Shared-Nothing Multiprocessors”, Distributed and Parallel Databases, 15(3):219–236, 2004. Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “PnP: Parallel And External Memory Iceberg Cubes”, Proceedings of International Conference on Data Engineering (ICDE), pp. 576–577, 2005. Chen, Y., Rau-Chaplin, A., Dehne, F., Eavis, T., Green, D., and Sithirasenan, E., “cgmO- LAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes”, Proceedings of International Conference on Data Engineering (ICDE), pp. 164–165, 2006. Codd, E. F. “An evaluation scheme for database management systems that are claimed to be relational”, Proceedings of International Conference on Data Engineering (ICDE), pp. 720–729, 1986. Codd, E.F. et. al. “Providing OLAP to User-Analysts: An IT Mandate”, http://dev.hyperion. com/resource library/white papers/providing olap to user analysts.pdf, 1993. Datta, A., VanderMeer, D.E., and Ramamritham, K., “Parallel Star Join C DataIndexes: Efficient Query Processing in Data Warehouses and OLAP”, IEEE Trans. Knowl. Data Eng., 14(6):1299–1316, 2002. 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 Com- putational Science, pp. 589–598, 2001. 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 Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. BIBLIOGRAPHY 533 Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface, 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. 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[...]... 480–488 structure, 478–479 result parallelism for the decision tree, 492–495 High-Performance Parallel Database Processing and Grid Databases, by David Taniar, Clement Leung, Wenny Rahayu, and Sushant Goel Copyright  2008 John Wiley & Sons, Inc 541 542 INDEX Classification, parallel (Continued) splitting attributes or feature selection, 481–484 Cluster/Clustering, parallel, 464–499 architectures, 23... clustering, parallel, 81–82, 471–477 algorithm, 468–471 data parallelism parallel k-means, 472–475 Leaf nodes, 189–190 Left-deep tree parallelization, 258 Linear scale up, 8 Linear search, 69 Linear speed up objective, parallel query processing, 7 Literals, 441 Load cost parallel binary-merge sort, 100 parallel merge-all sort, 99 parallel partitioned sort, 104 parallel redistribution binary-merge sort, 102 parallel. .. costs, 38–39 data parameters, 34–35 query parameters, 37 systems parameters, 36 time unit costs, 37–38 parallel database, operations in, See Databases, parallel skew model, 39–43 Architectures, grid database, 26–28 data-intensive applications working in, 26 grid middleware, 27 Architectures, parallel database, 19–26 interconnection networks, 24–26 shared-disk architectures, 20–21 shared-memory architectures,... 200–203 Case 1 (NRI-1 and NRI-3), 201 Case 2 (NRI-2), 201 Case 3 (PRI), 201 Case 4 (FRI), 201–203 Online analytic processing (OLAP) and business intelligence, 9, 401–426 cube queries, parallelization of, 412–417 cume dist queries, parallelization, 419–420 histogram queries, parallelization, 420–422 moving average queries, parallelization, 422–424 NTILE queries, parallelization, 420–422 parallel multidimensional... 440–450, See also Association rule mining 548 INDEX Parallel universal qualification, See Collection join queries Parallelism forms of, 12–19 independent parallelism, 15 interoperation parallelism, 12, 15–18 interquery parallelism, 12, 13–14 intraoperation parallelism, 12, 15, 16 intraquery parallelism, 12, 14–15 mixed parallelism, 18–19 pipeline parallelism, 15–18 Partial CUBE queries, analysis of,... analysis, 402–405 parallelization without using ROLLUP, 412 ranking queries, parallelization of, 418–419 rollup queries, parallelization, 405–412 top-N queries, parallelization of, 418–419 windowing queries, parallelization of, 422–424 Open Grid Service Architecture (OGSA), 27 Optimistic algorithms, 309 Optimistic Plan Correction (OPC), 278 Originator’s algorithm for Grid- ACP, 345 Page, 34 Parallel association... notations, parallel GroupBy-Join, 151–153 join selectivity, 153 projectivity, 152 selectivity, 152 parallel binary-merge sort, 100–101 parallel groupby, 104–108 parallel merge-all sort, 98–100 parallel partitioned sort, 103–104 parallel redistribution binary-merge sort, 101–102 parallel redistribution merge-all sort, 102–103 serial external merge-sort, 96–97 543 Count distribution-based parallelism... notation, parallel GroupBy-Join, 152 Projectivity ratio, 37 Query processing, parallel, 5–6 motivations, 5–6 objectives, 7–12 communication, 11–12 interference, 11–12 parallel obstacles, 10–12 scale up, 8–10 skew, 12 speed up, 7–8 parameters, 37 results generation cost, 45 Query scheduling and optimization, 256–287 cluster query processing model, 270–275 degree of parallelization, 258 bushy-tree parallelization,... traversal, 192–194 parallel exact-match search queries, 192–194 parallel range selection query, 194–195 processor involvement, 192–193 record loading, 192, 194 Select cost, 45, 70, 72 disjoint partitioning, 129 divide and broadcast, 128 local join, 130 parallel binary-merge sort, 100 parallel merge-all sort, 98–99 parallel partitioned sort, 104 parallel redistribution binary-merge sort, 102 parallel redistribution... mining, 431 data parallelism, 437–438 data warehouse, 429 data-intensive applications, 428 definition, 430 from databases to data warehousing to data mining, 428–431 parallel association rules, 440–450 parallel sequential patterns, 450–461 parallelism, 436–440 querying vs mining, 433–436 read-only queries, 429 result parallelism, 438–440 sequential patterns, 427–463 write queries, 429 Data parallelism, . 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. Conference on Parallel Processing Workshops (ICPPW), pp. 383–390, 2002. CHAPTERS 16 AND 17: PARALLEL AND GRID DATA MINING Brezany, P., Kloner, C., and Tjoa,