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BIBLIOGRAPHY 531
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in a High Performance Database Cluster”, Proceedings of International Conference on
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Grid”, Proceedings of 10th IEEE International Symposium on High Performance and
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Honicky, R.J. and Miller, E.L., “A Fast Algorithm for Online Placement and Reorganization
of Replicated Data”, Proceedings of International Paralleland 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.
Lei, M. and Vrbsky, S.V., “A Data Replication Strategy to Increase Data Availability in Data
Grids”, Proceedings of the International Conference on Grid Computing & Applications
(GCA), pp. 221–227, 2006.
Lin, Y., Liu, P., and Wu, J., “Optimal Placement of Replicas in Data Grid Environments
with Locality Assurance”, Proceedings of International Conference on Paralleland Dis-
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Liu, P. and Wu, J., “Optimal Replica Placement Strategy for Hierarchical Data Grid Sys-
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Park, S., Kim, J., Ko, Y., and Yoon, W., “Dynamic Data Grid Replication Strategy Based
on Internet Hierarchy”, Proceedings of Gridand Cooperative Computing (GCC),
pp. 838–846, 2003.
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Multi-objective Approach”, Proceedings of Gridand Cooperative Computing (GCC),
pp. 645–656, 2005.
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
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532 BIBLIOGRAPHY
You, X., Chang, G., Chen, X., Tian, C., and Zhu, C., “Utility-Based Replication Strategies
in Data Grids”, Proceedings of Gridand 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 andParallel Databases,
15(3):219–236, 2004.
Chen, Y., Dehne, F., Eavis, T., and Rau-Chaplin, A., “PnP: ParallelAnd 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.
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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 Com-
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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
BIBLIOGRAPHY 533
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Dehne, F., Eavis, T., and Rau-Chaplin, A., “Parallel querying of ROLAP cubes in the pres-
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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 andParallel Databases, 19(1):29–62,
2006.
Dehne, F., Eavis, T., Hambrusch, S.E., and Rau-Chaplin, A., “Parallelizing the Data Cube”,
Distributed andParallel 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-
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Gao, H. and Li, J., “Parallel Data Cube Storage Structure for Range Sum Queries and
Dynamic Updates”, J. Comput. Sci. Technol., 20(3):345–356, 2005.
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 ParallelProcessingand 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),
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Jin, R., Vaidyanathan, K., Yang, G., and Agrawal, G., “Communication and Memory
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Cube Construction”, Proceedings of International Conference on Parallel Processing
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Jin, R., Yang, G., Vaidyanathan, K., and Agrawal, G., “Communication and Memory Opti-
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allel Processing (ICPP), pp. 573–580, 2003.
Kim, J., Lee, B.S., Moon, Y., Ok, S., and Lee, W., “Parallel Consistency Maintenance of
Materialized Views Using Referential Integrity Constraints in Data Warehouses”, Pro-
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534 BIBLIOGRAPHY
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Computing Systems (HPCS), pp. 4, 2006.
Li, J. and Gao, H., “Parallel Hierarchical Data Cube for Range Sum Queries and
Dynamic Updates”, Proceedings of Databaseand Expert Systems Applications (DEXA),
pp. 339–348, 2004.
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Liu, B., Chen, S., and Rundensteiner, E.A., “A Transactional Approach to Parallel Data
Warehouse Maintenance”, Proceedings of Data Warehousing and Knowledge Discovery
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Lu, H., Yu, J.X., Feng, L., and Li, Z., “Fully Dynamic Partitioning: Handling Data Skew in
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CHAPTERS 16 AND 17: PARALLELANDGRID DATA
MINING
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Chung, S.M. and Mangamuri, M., “Mining Association Rules from Relations on a Parallel
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Chung, S.M. and Mangamuri, M., “Mining Association Rules from the Star Schema on a
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Dehne, F., Eavis, T., and Rau-Chaplin, A., “Coarse Grained Parallel On-Line Analytical
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Eitrich, T. and Lang, B., “Data Mining with Parallel Support Vector Machines for Classifi-
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El-Hajj, M. and Zaïane, O.R., “Parallel Association Rule Mining with Minimum
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El-Hajj, M. and Zaïane, O.R., “Parallel Leap: Large-Scale Maximal Pattern Mining in a
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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 Sys-
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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.
Guralnik, V., Garg, N., and Karypis, G., “Parallel Tree Projection Algorithm for Sequence
Mining”, Proceedings of Euro-Par, pp. 310–320, 2001.
Holt, J.D. and Chung, S.M., “Parallel Mining of Association Rules from Text Databases
on a Cluster of Workstations”, Proceedings of International Paralleland Distributed
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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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Jin, D. and Ziavras, S.G., “A Super-Programming Approach for Mining Association Rules
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ADDITIONAL READING: FUTURE PARALLEL/GRID
DATA-INTENSIVE APPLICATIONS
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[...]... 480–488 structure, 478–479 result parallelism for the decision tree, 492–495 High-PerformanceParallelDatabaseProcessingandGrid 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,
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CHAPTERS 16 AND 17: PARALLEL AND GRID DATA
MINING
Brezany, P., Kloner, C., and Tjoa,