1. Trang chủ
  2. » Công Nghệ Thông Tin

Tài liệu High-Performance Parallel Database Processing and Grid Databases- P13 doc

24 267 0

Đang tải... (xem toàn văn)

Tài liệu hạn chế xem trước, để xem đầy đủ mời bạn chọn Tải xuống

THÔNG TIN TÀI LIỆU

Thông tin cơ bản

Định dạng
Số trang 24
Dung lượng 159,02 KB

Nội dung

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. 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 Parallel and Dis- tributed Systems (ICPADS), pp. 465–474, 2006. Liu, P. and Wu, J., “Optimal Replica Placement Strategy for Hierarchical Data Grid Sys- tems”, Proceedings of Cluster Computing and the Grid (CCGRID), pp. 417–420, 2006. Park, S., Kim, J., Ko, Y., and Yoon, W., “Dynamic Data Grid Replication Strategy Based on Internet Hierarchy”, Proceedings of Grid and Cooperative Computing (GCC), pp. 838–846, 2003. Rahman, R.M., Barker, K., and Alhajj, R., “Replica Placement in Data Grid: A Multi-objective Approach”, Proceedings of Grid and 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 the Grid (CCGrid), pp. 106–113, May 2001. 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 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. 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 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 Confer- ence on Data Engineering (ICDE), pp. 314–320, 1997. Hu, K., Ling, C., Jie, S., Qi, G., and Tang, X., “Computing High Dimensional MOLAP with Parallel Shell Mini-cubes”, Proceedings of Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1192–1196, 2005. Jin, R., Vaidyanathan, K., Yang, G., and Agrawal, G., “Communication and Memory Optimal Parallel Data Cube Construction”, IEEE Trans. Parallel Distrib. Syst., 16(12):1105–1119, 2005. Jin, R., Vaidyanathan, K., Yang, G., and Agrawal, G., “Using Tiling to Scale Parallel Data Cube Construction”, Proceedings of International Conference on Parallel Processing (ICPP), pp. 365–372, 2004. Jin, R., Yang, G., and Agrawal, G., “Parallel Data Cube Construction: Algorithms, Theo- retical Analysis, and Experimental Evaluation”, Proceedings of High Performance Com- puting (HiPC), pp. 74–84, 2003. Jin, R., Yang, G., Vaidyanathan, K., and Agrawal, G., “Communication and Memory Opti- mal Parallel Data Cube Construction”, Proceedings of International Conference on Par- 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- ceedings of Data Warehousing and Knowledge Discovery (DaWaK), pp. 146–156, 2005. 534 BIBLIOGRAPHY Lawrence, M. and Rau-Chaplin, A., “The OLAP-Enabled Grid: Model and Query Pro- cessing Algorithms”, Proceedings of International Symposium on High Performance Computing Systems (HPCS), pp. 4, 2006. Li, J. and Gao, H., “Parallel Hierarchical Data Cube for Range Sum Queries and Dynamic Updates”, Proceedings of Database and Expert Systems Applications (DEXA), pp. 339–348, 2004. Lima, A., Mattoso, M., and Valduriez, P., “OLAP Query Processing in a Database Cluster”, Proceedings of Euro-Par, pp. 355–362, 2004. Liu, B., Chen, S., and Rundensteiner, E.A., “A Transactional Approach to Parallel Data Warehouse Maintenance”, Proceedings of Data Warehousing and Knowledge Discovery (DaWaK), pp. 307–316, 2002. Lu, H., Yu, J.X., Feng, L., and Li, Z., “Fully Dynamic Partitioning: Handling Data Skew in Parallel Data Cube Computation”, Distributed and Parallel Databases, 13(2):181–202, 2003. Märtens, H., Rahm, E., and Stöhr, T., “Dynamic query scheduling in parallel data warehouses”, Concurrency and Computation: Practice and Experience, 15(11–12):1169–1190, 2003. Märtens, H., Rahm, E., and Stöhr, T., “Dynamic Query Scheduling in Parallel Data Ware- houses”, Proceedings of Euro-Par, pp. 321–331, 2002. Monteiro, A.M.C. and Furtado, P., “Data Skew-Handling in Parallel MDIM Data Ware- houses”, Proceedings of Databases and Applications, pp. 157–162, 2005. Nguyen, T. M., Brezany, P., Tjoa, A. M., and Weippl, E., “Toward a Grid-Based Zero-Latency Data Warehousing Implementation for Continuous Data Streams Processing”, International Journal of Data Warehousing and Mining, 1(4):22–55, 2005. Saeki, S., Bhalla, S., and Hasegawa, M., “Parallel Generation of Base Relation Snapshots for Materialized View Maintenance in Data Warehouse Environment”, Proceedings of the 2002 International 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, A.M., “Development of a Grid Service for Scalable Deci- sion Tree Construction from Grid Databases”, Proceedings of Parallel Processing and Applied Mathematics (PPAM), pp. 616–624, 2005. Christen, P., Hegland, M., Nielsen, O.M., Roberts, S., Strazdins, P.E., Semenova, T., Altas, I., and Hancock, T., “Towards a Parallel Data Mining Toolbox”, Proceedings of Interna- tional Parallel and Distributed Processing Symposium (IPDPS), pp. 156, 2001. Chung, S.M. and Mangamuri, M., “Mining Association Rules from Relations on a Parallel NCR Teradata Database System”, Proceedings of Information Technology: Coding and Computing (ITCC), pp. 465–470, 2004. Chung, S.M. and Mangamuri, M., “Mining Association Rules from the Star Schema on a Parallel NCR Teradata Database System”, Proceedings of Information Technology: Cod- ing and Computing (ITCC), pp. 206–212, 2005. Cong, S., Han, J., and Padua, D.A., “Parallel mining of closed sequential patterns”, Pro- ceedings of Knowledge Discovery and Data Mining (KDD), pp. 562–567, 2005. BIBLIOGRAPHY 535 Congiusta, A., Talia, D., and Trunfio, P., “Parallel and Grid-Based Data Mining - Algo- rithms, Models and Systems for High-Performance KDD”, Proceedings of the Data Mining and Knowledge Discovery Handbook, pp. 1017–1041, 2005. 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. Demiriz, A., “webSPADE: A Parallel Sequence Mining Algorithm to Analyze Web Log Data”, Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 755–758, 2002. Eitrich, T. and Lang, B., “Data Mining with Parallel Support Vector Machines for Classifi- cation”, Proceedings of Advances in Information Systems (ADVIS), pp. 197–206, 2006. El-Hajj, M. and Zaïane, O.R., “Parallel Association Rule Mining with Minimum Inter-Processor Communication”, Proceedings of DEXA Workshops, pp. 519–523, 2003. El-Hajj, M. and Zaïane, O.R., “Parallel Leap: Large-Scale Maximal Pattern Mining in a Distributed Environment”, Proceedings of International Conference on Parallel and Dis- tributed Systems (ICPADS), pp. 135–142, 2006. Fiolet, V. and Toursel, B., “Progressive Clustering for Database Distribution on a Grid”, Proceedings of the 4th International Symposium on Parallel and Distributed Computing (ISPDC), pp. 282–289, 2005. Foti, D., Lipari, D., Pizzuti, C., and Talia, D., “Scalable Parallel Clustering for Data Min- ing on Multicomputers”, Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing, pp. 390–398, 2000. Garcke, J. and Griebel, M., “On the Parallelization of the Sparse Grid Approach for Data Mining”, Proceedings of Large-Scale Scientific Computing (LSSC), pp. 22–32, 2001. Glimcher, L., Zhang, X., and Agrawal, G., “Scaling and Parallelizing a Scientific Feature Mining Application Using a Cluster Middleware”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), 2004. Goda, K., Tamura, T., Oguchi, M., and Kitsuregawa, M., “Run-Time Load Balancing Sys- tem on SAN-connected PC Cluster for Dynamic Injection of CPU and Disk Resource - A Case Study of Data Mining Application”, Proceedings of Database and Expert Systems Applications (DEXA), pp. 182–192, 2002. 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 Parallel and Distributed Processing Symposium (IPDPS), 2004. Inoue, H. and Narihisa, H., “Parallel and Distributed Mining with Ensemble Self-Generating Neural Networks”, Proceedings of International Conference on Parallel and Distributed Systems (ICPADS), pp. 423–428, 2001. Ishikawa, H., Shioya, Y., Omi, T., Ohta, M., and Katayama, K., “A Peer-to-Peer Approach to Parallel Association Rule Mining”, Proceedings of Knowledge-Based Intelligent Infor- mation & Engineering Systems (KES), pp. 178–188, 2004. Jin, D. and Ziavras, S.G., “A Super-Programming Approach for Mining Association Rules in Parallel on PC Clusters”, IEEE Trans. Parallel Distrib. Syst., 15(9):783–794, 2004. 536 BIBLIOGRAPHY Jin, R. and Agrawal, G., “Shared Memory Parallelization of Decision Tree Construction Using a General Data Mining Middleware”, Proceedings of Euro-Par, pp. 346–354, 2002. Jinlan, T., et al., “Parallelism of Association Rules Mining and Its Application in Insur- ance Operations”, Proceedings of International Conference on Computational Science, pp. 907–914, 2004. Kim, H.S., Gao, S., Xia, Y., Kim, G.B., and Bae, H., “DGCL: An Efficient Density and Grid Based Clustering Algorithm for Large Spatial Database”, Proceedings of Web-Age Information Management (WAIM), pp. 362–371, 2006. Kitsuregawa, M. and Pramudiono, I., “PC Cluster Based Parallel Frequent Pattern Min- ing and Parallel Web Access Pattern Mining”, Proceedings of Databases in Networked Information Systems (DNIS), pp. 172–176, 2003. Kitsuregawa, M., Pramudiono, I., Takahashi, K., and Prasetyo, B., “Web Mining Is Paral- lel”, Proceedings of High Performance Computing (HiPC), pp. 385–398, 2001. Kitsuregawa, M., Shintani, T., Yoshizawa, T., and Pramudiono, I., “Web Log Mining and Parallel SQL Based Execution”, Proceedings of Databases in Networked Information Systems (DNIS), pp. 20–32, 2000. Kuntraruk, J. and Pottenger, W.M., “Massively Parallel Distributed Feature Extraction in Textual Data Mining Using HDDI(tm)”, Proceedings of IEEE International Symposium on High Performance Distributed Computing (HPDC), pp. 363–370, 2001. Leung, C.K., “Efficient Parallel Mining of Constrained Frequent Patterns”, Proceedings of International Symposium on High Performance Computing Systems (HPCS), pp. 73–82, 2004. Li, E., Li, W., Wang, T., Di, N., Dulong, C., and Zhang, Y., “Towards the Parallelization of Shot Detection—a Typical Video Mining Application Study”, Proceedings of Interna- tional Conference on Parallel Processing (ICPP), pp. 585–592, 2006. Li, T. and Bollinger, T., “Distributed and Parallel Data Mining on the Grid”, Proceed- ings of International Conference Architecture of Computing Systems (ARCS) Workshops, pp. 370–379, 2004. Li, X., Jin, R., and Agrawal, G., “Compiler and Runtime Support for Shared Memory Par- allelization of Data Mining Algorithms”, Proceedings of Languages and Compilers for Parallel Computing (LCPC), pp. 265–279, 2002. Liu, Z., Kamohara, S., and Guo, M., “A Scheme of Interactive Data Mining Support System in Parallel and Distributed Environment”, Proceedings of International Symposium on Parallel and Distributed Processing and Applications (ISPA), pp. 263–272, 2003. Ma, C. and Li, Q., “Parallel Algorithm for Mining Frequent Closed Sequences”, Proceed- ings of International Workshop on Autonomous Intelligent Systems: Agents and Data Mining (AIS-ADM), pp. 184–192, 2005. Melab, N. and Talbi, E., “A Parallel Genetic Algorithm for Rule Mining”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), p. 133, 2001. Melab, N., Cahon, S., Talbi, E., and Duponchel, L., “Parallel GA-Based Wrapper Feature Selection for Spectroscopic Data Mining”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), pp. 201–208, 2002. Oguchi, M. and Kitsuregawa, M., “Optimizing transport protocol parameters for large scale PC cluster and its evaluation with parallel data mining”, Cluster Computing, 3(1):15–23, 2000. Oguchi, M. and Kitsuregawa, M., “Parallel Data Mining on ATM-Connected PC Cluster and Optimization of Its Execution Environments”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS) Workshops, pp. 366–373, 2000. BIBLIOGRAPHY 537 Oguchi, M. and Kitsuregawa, M., “Using Available Remote Memory Dynamically for Parallel Data Mining Application on ATM-Connected PC Cluster”, Proceedings of Inter- national Parallel and Distributed Processing Symposium (IPDPS), pp. 411–420, 2000. Parthasarathy, S., Zaki, M.J., and Li, W., “Memory Placement Techniques for Parallel Association Mining”, Proceedings of Knowledge Discovery and Data Mining (KDD), pp. 304–308, 1998. Parthasarathy, S., Zaki, M.J., Ogihara, M., and Li, W., “Parallel Data Mining for Association Rules on Shared-Memory Systems”, Knowl. Inf. Syst. 3(1):1–29, 2001. Pramudiono, I. and Kitsuregawa, M., “Parallel Web Access Pattern Mining on PC Cluster”, Proceedings of International Conference on Internet Computing, pp. 70–76, 2003. Pramudiono, I. and Kitsuregawa, M., “Tree Structure Based Parallel Frequent Pattern Min- ing on PC Cluster”, Proceedings of Database and Expert Systems Applications (DEXA), pp. 537–547, 2003. Qiang, Z., Zheng, Z., Wei, S.Z., and Daley, E., “WINP: A Window-Based Incremental and Parallel Clustering Algorithm for Very Large Databases”, Proceedings of International Conference on Tools with Artificial Intelligence (ICTAI), pp. 169–176, 2005. Rana, O.F., Walker, D.W., Li, M., Lynden, S.J., and Ward, M., “PaDDMAS: Parallel and Distributed Data Mining Application Suite”, Proceedings of International Parallel and Distributed Processing Symposium (IPDPS), pp. 387–392, 2000. Sarker, B.K., Mori, T., Hirata, T., and Uehara, K., “Parallel Algorithms for Mining Asso- ciation Rules in Time Series Data”, Proceedings of International Symposium on Parallel and Distributed Processing and Applications (ISPA), pp. 273–284, 2003. Sarker, B.K., Uehara, K., and Yang, L.T., “Exploiting Efficient Parallelism for Mining Rules in Time Series Data”, Proceedings of the International Conference on High Performance Computing and Communications (HPCC), pp. 845–855, 2005. Senger, H., Hruschka, E.R., Silva, F.A.B.d., Sato, L.M., Bianchini, C.D.P., and Esperidi ~ aao, M.D., Inhambu: Data Mining Using Idle Cycles in Clusters of PCs, Proceedings of Net- work and Parallel Computing (NPC), pp. 213–220, 2004. Shi, L., Niu, C., Zhou, M., and Gao, J., “A DOM Tree Alignment Model for Mining Par- allel Data from the Web”, Proceedings of Meeting of the Association for Computational Linguistics (ACL), pp. 489–496, 2006. Sterritt, R., Adamson, K., Shapcott, M., and Curran, E.P., “Parallel Data Mining of Bayesian Networks from Telecommunications Network Data”, Proceedings of IPDPS Workshops, pp. 415–426, 2000. Talaie, S., Leigh, R., Louis, S.J., and Raines, G.L., “Predicting mining activity with parallel genetic algorithms”, Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 2149–2155, 2005. Valdés, J.J. and Barton, A.J., “Mining Multivariate Time Series Models with Soft-Computing Techniques: A Coarse-Grained Parallel Computing Approach”, Proceedings of Computational Science and Its Applications (ICCSA), pp. 259–268, 2003. Veloso, A., Otey, M.E., Parthasarathy, S. and Meira Jr. W., “Parallel and Distributed Fre- quent Itemset Mining on Dynamic Datasets”, Proceedings of High Performance Com- puting (HiPC), pp. 184–193, 2003. Wang, F. and Helian, N., “Mining Global Association Rules on an Oracle Grid by Scanning Once Distributed Databases”, Proceedings of Euro-Par, pp. 370–378, 2005. Wang, H., Xiao, Z., Zhang, H. and Jiang, S., “Parallel Algorithm for Mining Maximal Fre- quent Patterns”, Proceedings of Advanced Parallel Programming Technologies (APPT), pp. 241–248, 2003. 538 BIBLIOGRAPHY Wu, M., Chung, M. and Moonesinghe, H.D.K., “Parallel Implementation of WAP-Tree Mining Algorithm”, Proceedings of International Conference on Parallel and Distributed Systems (ICPADS), 2004. Zaïane, O.R., El-Hajj, M. and Lu, P., “Fast Parallel Association Rule Mining without Can- didacy Generation”, Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 665–668, 2001. Zaki, M.J. and Pan, Y., “Introduction: Recent Developments in Parallel and Distributed Data Mining”, Distributed and Parallel Databases 11(2):123–127, 2002. Zaki, M.J. Parthasarathy, S., Ogihara, M., and Li, W., “Parallel Algorithms for Discovery of Association Rules”, Data Min. Knowl. Discov. 1(4): 343–373, 1997. Zaki, M.J., “Parallel Sequence Mining on Shared-Memory Machines”, J. Parallel Distrib. Comput. 61(3):401–426, 2001. Zaki, M.J., Ho, C-T. and Agrawal, R., “Parallel Classification for Data Mining on Shared-Memory Multiprocessors”, Proceedings of the International Conference on Data Engineering (ICDE), pp. 98–205, 1999. Zaki,M.J., “Parallel Sequence Mining on Shared-Memory Machines”, Proceedings of Large-Scale Parallel KDD Systems, pp. 161–189, 1999. Zhao, B., Vogel, S., “Adaptive Parallel Sentences Mining from Web Bilingual News Col- lection”, Proceedings of IEEE International Conference on Data Mining (ICDM), 2002. ADDITIONAL READING: FUTURE PARALLEL/GRID DATA-INTENSIVE APPLICATIONS Chervenak, A., Foster, I., Kesselman, C., Salisbury, C., Tuecke, S., “The Data Grid: Towards an architecture for the Distributed Management and Analysis of Large Scientific Datasets”, Journal of Network and Computer Applications, 23(3):187–200, 2001. Chung, Y., “Parallel Information Retrieval with Query Expansion”, Proceedings of the 6th International Conference on Applied Parallel Computing Advanced Scientific Computing (PARA), pp. 195–202, 2002. Deloch, S., “Databases, Web Services, and Grid Computing—Standards and Directions”, Proceedings of Euro-Par, pp. 3, 2003. Koparanova, M.G. and Risch, T., “High-Performance GRID Stream Database Manager for Scientific Data”, Proceedings of European Across Grids Conference, pp. 86–92, 2003. Lü, K., Zhu, Y., and Sun, W., “Parallel Processing XML Documents”, Proceedings of International Database Engineering and Application Symposium (IDEAS), pp. 96–105, 2002. Matsuda, H., “A Grid Environment for Data Integration of Scientific Databases”, Proceed- ings of e-Science, pp. 3–4, 2005. Qin, J., Yang, S., and Dou, W., “Parallel Storing and Querying XML Documents Using Relational DBMS”, Proceedings of Advanced Parallel Programming Technologies (APPT), pp. 629–633, 2003. Sun, W. and Lü, K., “Parallel Query Processing Algorithms for Semi-structured Data”, Proceedings of Conference on Advanced Information Systems Engineering (CAiSE), pp. 770–773, 2002. BIBLIOGRAPHY 539 Trujillo, R., “Application-Specific XML Processing: A Parallel Approach for Optimum Performance”, Proceedings of Parallel and Distributed Processing Techniques and Appli- cations (PDPTA), pp. 959–964, 2005. Zaki, M.J. and Aggarwal, C.C., “XRules: An effective algorithm for structural classification of XML data”, Machine Learning 62(1–2):137–170, 2006. [...]... 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,

Ngày đăng: 26/01/2014, 15:20

TỪ KHÓA LIÊN QUAN

TÀI LIỆU CÙNG NGƯỜI DÙNG

TÀI LIỆU LIÊN QUAN