Parallel processing of OLAP queries using a cluster of workstations

International Journal of Information Technology and Decision Making

Volumn 6, Issue 2, 2007, Pages 279-299

  Dehuri, S ^a   Mall, R ^b  

^a FAKIR MOHAN UNIVERSITY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Cluster computing; Data warehouse; OLAP; Parallel processing; RDBMS

Indexed keywords

EID: 34547354165     PISSN: 02196220     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219622007002484     Document Type: Article

References (38)

1. B. Devlin, Data Warehouse; from Architecture to Implementation (Addison-Wesley, 1997).
2. Y. Shi, Y. Peng, W. Xu and X. Tang, Data mining via multiple criteria, linear programming applications in credit card portfolio management, International Journal of Information Technology and Decision Making 1(2) (2002) 131-152.
3. S. Hong, P. Katerattanukul, S.-K. Hong and Q. Cao, Usage and perceived impact of data, warehouses: A study in Korean financial companies, International Journal of Information Technology and Decision Making 5(2) (2006) 297-316.
4. J. He, X. Liu, Y. Shi, W. Xu and N. Yan, Classifications of credit cardholder behavior by using fuzzy linear programming, International Journal of Information Technology and Decision Making 3(4) (2004) 633-650.
5. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow and H. Pirahesh, Data, cube: A relational operator generalising group-by, cross-tab and sub-totals, Data Mining and Knowledge Discovery 1(1) (1997) 29-53.
6. A. Datta, B. Moon and H. Thomas, A case for parallelism in data warehousing and OLAP, DEXA '98 - 9th International Workshop on Database and Expert System Applications, Vienna, Austria (August 1998).
7. I. S. Mumick, D. Quass, and B. S. Mumick, Maintenance of data cube and summary tables in a warehouse, Proceedings of the VLDB Conference (1997).
8. D. Dewitt and J. Gray, Parallel database systems: The future of high performance database systems, Communication of the ACM 35(6) (June 1992) 85-98.
9. S. Goil and A. Choudhary, Design and implementation of a scalable parallel system for multi-dimensional analysis and OLAP, Proceedings of International Parallel Processing Symposium, Puetro Rico (1999).
10. M. Baker and R. Buyya, Cluster computing: The commodity supercomputing, Software-Practice and Experience 1(1) (January, 1999) 1-26.
11. J. Han, Towards on-line analytical mining in large databases, ACM SIGMOD Record 27 (1998) 97-107.
12. J. Han, Y. Fu, W. Wang, J. Chiang, W. Gong, K. Koperski, D. Li, Y. Lu, A. Rajan, N. Stefanovic, B. Xia and O. R. Zaiane, DBMiner: A system for mining knowledge in large relational databases, Proceedings of the 1996 International Conference on Data Mining and Knowledge Discovery (KDD'96) (Portland, Oregon, 1996), pp. 250-255.
13. Microsoft, EMC, and Unisys, T3 Project Technical Overview, White paper, Microsoft, EMC and Unisys, 2001.
14. Y. Chen, F. Dehne, T. Eavis and A. Rau-Chaplin, Building large ROLAP data cube in parallel, IDEA's (2004).
15. J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow and H. Pirahesh, Data cube: A relational operator generalising group-by, cross-tab and sub-totals, Journal of Data Mining and Knowledge Discovery 1(1) (1997) 29-53.
16. K. Beyer and R. Ramakrishnan, Bottom-up Computation of Sparse and Iceberg Cubes, ACM SIGMOD Conference on Management of Data (1999) pp. 359-370.
17. V. Harinarayan, A. Rajaraman and J. D. Ullman, Implementing data cubes efficiently, ACM SIGMOD Record 25(2) (1996), 205-216.
18. K. A. Ross and D. Srivastava, Fast computation of sparse data cubes, in Proceedings of the 23rd VLDB Conference (1997), pp. 116-125.
19. S. Sarawagi, R. Agrawal and A. Gupta, On computing the data cube, Technical Report rj10026, IBM Almaden Research Center, San Jose, CA (1996).
20. J. X. Yu and H. Lu, Multi-cube computation, Proceedings of the 7th International Symposium on Database Systems for Advanced Applications, Hong Kong (2001), pp. 126-133.
21. Y. Zhao, P. M. Deshpande and J. F. Naughton, An array-based algorithm for simultaneous multi-dimensional aggregates, Proceedings of the ACM SIGMOD Conference (1997), pp. 159-170.
22. Y. Chen, F. Dehne, T. Eavis and A. Rau-Chaplin, Parallel ROLAP data cube construction on shared-nothing multiprocessors, Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS 2003), 70.2. IEEE Computer Society (2003).
23. F. Dehne, T. Eavis and A. Rau-Chaplin, A cluster architecture for parallel data warehousing, Proceedings of the IEEE International Conference on Cluster Computing and the Grid (CCGrid 2001), Brisbane, Australia (2001).
24. F. Dehne, T. Eavis, S. Hambrusch and A. Rau-Chaplin, Parallelizing the data cube, Distributed and Parallel Databases 11(2) (2002) 181-201.
25. S. Goil and A. Choudhary, High performance OLAP and data mining on parallel computers, Journal of Data Mining and Knowledge Discovery 1(4) (1997).
26. S. Goil and A. N. Chaudhary, High performance multi-dimensional analysis of large datasets, International Workshop on Data Warehousing and OLAP (1998) 34-39.
27. H. Lu, X. Huang and Z. Li, Computing data cubes using massively parallel processors, Proceedings of the 7th Parallel Computing Workshop (PCW'97), Canberra, Australia (1997).
28. S. Muto and M. Kitsuregawa, A dynamic load balancing strategy for parallel data cube computation, ACM Second International Workshop on Data Warehousing and OLAP (DOLAP 1999) (1999), pp. 67-72.
29. R. T. Ng, A. Wagner and Y. Yin, Iceberg-cube computation with PC clusters, ACM SIGMOD Conference on Management of Data (2001), pp. 25-36.
30. Y. Chen, F. Dehne, T. Eavis and A. Rau-Chaplin, Building large ROLAP data cube in parallel, IDEA's (2004).
31. A. Data, B. Moon, K. Ramamritham, H. Thomas and I. Viguier, Have your data and index it, too: Efficient storage and indexing for data warehouses (tr98-07), Technical Report, University of Arizona (1998).
32. P. O.' Neil and G. Graefe, Multi-table joins through bitmapped join indices, SIGMOD Record 24(3) (1995) 8-11.
33. D. Dewitt and J. Gray, Parallel database systems: The future of high performance database systems, Communications of the ACM 35(6) (June 1992) 85-98.
34. M. Seetha and P. Yu, Effectiveness of parallel joins, Transaction on Knowledge and Data Engineering 2(4) (1990) 410-424.
35. A. Shatdal and J. Naughton, Using shared virtual memory for parallel join processing, Proceedings of the ACM SIGMOD (June 1993), pp. 119-128.
36. J. Wolf, D. Dias, P. Yu and J. Turek, Comparative performance of parallel join algorithms, Proceedings of the PDIS (1991), pp. 78-88.
37. C. Lee and Z. Chang, Workload balance and page access scheduling for parallel joins in shared-nothing systems, Proceedings of the ICDE (1993), pp. 411-418.
38. A. Geist, A. Beguelin, J. Dongarra, J. Jiang, W. Mancheck and V. Sunderam, PVM: Parallel Virtual Machine - A User's Guide and Tutorial for Networked Parallel Computing (MIT Press, Cambridge, MA, 1994).