Abstract
This paper describes a computer-cluster based parallel database management system (DBMS), InfiniteDB, developed by the authors. InfiniteDB aims at efficiently support data intensive computing in response to the rapid growing in database size and the need of high performance analyzing of massive databases. It can be efficiently executed in the computing system composed by thousands of computers such as cloud computing system. It supports the parallelisms of intra-query, inter-query, intra-operation, inter-operation and pipelining. It provides effective strategies for managing massive databases including the multiple data declustering methods, the declustering-aware algorithms for relational operations and other database operations, and the adaptive query optimization method. It also provides the functions of parallel data warehousing and data mining, the coordinatorwrapper mechanism to support the integration of heterogeneous information resources on the Internet, and the fault tolerant and resilient infrastructures. It has been used in many applications and has proved quite effective for data intensive computing.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Dewitt D J, Ghandeharizadeh S, Schneider D A, et al. The gamma database machine project. IEEE Transactions on Knowledge and Data Engineering, 1990, 2(1): 44–62
Boral H, Alexander W, Clay L, et al. A highly parallel database system. IEEE Transactions on Knowledge and Data Engineering, 1990, 2(1): 4–24
Stonebraker M, Katz R H, Patterson D A, et al. The design of XPRS. In: Proceedings of the 14th VLDB Conference, 1988, 318–330
Baru C K, Fecteau G, Hsiao H, et al. DB2 parallel edition. IBM Systems journal, 1995, 34(2): 292–322
Hallmark G. Oracle parallel warehouse server. In: Proceedings of the 13th international conference on Data Engineering, 1997, 314–320
Li J Z, Du W. Parallel join algorithms based on CMD data declustering method. Chinese Journal of Software, 9(4): 256–262 (in Chinese)
Li J Z, Sun W J, Li Y S. Parallel join algorithms based on parallel Btree. In: Proceedings of the third international Symposium on Cooperative Database System for Advanced Applications (CODAS). Beijing, China, 2001, 178–185
Li J Z, Li J. A parallel query plan model for parallel relational database systems. Chinese Journal of Advanced Software Research, 1994, 1.1(4): 301–318 (in Chinese)
Li J Z, Cai Z P, Chen S Y. Multi-weighted tree based query optimization methods for parallel relational database system. In: Proceedings of the third international Symposium on Cooperative Database System for Advanced Applications (CODAS). Beijing, China, 2001, 186–193
Wu W L, Gao H, Li J Z. New algorithm for computing cube on very large compressed data sets. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(12): 1667–1680
Li J Z, Srivastava J. Efficient aggregation algorithms for compressed data warehouses. IEEE Transactions on Data and Knowledge Engineering, 2002, 14(3): 515–529
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, J., Zhang, W. Cluster based parallel database management system for data intensive computing. Front. Comput. Sci. China 3, 302–314 (2009). https://doi.org/10.1007/s11704-009-0031-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-009-0031-5