Managing periodically updated data in relational databases: A stochastic modeling approach

Journal of the ACM

Volumn 48, Issue 6, 2001, Pages 1141-1183

  Gal, Avigdor ^a   Eckstein, Jonathan ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Data obsolescence; Database replication management; Obsolescence cost; Stochastic modeling

Indexed keywords

DATA OBSOLESCENCE; DATABASE REPLICATION MANAGEMENT; DISTRIBUTED DATABASES; OBSOLESCENCE COSTS;

COMPUTER NETWORKS; COMPUTER OPERATING SYSTEMS; COSTS; DATA ACQUISITION; DATA REDUCTION; INFORMATION MANAGEMENT; NETWORK PROTOCOLS; OBSOLESCENCE; POISSON DISTRIBUTION; RANDOM PROCESSES; STOCHASTIC CONTROL SYSTEMS; SYNCHRONIZATION;

RELATIONAL DATABASE SYSTEMS;

EID: 0141632818     PISSN: 00045411     EISSN: None     Source Type: Journal    
DOI: 10.1145/504794.504797     Document Type: Article

References (39)

1. ABITEBOUL, S. 1999. On views and XML. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS). ACM, New York, pp. 1-9.
2. ABITEBOUL, S., AND DUSCHKA, O. 1998. Complexity of answering queries using materialized views. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS). ACM, New York, pp. 254-263.
3. ALONSO, R., BARBARA, D., AND GARCIA-MOLINA, H. 1990. Data caching issues in information retrieval system. ACM Trans. Datab. Syst. 15, 3 (Sept.), 359-384.
4. ANDERSON, T., BREITBART, Y., KORTH, H., AND WOOL, A. 1998. Replication, consistency, and practicality: Are these mutually exclusive? In Proceedings of the ACM-SIGMOD conference on Management of Data (SIGMOD) (Seattle, Wash. June). L. M. Haas and A. Tiwari, Eds. ACM, New York, pp. 484-495.
5. CAREY, M., FRANKLIN, M., LIVNY, M., AND SHEKITA, E. 1991. Data caching tradeoffs in client-server DBMS architectures. In Proceedings of the ACM-SIGMOD conference on Management of Data (SIGMOD) (Denver, Colo., May), J. Clifford and R. King, Eds. ACM, New York, pp. 357-366.
6. CHAUDHURI, S., KRISHNAMURTHY, R., POTAMIANOS, S., AND SHIM, K. 1995. Optimizing queries with materialized views. In Proceedings of the IEEE CS International Conference on Data Engineering (Taipei, Taiwan). IEEE Computer Society Press, Los Alamitos, Calif., pp. 190-200.
7. CHO, J., AND GARCIA-MOLINA, H. 2000. Synchronizing a database to improve freshness. In Proceedings of the ACM-SIGMOD Conference on Management of Data (SIGMOD) (Dallas, Tex., May). ACM, New York, pp. 117-128.
8. COLBY, L., GRIFFIN, T., LIBKIN, L., MUMICK, I., AND TRICKEY, H. 1996. Algorithms for deferred view maintenance. In Proceedings of the ACM-SIGMOD Conference on Management of Data (SIGMOD) (Montreal, Que., Canada, June). H. V. Jagadish and I. S. Mumick, Eds. ACM, New York, pp. 469-480.
9. COLBY, L., KAWAGUCHI, A., LIEUWEN, D., MUMICK, I., AND Ross, K. 1997. Supporting multiple view maintenance policies. In Proceedings of the ACM-SIGMOD Conference on Management of Data (SIGMOD) (Tucson, AZ., May). J. Peckham, Ed, ACM, New York, pp. 405-416.
10. DELIS, A., AND ROUSSOPOULOS, N. 1998. Techniques for update handling in the enhanced client-server DBMS. IEEE Trans. Knowl. Data Eng. 10, 3, 458-476.
11. FRIEDMAN, N., GETOOR, L., KOLLER, D., AND PFEFFER, A. 1999. Learning probabilistic relational models. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 99) (Stockholm, Sweden, July-Aug.), Morgan-Kaufmann, San Mateo, Calif.
12. GAL, A. 1999. Obsolescent materialized views in query processing of enterprise information systems. In Proceedings of the International Conference on Information and Knowledge Management (CIKM) (Kansas City, Mo.), pp. 367-374.
13. GAL, A., AND ECKSTEIN, J. 2001. Scheduling of data transcription in periodically connected databases. Tech. Rep. 25-2001 (Feb.). Rutgers Univ., RUTCOR, Rutgers Center for Operations Research, Piscataway, N.J.
14. GRUMBACH, S., AND TINININI, L. 2000. On the content of materialized aggregate views, In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS). ACM, New York, pp. 47-57.
15. HAAS, P., NAUGHTON, J., SESHADRI, S., AND STOKES, L. 1995. Sampling-based estimation of the number of distinct values of an attribute. In Proceedings of the International Conference on Very Large Data Bases (VLDB). pp. 311-322.
16. HEYDON, A., AND NAJORK, M. 1999. Mercator: A scalable, extensible, Web crawler. In Proceedings of the 8th World-Wide Web Conference, pp. 219-229.
17. HOGG, R., AND TANIS, E. 1983. Probability and Statistical Inference (2nd ed.). MacMillan, New York.
18. KÉMENY, J., AND SNELL, J. 1960. Finite Markov Chains. Van Nostrand.
19. LAKSHMANAN, L., LEONE, N., Ross, R., AND SUBRAHMANIAN, V. 1997. Probview: A flexible probabilistic database system. ACM Transactions on Database Systems (TODS) 22, 3, 419-469.
20. LEVY, A., MENDELZON, A., AND SAGIV, Y. 1995. Answering queries using views. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) (San Jose, Cailf., May). ACM, New York, pp. 95-104.
21. LINDSAY, B., HAAS, L., MOHAN, C., PIRAHESH, H., AND WILMS, P. 1986. A snapshot differential refresh algorithm. In Proceedings of the ACM-SIGMOD Conference on Management of Data (SIGMOD) (Washington, D.C., May). C. Zaniolo, Ed. ACM, New York, pp. 53-60.
22. LOHMAN, G., MOHAN, C., HAAS, L., DANIELS, D., LINDSAY, B., SELINGER, P., AND WILMS, P. 1985. Query processing in R*. In Query Processing in Database Systems. W. Kim, D. Reiner, and D. Batroy, Eds., Springer-Verlag, New York.
23. MATTIAS, Y., VITTER, J., AND WANG, M. 1998. Wavelet-based histograms for selectivity estimation. In Proceedings of the ACM-SIGMOD Conference on Management of Data (SIGMOD) ACM, New York, pp. 448-459.
24. MENDELZON, A., AND MILO, T. 1997. Formal models of the Web. In Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) ACM, New York, pp. 134-143.
25. NIEZETTE, M., AND STEVENNE, J. 1992. An efficient symbolic representation of periodic time. In Proceedings of the International Conference on Information and Knowledge Management (CIKM).
26. OLSTON, C., AND WIDOM, J. 2000. Offering a precision-performance tradeoff for aggregation queries over replicated data. In Proceedings of the International Conference on Very Large Data Bases (VLDB) (Cairo, Egypt, Sept.), Morgan-Kaufmann, San Mateo, Calif., pp. 144-155.
27. PINKERTON, B. 1994. Finding what people want: Experiences with the Web crawler. In Proceedings of the Second World-Wide Web Conference.
28. RASCHID, L., VIDAL, M., AND ZADOROSHNY, V. 2001. A strategy for query optimization for wide area applications. Tech. rep., University of Maryland.
29. Ross, S. 1980. Introduction to Probability Models. Academic Press, Orlando, Fla.
30. Ross, S. 1995. Stochastic Processes (2nd ed.). Wiley, New York.
31. ROTH, M., ARYA, M., HASS, L., CAREY, M., CODEY, W., FAGIN, R., SCHWARTZ, P., II, J. T., AND WIMMERS, E. 1996. The Garlic project, In Proceedings of the ACM-SIGMOD Conference on Management of Data (SIGMOD). ACM, New York.
32. SHETH, A., AND LARSON, J. 1990. Federated database systems for managing distributed, heterogeneous, and autonomous databases. ACM Comput. Surv. 22, 3, 183-236.
33. SHURETY, S. 1998. E-Business with Net.Commerce. Prentice-Hall, Englewood Cliffs, N.J.
34. STOUMBOS, Z., AND REYNOLDS, M., JR. 1997. Control charts applying a sequential test at fixed sampling intervals. J. Qual. Tech. 29, 21-40.
35. STOUMBOS, Z., AND REYNOLDS, M., JR. 2002. Economic statistical design of adaptive individuals control schemes for monitoring the process mean and variance. Nonlinear Analysis: Real World Applications, to appear.
36. TAYLOR, H., AND KARLIN, S. 1994. An Introduction to Stochastic Modeling. Academic Press, Orlando, Fla.
37. TRAUB, J., AND WERSCHULZ, A. 1998. Complexity and Information. Cambridge University Press.
38. WHANG, K.-Y., ZANDEN, B. V., AND TAYLOR, H. 1990. A linear-time probabilistic counting algorithm for database applications. ACM Trans. Datab. Syst. (TODS) 15, 2, 208-229.
39. YANG, H., AND LARSON, P. 1987. Query transformation for PSJ-queries. In Proceedings of the International Conference on Very Large Data Bases (VLDB). pp. 245-254.