- 1.Apers, P.M.G., Hevner, A.R., Yao, S.B. Optimization Algorithms for Distributed Queries. IEEE Transactions on Software Engineering, Vol 9:1, 1983.Google Scholar
- 2.Baneilhon, F., Maier, D., Sagiv, Y., Ullman, J.D. Magic sets and other strange ways to execute logic programs. In Proe. of ACM PODS, 1986. Google ScholarDigital Library
- 3.Bernstein, P.A., Goodman, N., Wong, E., Reeve, C.L, Rothnie, J. Query Processing in a System for Distributed Databases (SDD-I), ACM TODS 6:4 (Dee 1981). Google ScholarDigital Library
- 4.Chaudhuri, S., Shim K. An Overview of Cost-based Optimization of Queries with Aggregates. IEEE DIS Bulletin, Sep. 1995. (Special Issue on Query Processing).Google Scholar
- 5.Chaudhuri, S., Shim K. Including Group-By in Query Optimization. In Proc. of VLDB, Santiago, 1994. Google ScholarDigital Library
- 6.Chaudhuri, S., Shim K. Query Optimization with aggregate views: In Proc. of EDBT, Avignon, 1996. Google ScholarDigital Library
- 7.Chaudhuri, S., Dayal, U. An Overview of Data Warehousing and OLAP Technology. In ACM SIGMOD Record, March 1997, Google ScholarDigital Library
- 8.Chaudhuri, S., Shim K. Optimization of Queries with Userdefined Predicates. In Proe. of VLDB, Murnbai, 1996. Google ScholarDigital Library
- 9.Chaudhuri, S., Krishnamurthy, R., Potamianos, S., Shim K, Optimizing Queries with Materialized Views. In Proe. of IEEE Data Engineering Conference, Taipei, 1995. Google ScholarDigital Library
- 10.Chaudhuri, S., Gravano, L. Optimizing Queries over Multimedia Repositories. In Proc. of ACM SIGMOD, Montreal, 1996. Google ScholarDigital Library
- 11.Chaudhuri, S., Motwani, R., Narasayya, V. Random Sampling for Histogram Construction: How much is enough? In Proe. of ACM SIGMOD, Seattle, 1998. Google ScholarDigital Library
- 12.Chimenti D., Gamboa R., Krishnamurthy R. Towards an Open Architecture for LDL. In Proe. of VLDB, Amsterdam, 1989. Google ScholarDigital Library
- 13.Dayal, U. Of Nests and Trees: A Unified Approach to Processing Queries That Contain Nested Subqueries, Aggregates and Quantifiers. In Proc. of VLDB, 1987. Google ScholarDigital Library
- 14.Fagin, R. Combining Fuzzy Information from Multiple Systems, In Proe. of ACM PODS, 1996. Google ScholarDigital Library
- 15.Finkelstein S., Common Expression Analysis in Database Applications. In Proe. of ACM SIGMOD, Orlando, 1982. Google ScholarDigital Library
- 16.Ganski, R.A., Long, H.K.T. Optimization of Nested SQL Queries Revisited. In Proe. of ACM SIGMOD, San Francisco, 1987. Google ScholarDigital Library
- 17.Gassner, P., Lohman, G., Sehiefer, K.B. Query Optimization in the IBM DB2 Family. IEF~ Data Engineering Bulletin, Dee. 1993.Google Scholar
- 18.Gibbons, P.B., Matias, Y., Poosala, V. Fast Incremental Maintenance of Approximate Histograms. In Proe. of VLDB, Athens, 1997. Google ScholarDigital Library
- 19.Graefe, G., Ward K. Dynamic Query Evaluation Plans. In Proe. of ACM SIGMOD, Portland, 1989. Google ScholarDigital Library
- 20.Graefe G. Query Evaluation Techniques for Large Databases. In ACM Computing Surveys: Vo125, No 2., June 1993. Google ScholarDigital Library
- 21.Graefe, G. The Cascades Framework for Query Optimization. In Data Engineering Bulletin. Sept. 1995.Google Scholar
- 22.Graefe, G., Dewitt DJ. The Exodus Optimizer Generator. In Proe. of ACM SIGMOD, San Francisco, 1987. Google ScholarDigital Library
- 23.Graefe, G,, MeKenna, W.J. The Volcano Optimizer Generator: Extenslbility and Efficient Search. In Proe. of the IEEE Conference on Data Engineering, Vienna, 1993. Google ScholarDigital Library
- 24.Gray, J,, Bosworth, A., Layman A., Pirahesh H. Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross- Tab, and Sub.Totals. In Proe. of IEEE Conference on Data Engineering, New Orleans, 1996. Google ScholarDigital Library
- 25.Gupta A,, Harinarayan V., Quass D. Aggregate-query processing In data warehousing environments. In Proe. of VLDB, Zurich, 1995, Google ScholarDigital Library
- 26.Hnas, L,, Freytag, J,C,, Lohman, G.M., Pirahesh, H. Extensible Query Processing in Starburst. In Proe. of ACM SIGMOD, Portland, 1989. Google ScholarDigital Library
- 27.Haas, P,J,, Naughton, J.F., $eshadri, S., Stokes, L. Sampling- Based Estimation of the Number of Distinct Values of an Attribute, In Proe, of VLDB, Zurich, 1995. Google ScholarDigital Library
- 28.Hasan, W, Optimization of SQL Queries for Parallel Machines. LNCS 1182, Springer, Verlag, 1996. Google ScholarDigital Library
- 29.Hellersteln J,M,, Stonebraker, M. Predicate Migration: Optimization queries with expensive predicates. In Proe. of ACM SIGMOD, Washington D.C., 1993. Google ScholarDigital Library
- 30.Hellersteln, J.M, Predicate Migration placement. In Proe. of ACM SIGMOD, Minneapolis, 1994.Google Scholar
- 31.Hong, W., Stonebraker, M. Optimization of Parallel Query Execution Plans in XPRS. In Proe. of Conference on Parallel and Distributed Information Systems. 1991. Google ScholarDigital Library
- 32.Hong, W, Parallel Query Processing Using Shared Memory Multlproeessors and Disk Arrays. Ph.D. Thesis, University of California, Berkeley, 1992.Google Scholar
- 33.loannidis, Y,, Ng, R.T,, Shim, K., Sellis, T. Parametric Query Optimization. In Proe. of VLDB, Vancouver, 1992. Google ScholarDigital Library
- 34.loannldls, Y,E, Universality of Serial Histograms. In Proe. of VLDB, Dublin, ireland, 1993. Google ScholarDigital Library
- 35.Klm, W, On Optimizing an SQL-like Nested Query. ACM TODS, Vol 9, No, 3, 1982. Google ScholarDigital Library
- 36.Levy, A,, Mumiek, I,S., $agiv, Y. Query Optimization by Predicate Move.Around, In Proe. of VLDB, Santiago, 1994. Google ScholarDigital Library
- 37.Lohman, G.M, Grammar-like Functional Rules for Representing Query Optimization Alternatives. In Proe. of ACM SIGMOD, 1988, Google ScholarDigital Library
- 38.Lohman, G,, Mohan, C,, Haas, L., Daniels, D., Lindsay, B., Selinger, P,, Wilms, P. Query Processing in R*. In Query Processing in Database Systems. Springer Verlag, 1985.Google ScholarCross Ref
- 39.Maekcrt, L,F,, Lohman, G.M. R* Optimizer Validation and Performance Evaluation For Distributed Queries. In Readings in Database Systems, Morgan Kaufman. Google ScholarDigital Library
- 40.Maekert, L,F,, Lohman, G.M. R* Optimizer Validation and Performance Evaluation for Local Queries. In Proe. of ACM SIGMOD, 1986, Google ScholarDigital Library
- 41.Melton, J,, Simon A, Understanding The New SQL: A Complete Guide, Morgan Kaufman, Google ScholarDigital Library
- 42.Mumiek, I,S,, Finkelstein, S., Pirahesh, H., Ramakrishnan, R. Magic is Relevant. In Proe. of ACM SIGMOD, Atlantic City, 1990, Google ScholarDigital Library
- 43.Mumick, I.S., Pimhesh, H. Implementation of Magic Sets in a Relational Database System. In Proe. of ACM SIGMOD, Montreal, 1994. Google ScholarDigital Library
- 44.Muralikrishna, M. Improved Unnesting Algorithms for Join Aggregate SQL Queries. In Pro(::. of VLDB, Vancouver, 1992. Google ScholarDigital Library
- 45.Muralikrishna M., Dewitt D.J. Equi-Depth Histograms for Estimating Selectivity Factors for Multi-Dimensional Queries, Proe. of ACM SIGMOD, Chicago, 1988.Google Scholar
- 46.Ono, K., Lohman, G.M. Measuring the Complexity of Join Enumeration in Query Optimization. In Proe. of VLDB, Brisbane, 1990. Google ScholarDigital Library
- 47.Ozsu M.T., Valduriez, P. Principles of Distributed Database Systems. Prentice-Hall, 1991. Google ScholarDigital Library
- 48.Piatetsky-Shapiro, G., Connell, C. Accurate Estimation of the Number of Tuples Satisfying a Condition. In Proe. of ACM SIGMOD, 1984. Google ScholarDigital Library
- 49.Pirahesh, H., Hellerstein J.M., Hasan, W. F.xtensible/Rule Based Query Rewrite Optimization in Starburst. In Proe. of ACM SIGMOD 1992. Google ScholarDigital Library
- 50.Poosala, V., loannidis, Y., Haas, P., Shekita, E. Improved Histograms for Selectivity Estimation. In Proc. of ACM SIGMOD, Montreal, Canada 1996. Google ScholarDigital Library
- 51.Poosala, V., Ioannidis, Y.E. Selectivity Estimation Without the Attribute Value Independence Assumption. In Proe. of VLDB, Athens, 1997. Google ScholarDigital Library
- 52.Poosala, V., loannidis, Y.E., Haas, PJ., Shekita, E.J. Improved Histograms for Selectivity Estimation of Range Predicates In Proe. of ACM SIGMOD, Montreal, 1996. Google ScholarDigital Library
- 53.Rosenthal, A., Galindo-Legaria, C. Query Graphs, Implementing Trees, and Freely Reorderable Outerjoins. In Proe. of ACM SIGMOD, Atlantic City, 1990. Google ScholarDigital Library
- 54.Schneider, D.A. Complex Query Processing in Multiprocessor Database Machines. Ph.D. thesis, University of Wisconsin, Madison, Sept. 1990. Computer Sciences Teehaieal Report 965. Google ScholarDigital Library
- 55.Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price T.G. Access Path Selection in a Relational Database System. In Readings in Database Systems. Morgan Kaufman. Google ScholarDigital Library
- 56.Seshadri P., et al. Cost Based Optimization for Magic: Algebra and Implementation. In Proe. of ACM SIGMOD, Montreal, 1996. Google ScholarDigital Library
- 57.Seshadri, P., Pirahesh, H., Leung, T.Y.C. Decorrelating complex queries. In Proe. of the IEEE International Conference on Data Engineering, 1996.Google ScholarCross Ref
- 58.Simmen, D., Shekita E., Malkemus T. Fundamental Techniques for Order Optimization. In Proe. of ACM SIGMOD, Montreal, 1996. Google ScholarDigital Library
- 59.Srivastava D., Dar S., Jagadish H.V., Levy A.: Answering Queries with Aggregation Using Vie,vs. Proe. of VLDB, Mumbai, 1996. Google ScholarDigital Library
- 60.Yah, Y.P., Larson P.A. Eager aggregation and lazy aggregation. In Pro(::. of VLDB Conference, Zurich, 1995. Google ScholarDigital Library
- 61.Yang, H.Z., Larson P.A. Query Transformation for PSJ-Queries. In Proe. of VLDB, 1987. Google ScholarDigital Library
Index Terms
- An overview of query optimization in relational systems
Recommendations
Query processing over object views of relational data
This paper presents an approach to object view management for relational databases. Such a view mechanism makes it possible for users to transparently work with data in a relational database as if it was stored in an object-oriented (OO) database. A ...
Query optimization in multidatabase systems
CASCON '92: Proceedings of the 1992 conference of the Centre for Advanced Studies on Collaborative research - Volume 2A multidatabase system (MDBS) integrates information from autonomous local databases managed by heterogeneous database management systems (DBMS) in a distributed environment. For a query involving more than one database, global query optimization should ...
Comments