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Database Systems

Query optimization in database systems

Profile image of Nisar AhmedNisar Ahmed

ACM Computing Surveys (CSUR)

https://doi.org/10.1145/356924.356928
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Abstract

Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and ...

Figures (8)
Figure 2. An operator graph representing the example query. responding to existentially quantified var- iables), constants, blanks, and tags (indi- cating the range relation). subset of relational calculus queries, char- acterized by containing only AND-con- nected terms and no universal quantifiers. Thus tableau queries are a particular kind of conjunctive queries [Chandra and Merlin 1977; Rosenkrantz and Hunt 1980]. Nohlaaina aro onanialivand matrinrac tha Expressions containing disjunction (set union) and negation (set difference) can be represented by sets of tableaus [Sagiv and Yannakakis 1980]. Klug [1983] and John- son and Klug [1983] use sets of tableaus
Figure 2. An operator graph representing the example query. responding to existentially quantified var- iables), constants, blanks, and tags (indi- cating the range relation). subset of relational calculus queries, char- acterized by containing only AND-con- nected terms and no universal quantifiers. Thus tableau queries are a particular kind of conjunctive queries [Chandra and Merlin 1977; Rosenkrantz and Hunt 1980]. Nohlaaina aro onanialivand matrinrac tha Expressions containing disjunction (set union) and negation (set difference) can be represented by sets of tableaus [Sagiv and Yannakakis 1980]. Klug [1983] and John- son and Klug [1983] use sets of tableaus
Cyclic expressions are the complement o ree expressions with respect to the entir at of expressions. Although we quote ome benign exceptions in Section 3.: yclic expressions in general cannot be full duced by means of semijoins [Bernstei: nd Goodman 1981a, 1981b; Goodman an hmueli 1982].
Cyclic expressions are the complement o ree expressions with respect to the entir at of expressions. Although we quote ome benign exceptions in Section 3.: yclic expressions in general cannot be full duced by means of semijoins [Bernstei: nd Goodman 1981a, 1981b; Goodman an hmueli 1982].

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References (224)

  1. AHO, A. V., BEERI, C., AND ULLMAN, J. D. 1979a. The theory of joins in relational databases. ACM Trans. Database Syst. 4, 3 (Sept.), 297-314.
  2. AHO, A. V., SAGIV, Y., AND ULLMAN, J. D. 1979b. Efficient optimization of a class of relational expressions. ACM Trans. Database Syst. 4, 4 (Dec.), 435-454.
  3. AHO, A. V., SAGIV, Y., AND ULLMAN, J. D. 1979c. Equivalences among relational expressions.
  4. SIAM J. Comput. 8, 2,218-246.
  5. ANDLER, S., DING, I., ESWARAN, K., HAUSER, C., KIM, W., MEHL, J., AND WILLIAMS, R. 1982. System D: A distributed system for availability. In Proceedings of the 8th International Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Saratoga, Calif., pp. 33-44.
  6. APERS, P. M. G., HEVNER, A. R., AND YAO, S. B. 1983. Optimization algorithms for distributed queries. IEEE Trans. Softw. Eng. SE-g, 1,5768.
  7. ASTRAHAN, M. M., AND CHAMBERLIN, D. D. 1975. Implementation of a structured English query language. Commun. ACM 18, 10 (Oct.), 580-588.
  8. ASTRAHAN, M. M., AND GHOSH, S. P. 1974. A search path selection algorithm for the Data Independ- ent Accessing Model (DIAM). In Proceedings of the ACM-SZGMOD Workshop on Data Descrip- tion, Access and Control (Ann Arbor, Mich., May l-3). ACM, New York, pp. 367-388.
  9. ASTRAHAN, M. M., SCHKOLNICK, M., AND KIM, W. 1980. Performance of the System R access path selection algorithm. In Information Processing 80.
  10. BANCILHON, F., RICHARD, P., AND SCHOLL, M. 1982. On line processing of compacted relations. In Proceedings of the 8th International Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Saratoga, Calif., pp. 263-269.
  11. BANERJEE, J., AND HSIAO, D. K. 1979. DBC-A database computer for very large databases. IEEE Trans. Comput. C-28,6,414-429.
  12. BATORY, D. S. 1982. Index selection. In Principles of Database Design, S. B. Yao, Ed. Springer, New
  13. York. BAYER, R., AND MCCREIGHT, E. 1972. Organization and maintenance of large ordered indexes. Acta Znf. 1, 173-189.
  14. BAYER, R., ELHARDT, K., KIESSLING, W., AND KIL- LAR, D. 1984. Verteilte Datenbanksysteme. Znf. Spektrum 7, 1, 1-19.
  15. BENTLEY, J. L., AND FRIEDMAN, J. H. 1979. Data structures for range searching. ACM Comput. Sum. 11, 4 (Dec.), 397-409.
  16. BERNADAT, P. 1983. Decomposition et evaluation de questions dans une machine base de don&es relationnelles. These IIIme cycle, Department d'Informatique, Universite de Paris VI, Paris, France. BERNSTEIN, P. A., AND CHIU, D. M. W. 1981. Using semi-joins to solve relational queries. J. ACM 28, 1 (Jan.), 25-40.
  17. BERNSTEIN, P. A., AND GOODMAN, N. 1981a. The power of natural semijoins. SIAM J. Comput. 10, 4,751-771.
  18. BERNSTEIN, P. A., AND GOODMAN, N. 1981b. The power of inequality semijoins. Znf. Syst. 6,4,255- 265. l M. Jarke and J. Koch BERNSTEIN, P. A., AND GOODMAN, N. 1981c. Concurrency control in distributed database sys- tems. ACM Comput. Surv. 13, 2 (June), 185-221.
  19. BERNSTEIN, P. A., BLAUSTEIN, B. T., AND CLARKE, E. M. 1980. Fast maintenance of semantic in- tegrity assertions using redundant aggregate data. In Proceedings of the 6th International Conference on Very Large Data Bases (Montreal, Oct. l-3). IEEE, New York, pp. 126-136.
  20. BERNSTEIN, P. A., GOODMAN, N., WONG, E., REEVE, C. L., AND ROTHNIE, J. B., JR. 1981. Query processing in a system for distributed databases (SDD-1). ACM Trans. Database Syst. 6, 4 (Dec.), 602-625.
  21. BERRA, B., AND OLIVER, E. 1979. The role of asso- ciative array processors in database machine ar- chitectures. IEEE Comput. 12, 3, 53-61.
  22. BITTON, D., BORAL, H., DEWI~, D., AND WILKIN- SON, W. K. 1983. Parallel algorithms for the execution of relational database operations. ACM Trans. Database Syst. 8, 3 (Sept.), 324-353.
  23. BLASGEN, M. W., AND ESWARAN, K. P. 1976. On the evaluation of queries in a relational data base system. IBM Res. Rep. RJ 1745, IBM Research Laboratories, San Jose, Calif.
  24. BLASGEN, M. W., AND ESWARAN, K. P. 1977. Storage and access in relational databases. IBM Syst. J. 16, 363-377.
  25. BOLOUR, A. 1981. Optimal retrieval for small range queries. SIAM J. Comput. 10, 4, 721-741.
  26. BREITBART, Y., AND REITER, A. 1975. Algorithms for fast evaluation of Boolean expressions. Acta Znf 4, 107-116.
  27. BRODIE, M., MYLO~OULOS, J., AND SCHMIDT, J. W., Eds. 1984. On Conceptual ModelZing. Perspec- tives from Artificial Intelligence, Databases, and Programming Languages. Springer, New York.
  28. BUNEMAN, P. 1979. The problem of multiple paths in a database schema. In Proceedings of the 5th International Conference on Very Large Data Bases (Rio de Janeiro, Oct. 3-5). IEEE, New York, pp. 368-372.
  29. CARDENAS, A. F. 1975. Analysis end performance of inverted data base structures. Commun. ACM 18, 5 (May), 253-263.
  30. CARLIS, J. V., MARCH, S. T., AND DICKSON, G. W. 1981. Physical database design: A DSS ap- proach. In Proceedings of the 2nd International Conference on Information Systems (Boston, Mass.). ACM, New York, pp. 153-172.
  31. CARLSON, C. R., AND KAPLAN, R. S. 1976. A gener- alized access path model and its application to a relational data base system. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Washington, D.C., June 2-
  32. CERI, S., AND PELAGA~I, G. 1982. Allocation of operations in distributed database access. IEEE Trans. Comput. C-31, 2,119-128.
  33. CHAMBERLIN, D. D., ASTRAHAN, M. M., KING, W. F., LORIE, R. A., MEHL, J. W., PRICE, T. G., SCHKOLNICK, M., SELINGER, P. G., SLUTZ, D. R.. WADE. B. W.. AND YOST, R. A. 1981. Support for repetitive transactions and ad hoc queries in System R. ACM Trans. Database Syst. 6, 1 (Mar.), 70-94.
  34. CHAN, A., AND NIAMIR, B. 1982. On estimating cost of accessing records in blocked database organi- zations. Comput. J. 25, 3, 368-374.
  35. CHAN, A., DAYAL, U., Fox, S., GOODMAN, N., RIES, D. D., AND SKEEN, D. 1983. Overview of an Ada compatible distributed database manager. In SZGMOD 83, Proceedings of the Annual Meeting (San Jose, Calif., May 23-26). ACM, New York, pp. 228-237.
  36. CHANDRA, A. K., AND HAREL, D. 1982a. Structure and complexity of relational queries. J. Comput. Syst. Sci. 25, 99-128.
  37. CHANDRA, A. K., AND HAREL, D. 1982b. Horn clauses and the fixpoint query hierarchy. In Pro- ceedings of the ACM Symposium on Principles of Database Systems (Los Angeles, Calif., Mar. 29- 31). ACM, New York, pp. 158-163.
  38. CHANDRA, A. K., AND MERLIN, P. M. 1977. Optimal implementation of conjunctive queries in rela- tional data bases. In Proceedings of the 9th An- nual ACM Symposium on Theory of Computation (Boulder, Colo., May 24). ACM, New York, pp. 77-90. CHANG, C. L. 1978. DEDUCE 2: Further investiga- tions of deduction in relational databases. In Logic and Databases, H. Gallaire and J. Minker, Eds. Plenum, New York, pp. 210-236.
  39. CHANG, C. L. 1979. On evaluation of queries con- taining derived relations in a relational data base. IBM Res. Rep. RJ2667, IBM Research Labora- tories, San Jose, Calif.
  40. CHANG, J.-M. 1982. A heuristic approach to distrib- uted query processing. In Proceedings of the 8th Znternational Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Sara- toga, Calif., pp. 54-61.
  41. CHEN, P. P. S., AND AKOKA, J. 1980. Optimal design of distributed information systems. IEEE Trans. Comput. C-29, 12,1068-1080.
  42. CHESNAIS, A., GELENBE, E., AND MITRANI, I. 1983. On the modeling of parallel access to shared data. Commun. ACM 26.3 (Mar.), 196-202.
  43. CHEUNG, T.-Y. 1982a. A method for equijoin queries in distributed relational databases. IEEE Trans. Comput. C-31,8, 746-751.
  44. CHEUNG, T.-Y. 1982b. Estimating block accesses and number of records in file management. Com- mun. ACM 25, 7 (July), 484-487.
  45. CHIU, D. M., AND Ho, Y. C. 1980. A methodology for interpreting tree queries into optimal semi- join expressions. In Proceedings of the ACM- SZGMOD Znternational Conference on Manage- ment of Data (Santa Monica, Calif., May 14-16). ACM, New York, pp. 169-178.
  46. CHIU, D. M., BERNSTEIN, P. A., AND Ho, Y. C. 1981. Optimizing chain queries in a distributed database system. Tech. Rep. TR-01-81, Computer
  47. Computing Surveys, Vol. 16, No. 2, June 1984 Query Optimization in Database Systems l 145 Science Dept., Harvard University, Cambridge, Mass. CHRISTODOULAKIS, S. 1981. Estimating selectivities in data bases. Tech. Rep. CSRG-136, Computer Science Dept., University of Toronto, Toronto, Canada. CHRISTODOULAKIS, S. 1983. Estimating block trans- CCAdl-11, Computer Corporation of America, Cambridge, Mass.
  48. DEMOLOMBE, R. 1980. Estimation of the number of tuples satisfying a query expressed in predicate fers and join sizes. In SIGMOD 83, Proceedings of the Annual Meeting (San Jose. Calif., Mav 23-
  49. ACM, New York: pp. 40-54.' - CHU, W. W., AND HURLEY, P. 1982. Optimal query processing for distributed database systems. IEEE Trans. Comput. C-31,9,835-850.
  50. CLAUSEN, S. E. 1980. Optimizing the evaluation of calculus expressions in a relational database sys- tem. Znf. Syst. 5, 1,41-54.
  51. CODD, E. F. 1971. A database sublanguage founded on the relational calculus. In Proceedings of the ACM-SIGFIDET Workshop, Data Description, Access, and Control (San Diego, Calif., Nov. ll-
  52. CODD, E. F. 1972. Relational completeness of data base sublanguages. In Courant Computer Science Symposia No. 6: Data Base Systems. Prentice- Hall, New York, pp. 67-101.
  53. DANIELS, D. 1982. Query compilation in a distrib- uted database system. IBM Res. Rep. RJ3423, IBM Research Laborator+, San Jose, Calif.
  54. DANIELS, D., SELINGER, S., HAAS, L., LINDSAY, B., MOHAN, C., WALKER, A., AND WILMS, P. 1982. An introduction to distributed query com- Dilation in R*. In Proceedings of the 2nd Svmpos- iurn on Distributed Databask iBerlin, FRG): El- sevier North-Holland, New York.
  55. DAVIS, H. W., AND WINSLOW, L. E. 1982. Computational power in query languages. SIAM J. Comput. 11, 3,547-554.
  56. DAVIS, L. S., AND KUNII, T. L. 1982. Pattern data- bases. In Data Base Design Technioues II, S. B. Yao and T. L. Kunii, Ed;. Springer, New' York, pp. 357-399.
  57. DAYAL, U. 1983a. Evaluating queries with quanti- fiers: A horticultural approach. In Proceedings of the ACM Symposium on Principles of Database Systems (Atlanta, Ga.). ACM, -New. York, pp. 125-136. DAYAL, U. 1983b. Processing queries over generali- zation hierarchies in a multi-database system. In Proceedings of the 9th International Conference on Very Large Data Bases (Florence, Italy).
  58. VLDB Endowment, Saratoga, Calif., pp. 342-353.
  59. DAYAL, U., AND GOODMAN, N. 1982. Query optimi- zation for CODASYL database systems. In Pro- ceedings of the ACM-SZGMOD International Conference on Management of Data (Orlando, Fla., June 2-4). ACM, New York, pp. 138-150.
  60. DAYAL, U., GOODMAN, N., LANDERS, T. A., OLSON, K.. SMITH. J. M.. AND YEDWAB. L. 1981. Local query optimization in Multibase-A system for heterogeneous distributed databases. Tech. Rep. calculus language. In Proceedings of thesth Con- ference on Very Large Data Bases (Montreal, Oct. i-3). IEEE, New York, pp. 55-63:
  61. DEWITT, D. J. 1979. Query execution in DIRECT. In Proceedings of the ACM-SZGMOD Znterna- tional Conference on Management of Data (Bos- ton, Mass., May 30June 1). ACM, New York, pp. 13-22.
  62. DOWNEY, P. J., SETHI, R., AND TARJAN, R. E. 1980. Variations on the common subexpression problem. J. ACM 27, 4 (Oct.), 758-771.
  63. ECGERS, S. J., AND SHOSHANI, A. 1980. Efficient access of compressed data. In Proceedings of the 6th Zntemational Conference on Very Large Data Bases (Montreal, Oct. l-3). IEEE, New York, pp. 205-211. EPSTEIN, R., AND STONEBRAKER, M. 1980. Analysis of distributed data base processing strategies. In Proceedings of the 6th International Conference on Very Large Data Bases (Montreal, Oct. l-3). IEEE, New York, pp. 92-101.
  64. EPSTEIN, R., STONEBRAKER, M., AND WONG, E. 1978. Distributed query processing in a rela- tional data base system. In Proceedings of the ACM-SZGMOD Znternational Conference on Management of Data (Austin, Tex., May Bl-June
  65. ESCULIER, C., AND CLORIEUX, A. M. 1979. The SIR- IUS-DELTA distributed DBMS. In Proceedings of the International Conference on Entity-Rela- tiomhb Aonroach. P. Chen. Ed. Elsevier North- Holland, New York, pp. 543-551.
  66. ESWARAN, K. P., GRAY, J. N., LORIE, R. A., AND TRAIGER, I. L. 1976. The notions of consistency and predicate locks in a database system. Com- mun. ACM 19, 11 (Nov.), 624-633.
  67. FINKELSTEIN, S. 1982. Common expression analysis in database applications. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Orlando, Fla., June 2-4).
  68. FORKER, H. J. 1982. Algebraical and operational methods for the optimization of query processing in distributed relational database management systems. In Proceedings of the 2nd International Symposium on Distributed Databases (Berlin, FRG). Elsevier North-Holland, New York, pp.
  69. 39-59. GARDARIN, G., VALDURIEZ, P., AND VIEMONT, Y. 1984. Predicate trees: An approach to optimize relational query operations. In Proceedings of the . M. Jarke and J. Koch GAVISH, B., AND SEGEV, A. 1983. Set query optimi- zation in horizontally partitioned distributed da- tabases. Working Paper QM8304, Graduate School of Management, University of Rochester, Rochester, N.Y.
  70. GELENBE, E., AND GARDY, D. 1982. The size of projections of relations satisfying a functional dependency. In Proceedings of the 8th Intema- tional Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Saratoga, Cahf., pp. 325-333.
  71. GILLES, J. H., AND SCHUSTER, S. A. 1975. Query execution and index selection for relational data bases. Tech. Rep. CSRG-53, Computer Science Dept., University of Toronto, Toronto, Ontario.
  72. GOODMAN, N., AND SHMUELI, 0. 1980. Nonreduc- ible database states for cyclic queries. Tech. Rep. TR-15-80, Computer Science Dept., Harvard University, Cambridge, Mass.
  73. GOODMAN, N., AND SHMUELI, 0.1982. Tree queries: A simple class of relational queries. ACM Trans. Database Syst. 7,4 (Dec.), 653-677.
  74. GOTLIEB, L. R. 1975. Computing joins of relations. In Proceedings of the ACM-SZGMOD Znterna- tional Confer&& on Management of Data (San Jose, Calif., May 14-16). ACM, New York, pp. 55-63. GOUDA, M. G., AND DAYAL, U. 1981. Optimal semi- join schedules for query processing in local dis- tributed database systems. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Ann Arbor, M&h., Apr. 29- May 1). ACM, New York, pp. 164-175.
  75. GRANT, J., AND MINKER, J. 1981. Optimization in deductive and conventional relational database systems. In Advances in Database Theory, H. Gallaire. J. Minker. and J.-M. Nicolas. Eds. Plenum; New York, pp. 195-234.
  76. ' GRAY, P. M.D. 1981. Use of automatic programming and simulation to facilitate operations on CO- DASYL databases. In Database, M. P. Atkinson, Ed. Pergamon Infotech, London, pp. 315-369.
  77. GRAY, P. M. D. 1984. Implementing the join opera- tion on CODASYL DBMS. In Databases: Role and Structure, P. M. Stocker, Ed. Cambridge University Press, Cambridge, England.
  78. GRIES, D. 1971. Compiler Construction for Digital Computers. Wiley, New York.
  79. GRIFFETH, N. D. 1978. Nonprocedural query proc- essing for databases with access paths. In Pro- ceedings of the ACM-SZGMOD International Conference on Management of Data (Austin, Tex., May 31-June 2). ACM, New York, pp. 160-168.
  80. GRIFFITHS, P. G., AND WADE, B. W. 1976. An au- thorization mechanism for a relational database system. ACM Trans. Database Syst. 1, 3 (Sept.), 242-255.
  81. GRISHMAN, R. 1978. The simplification of retrieval requests generated by question-answering sys- tems. In Proceedings of the 4th International Con- ference on Very Large Data Bases (West Berlin, FRG, Sept. 13-15). IEEE, New York, pp. 400- 406. GUDES, E., AND REITER, A. 1973. On evaluating Boolean expressions. Softw. Pratt. Exper. 3, 345- 350. HALL, P. A. V. 1974. Common subexpression iden- tification in general algebraic systems. Tech. Rep. UKSC 0060. IBM UK Scientific Center. Peterlee.
  82. England. HALL, P. A. V. 1976. Optimization of single expres- sions in a relational data base system. IBM J. Res. Devel. 20, 3, 244-257.
  83. HAMMER, M., AND NIAMIR, B. 1979. A heuristic approach to attribute partitioning. In Proceedings of the ACM-SIGMOD International Conference on Management of Data (Boston, Mass., May 30- June 1). ACM, New York, pp. 93-101.
  84. HAMMER, M., AND ZDONIK, S. B., JR. 1980. Knowledge-based query processing. In Proceed- ings of the 6th Znternational Conference on Very Large Data Bases (Montreal, Oct. l-3). IEEE, New York, pp. 137-147.
  85. HANANI, M. Z. 1977. An optimal evaluation of Boo- lean expressions in an online query system. Com- mun. ACM 20, 5 (May), 344-347.
  86. HAWTHORN, P. 1982. Microprocessor assisted tuple access, decompression, and assembly for statisti- cal database systems. In Proceedings of the 8th International Conference on Very Large Data Bases (Mexico Citv). VLDB Endowment. Sara- toga, Calif., pp. 223-233.
  87. HENSCHEN, L. J., AND NAQVI, S. A. 1984. On com- piling queries in recursive first-order databases. J. ACM 31, 1 (Jan.), 47-85.
  88. HEVNER, A. R. 1979. The optimization of query processing on distributed database systems.
  89. Ph.D. dissertation. Comnuter Science Dent.. Pur- due University, West Lafayette, Ind. -' HEVNER, A. R., AND YAO, S. B. 1979. Query proc- essing on a distributed database. IEEE Trans. Softw. Eng. SE-5, 3, 177-187.
  90. HEVNER, A. R., AND YAO, S. B. 1981. Transaction optimization on a distributed database system. Tech. Rep. HR-81-257, Honeywell Corporate Computer Center, Bloomington, Minn.
  91. HSIAO, D. K. 1979. Database machines are coming, database machines are coming. IEEE Comput. 12, 3, 7-9.
  92. IBM CORPORATION 1966. Introduction to IBM di- rect-access storage devices and organization methods. Programming manual GC 20-1649-06, IEEE 1982. Special issue on query optimization. IEEE Database Eng. 5, 3 (Sept.).
  93. JARKE, M. 1984. Common subexpression isolation in multiple query optimization. In Query Processing in Database Systems, W. Kim, D. Reiner, and D. Batory, Eds. Springer, New York.
  94. JARKE, M., AND KOCH, J. 1983. Range nesting: A fast method to evaluate quantified queries. In SIGMOD 83, Proceedings of Annual Meeting (San Jose, Calif., May 23-26). ACM, New York, pp. 196-206. JARKE, M., AND SCHMIDT, J. W. 1981. Evaluation of first-order relational expressions. Tech. Rep. 78, Fachbereich Informatik, Universitaet Ham- burg, Hamburg, FRG.
  95. JARKE, M., AND SCHMIDT, J. W. 1982. Query proc- essing strategies in the PASCAL/R relational database management system. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Orlando, Fla., June 2-4).
  96. JARKE, M., AND VASSILIOU, Y. 1984. Coupling ex- pert systems with database management systems. In Artificial Intelligence Applications for Business, W. Reitman, Ed. Ablex, Norwood, N.J., pp. 6%
  97. JARKE, M., CLIFFORD, J., AND VASSILIOU, Y. 1984. An optimizing Prolog front-end to a relational query system. In SZGMOD 84, Proceedings of Annual Meeting (Boston, Mass., June 18-21).
  98. JOHNSON, D. S., AND KLUG, A. 1982. Testing con- tainment of conjunctive queries under functional and inclusion dependencies. In Proceedings of the ACM Symposium on Principles of Database Sys- tems (Los Angeles, Calif., Mar. 29-31). ACM, New York, pp. 164-169.
  99. JOHNSON, D. S., AND KLUG, A. 1983. Optimizing conjunctive queries that contain untyped varia- bles. SIAM J. Comput. 12, 4, 616640.
  100. JOHNSTON, H. R., SCHWEITZER, J. E., AND WARKEN- TINE, E. R. 1983. A DBMS facility for handling structured engineering entities. In Proceedings of the Database Week Engineering Design Applica- tions Conference (San Jose, Calif.). ACM, New York, pp. 3-12.
  101. KAMBAYASHI, Y., AND YOSHIKAWA, M. 1983. Query processing utilizing dependencies and horizontal decompo&ion. In SZG%fOD 8.3 Proceedings of the Annual Meetine (San Jose. Calif.. Mav 23-26).
  102. - KAMBAYASHI, Y., YOSHIKAWA, M., AND YAJIMA, S. 1982. Query processing for distributed data- bases using generalized semi-joins. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Orlando, Fla., June 2-
  103. KATZ, R. H., AND WONG, E. 1982. Decompiling CO- DASYL DML into relational queries. ACM Trans. Database Syst. 7, 1 (Mar.), l-23.
  104. KELLOGG, C. 1982. A practical amalgam of knowl- edge and data base technology. InProceedings of the National Conference on Artificial Intelligence (Pittsburgh, Pa.): AAAI, Menlo Park, Calif, KERSCHBERG, L., TING, P. D., AND YAO, S. B. 1982. Query optimization in star computer net- works. ACM Trans. Database Syst. 7, 4 (Dec.), 678-711.
  105. KIM, W. 1980. A new way to compute the product and join of relations. In Proceedings of the ACM- SZGMOD Znternatianal Conference on Manage- ment of Data (Santa Monica, (Calif., May 14-16). ACM, New York, pp. 179-187.
  106. KIM, W. 1981. Query optimization for relational da- tabase systems. IBM Res. Rep. RJ3081, IBM Research Laboratories, San Jose, Calif.
  107. KIM, W. 1982. On optimizing an SQL-like nested query. ACM Trans. Database Syst. 7, 3 (Sept.), pp. 443-469.
  108. KIM, W. 1984. Global optimization of relational queries: A first step. In Query Processing in Da- tabase Systems, W. Kim, D. Reiner, and D. Ba- tory, Eds. Springer, New York.
  109. KIM, W., KUCK, D. J., AND GARSKI, D. 1981. A bit- serial/tuple-parallel relational query processor.
  110. IBM Res. Rep. RJ3194, IBM Research Labora- tories, San Jose, Calif.
  111. KING, J. J. 1979. Exploring the use of domain knowl- edge for query processing efficiency. Tech. Rep. STAN-CS-79-781, Computer Science Dept., Stanford University, Stanford, Calif.
  112. KING, J. J. 1981. QUIST: A system for semantic query optimization in relational databases. In Proceedings of the 7th International Conference on Very Large Data Bases (Cannes, Sept. 9-11). IEEE, New York, pp. 510-517.
  113. KLUG, A. 1980. Calculating constraints on relational expressions. ACM Trans. Database Syst. 5, 3 (Sept.), 260-290.
  114. KLUG, A. 1982a. Equivalence of relational algebra and relational calculus query languages having aggregate functions. J. ACM 29, 3 (July), 699- 717.
  115. KLUG, A. 1982b. Access paths in the "ABE" statis- tical query facility. In Proceedings of the ACM- SZGMOD International Conference on Manage- ment of Data (Orlando, Fla., June 2-4). ACM, New York, pp. 161-173.
  116. KLUG, A. 1983. Locking expressions for increased database concurrency. J. ACM 30, 1 (Jan.), 36-
  117. KOCH, J., SCHMIDT, J. W., AND WUNDERLICH, V. 1981. Type derivation for first-order relational expressions. Tech. Rep. no. 79, Fachbereich In- formatik, Universitiit Hamburg, Hamburg, FRG.
  118. KOWALSKI, R. 1981. Logic as a database language. Unpublished manuscript, Computer Science Dept., Imperial College, London.
  119. KUNIFUJI, S., AND YOKOTA, H. 1982. Prolog and relational databases for Fifth Generation Com- puter Systems. In Proceedings of the Workshop on Loeical Bases for Data Bases (Toulouse, France?. ONERA-CERT, Toulouse, France. LAMERSDORF, W. 1984. Recursive data models for non-conventional database applications. In Pro- ceedings of the IEEE COMPDEC Conference (Los Angeles, Calif.). IEEE, New York.
  120. LANG, T., WOOD, C., AND FERN~NDEZ, I. B. 1977. Database buffer paging in virtual storage systems. ACM Trans. Database Syst. 2, 4 (Dec.), 339-351.
  121. LANGDON, G. G. 1979. Database machines: an intro- duction. IEEE Trans. Comput. C-28,6,381-383.
  122. l M. Jarke and J. Koch LEILICH, H.-O., STIEGE, G., AND ZEIDLER, H. C. MARYANSKI, F. J. 1980. Backend database systems. 1978. A search processor for data base manage- ACM Comput. Surv. 1.2, 1 (Mar.), 3-25. ment systems. In Proceedings of the 4th Znterna- ti0na.l Conference on Very Large Data Bases (West MENON, M. J., AND HSIAO, D. K. 1981. Design and Berlin, FRG, Sept. 13-15). IEEE, New York, pp. analysis of a relational join operation for VLSI.
  123. 286-287. In Proceedings of the 7th International Conference on Very Large Data Bases (Cannes, Sept. 9-11).
  124. LIN, C. S., SMITH, D. C. P., AND SMITH, J. M. IEEE, New York, pp. 44-55. 1976. The design of a rotating associative mem- ory for relational database applications. ACM MERRETT, T. H. 1977. Database cost analysis: Atop- Trans. Database Syst. 1, 1 (Mar.), 53-65. down approach. In Proceedings of the ACM-SZG- MOD International Conference on Management LIU, J. W. S. 1976. Algorithms for parsing search of Data (Toronto, Canada, Aug. 3-5). ACM, New queries in systems with inverted file organiza- York, pp. 135-143. tions. ACM Trans. Database Syst. 1, 4 (Dec.), 299-316. MERRETP, T. H. 1981. Why sort-merge gives the best implementation of the natural join. SZG- LUK, W. S. 1983. On estimating block accesses in MOD Rec. 13, 2,39-51.
  125. database organizations. Commun. ACM 26, 11 M (Nov.), 945-947.
  126. ERRETT, T. H., KAMBAYASHI, Y., AND YASUURA, H. 1981. Scheduling of page-fetches in join opera- MAEKAWA, M. 1982. Parallel join and sorting algo- tions. In Proceedings of the 7th Znternatianal Con- rithms. In Data Base Design Techniques ZZ, S. B. ference on Very Large Data Bases (Cannes, Sept.
  127. Yao and T. L. Kunii, Eds. Springer, New York, 9-11). IEEE, New York, pp. 488-498. pp. 266-296.
  128. MINKER, J. 1975. Performing inferences over rela- MAHMOUD, S. A., RIORDON, J. S., AND TOTH, K. C. tion data bases. In Proceedings of the ACM- 1979. Database partitioning and query process- SZGMOD Zntematianal Conference on Manage- ing. In Proceedings of the ZFZP Working Confer- ment of Data (San Jose, Calif., May 14-16). ACM, ence on Database Architecture. Elsevier North- New York, pp. 79-91.
  129. Holland, New York, pp. 3-21.
  130. MINKER, J. 1978. Search strategy and selection func- MAIER, D. 1983. The Theory of Relational Databases. tion for an inferential relational system. ACM Computer Science Press, Rockville, Md. Trans. Database Syst. 3, 1 (Mar.), 1-31.
  131. MAIER, D., AND ULLMAN, J. D. 1983. Fragments of MINKER, J., AND NICOLAS, J.-M. 1983. On recursive relations. In SZGMOD 33, Proceedings of the An- axioms in deductive databases. Znf. Syst. 8, 1, l- nual Meeting (San Jose, Calif., May 23-26).
  132. ACM, New York, pp. 15-22. MISSIKOFF, M., AND SCHOLL, M. 1983. Relational MAIER, D., AND WARREN, D. S. 1981. Incorporating queries in domain based DBMS. In SZGMOD 63, computed relations in relational databases. In Proceedings of Annual Meeting (San Jose, Calif., Proceedings of the ACM-SZGMOD International May 23-26). ACM, New York, pp. 219-227. Conference on Management of Data (Ann Arbor, MONTGOMERY, A. I., D'SOUZA, D. J., AND LEE, S. B. Mich., Apr. 29-May 1). ACM, New York, pp. 1983. The cost of relational algebraic operations 176-187. in skewed data: Estimates and experiments. In MAKINOUCHI, A., TEZUKA, M., KITAKAMI, H., AND Information Processing 83. Elsevier North-Hol-
  133. ADACHI, S. 1981. The optimization strategy for land, New York, pp. 235-241.
  134. query evaluation in RDB/Vl. In Proceedings of MUNZ, R. R. 1979. Gross architecture of the distrib- the 7th International Conference on Very Large uted database system. VDN. In Proceedings of the Data Bases (Cannes, Sept. 9-11). IEEE, New ZFZP Working Conference on Database Architec- York, pp. 518-529. ture. Elsevier North-Holland, New York, pp. 23- MALL, M., REIMER, M., AND SCHMIDT, J. W. 34. 1984. Data selection, sharing and access control MUNZ, R. R., SCHNEIDER, H.-J., AND STEYER, F. in a relational scenario. In On Conceptual Mod- 1979. Application of sub-predicate tests in da- cling. Perspectives from Artificial Intelligence, Da- tabase systems. In Proceedings of the 5th Znter- tabases, and Programming Languages, M. Brodie, national Conference on Very Large Data Bases J. Mylopoulos, and J. W. Schmidt, Eds. Springer, (Rio de Janeiro, Oct. 3-5). IEEE, New York, pp.
  135. MARBURGER, H., AND NEBEL, B. 1983. Natuer- MUTHUSWAMY, B., AND KERSCHBERG, L. 1983. lichsprachlicher Datenbankzugang mit HAM- Distributed query optimization using detailed da- ANS: Syntaktische Korespondenz, natuerlich- tabase statistics. Unpublished manuscript, Com- sprachliche Quantifizienmg und semantisches puter Science Dept., University of South Caro- Model1 des Diskursbereichs. In Sprachen fuer lina.
  136. Datenbanken, J. W. Schmidt, Ed. Springer-Ver- NAU, D. 1983. Expert computer systems. IEEE Com- lag, Berlin, pp. 26-41. put. 16, 2 (Feb.), 63-85.
  137. MARCH, S. T. 1983. A mathematical programming NEUHOLD, E. J., AND BILLER, H. 1977. POREL: A approach to the selection of access paths for large distributed data base on an inhomogeneous com- multiuser data bases. Decision Sci. 14,4,564-587. puter network. In Proceedings of the 3rd Znter- Computing Surveys, Vol. 16, No. 2, June 1964 national Conference on Very Large Data Bases (Tokyo, Oct. 6-8). IEEE, New York, pp. 380-395.
  138. NG, P. 1982. Distributed compilation and recompi- lation of distributed queries. IBM Res. Rep. RJ3375, IBM Research Laboratories, San Jose, Calif. NIEBUHR, K. E., AND SMITH, S. E. 1976. N-ary joins for processing Query by Example. IBM Tech. Disclosure Bull. 19, 6,2377-2381.
  139. NIEBUHR, K. E., SCHOLZ, K. W., AND SMITH, S. E. 1976. Algorithm for processing Query by Ex- ample. IBM Tech. Disclosure Bull. 19,2,736-741.
  140. NIEVERGELT, J., HINTERBERGER, H., AND SEVCIK, K. C. 1984. The grid file: An adaptable, symmetric multikey file structure. ACM Trans. Database Syst. 9, 1 (Mar.), 38-71.
  141. NILSSON, N. 1982. Principles of Artificial Zntelli- gence. Springer, New York.
  142. OTT, N. 1977. Interpretation of questions with quan- 'tifiers and negation in the -USL system. IBM Tech. RCTD. TR-77.10965. IBM Scientific Center. Heidelberg, FRG.
  143. ' OTT, N., AND HORLAENDER, K. 1982. Removing re- dundant join operations in queries involving views. IBM Tech. Rep. TR-82.03993, IBM Sci- entific Center, Heidelberg, FRG.
  144. OZKARAHAN, E. A. 1982. Database machine/com- puter based distributed databases. In Proceedings bf the 2nd Znternational Symposium on Distrib- uted Databases (Berlin. FRG). Elsevier North- Holland, New York, pp: 61-86.
  145. OZSOYOGLU, M. Z., AND Ozso~o~~u, G. 1983. An extension of relational algebra for summary ta- bles. In Proceedings of the 2nd Statistical Data- base Workshop (Berkeley, Calif.). University of California, Berkeley.
  146. Ozso~o~~u, M., AND Yu, C. T. 1980. On identifying a class of database queries that can be processed efficiently. In Proceedings of the IEEE COMP- SAC Conference. IEEE, New York, pp. 453-461.
  147. PAIGE, R. 1982. Applications of finite differencing to database integrity control and query/transac- tion optimization. In Proceedings of the Workshop on Logical Bases for Data Bases (Toulouse, France). ONERA-CERT, Toulouse, France.
  148. PALERMO, F. P. 1972. A data base search problem. In Proceedings of the 4th Symposium on Computer and Information Science (Miami Beach, Fla.).
  149. PARSAYE, K. 1983. Database management, knowl- edge base management, and expert system devel- onment in PROLOG. In Proceedings of the Da- tabase Week Conference on Engineering Applica- tions of Databases (San Jose. Calif.). ACM. New York, pp. 159-178.' ' ' ' PECHERER, R. M. 1975. Efficient evaluation of expressions in a relational algebra. In Proceedings of the ACM Pacific 75 Conference (San Francisco, Calif., May 14-16). ACM, New York, pp. 44-49.
  150. PECHERER, R. M. 1976. Efficient exploration of product spaces. In Proceedings of tke ACM-SZG- MOD International Conference on Management of Data (Washington, D.C., June 2-4). ACM, New York, pp. 169-177.
  151. PIROTTE, A. 1979. Findamental and secondary issues in the design of non-procedural relational lan- guages. In Proceedings of the 5th International Conference on Very Large Data Bases (Rio de Janeiro, Oct. 3-5). IEEE, New York, pp. 239-250.
  152. PUTKONEN, A. 1979. On the selection of the access path in inverted database organizations. Znf. Syst. 4, 4, 219-225.
  153. REIMER, M. 1983. Solving the phantom problem by predicative optimistic concurrency control. In Proceedings of the 9th International Conference on Very Large Data Bases (Florence, Italy).
  154. VLDB Endowment, Saratoga, Calif., pp. 81-88.
  155. REITER, R. 1978. Deductive question-answering on relational data bases. In Logic and Databases, H. Gallaire and J. Minker, Eds. Plenum, New York, pp. 149-178.
  156. RICHARD, P. 1981. Evaluation of the size of a query expressed in relational algebra. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Ann Arbor, Mich., Apr. 29- May 1). ACM, New York, pp. 155-163.
  157. ROSENKRANTZ, D. J., AND HUNT, H. B. III. 1989. Processing conjunctive predicates and queries. In Proceedings of the 6th Znternatianal Conference on Very Large Data Bases (Montreal, Oct. l-3). IEEE, New York, pp. 64-72.
  158. ROSENTHAL, A., AND REINER, D. 1982. An architec- ture for.query optimization. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Orlando, Fla.,' June 2-4).
  159. ROSENTHAL, A., AND REINER, D. 1984. Querying relational views of networks. In Query P&es& in Database Systems. W. Kim. D. Reiner. and D. Batory, Eds. ipringer, New York.
  160. ROTHNIE, J. B. 1974. An approach to implementing a relational data management system. In Pro- ceedings of the ACM-SZGMOD Workshop on Data Description, Access, ana' Contml (Ann Arbor, Mich., May l-3). ACM, New York, pp. 277-294.
  161. ROTHNIE, J. B., JR. 1975. Evaluating inter-entry retrieval expressions in a relational data base management system. In Proceedings of the Na- tional Computer Conference (Anaheim, Calif., May lS-22), vol. 44. AFIPS Press, Reston, Va., pp. 417-423.
  162. ROTHNIE, J. B., AND GOODMAN, N. 1977. A survey of research and development in distributed data- base management. In Proceedings of the 3rd Zn- ternational Conference on Very Large Data Bases (Tokyo, Oct. 6-8). IEEE, New York, pp. 48-62.
  163. Rousso~ou~os, N. 1982a. View indexing in rela- tional databases. ACM Trans. Database Syst. 7, 2 (June), 258-290.
  164. l M. Jarke ana! J. Koch ROUSSOPOULOS, N. 1982b. The logical access path schema of a database. IEEE Trans. Softw. Eng. SE-8,6,563-573.
  165. SACCO, G. M., AND SCHKOLNICK, M. 1982. A mech- anism for managing the buffer pool in a relational database system using the hot set model. In Pro- ceedings of the 8th International Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Saratoga, Calif., pp. 257-262.
  166. SACCO, G. M., AND YAO, S. B. 1982. Query optimi- zation in distributed database systems. In Ad- vances in Computers, vol. 21. Academic Press, New York, pp. 225-273.
  167. SAGALOWICZ, D. 1977. IDA: An intelligent data ac- cess program. In Proceedings of the 3rd Interna- tional Conference on Very Large Data Bases (To- kyo, Oct. 6-8). IEEE, New York, pp. 293-302.
  168. SAGIV, Y. 1981. Optimization of Queries in Relational Databases. UMI Research Press, Ann Arbor, Michigan. SAGIV, Y. 1983. Quadratic algorithms for minimizing joins in restricted relational expressions. SIAM J. Comput. 12, 2, 316328.
  169. SAGIV, Y., AND YANNAKAKIS, M. 1980. Equivalences among relational expressions with the union and difference operators. J. ACM 27, 4 (Oct.) 633- 655. SALTON, G., AND WONG, A. 1978. Generation and search of clustered files. ACM Trans. Database Syst. 3, 4 (Dec.) 321-346.
  170. SCHEK, H.-J., AND PISTOR, P. 1982. Data structures for an integrated database management and in- formation retrieval system. In Proceedings of the 8th International Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Sara- toga, Calif., pp. 197-207.
  171. SCHENK, K. L., AND PINKERT, J. R. 1977. An algo- rithm for servicing multi-relational queries. In Proceedings of the ACM-SIGMOD International Conference on Management of Data (Toronto, Canada, Aug. 3-5). ACM, New York, pp. 10-20.
  172. SCHKOLNICK, M. 1975. The optimal selection of in- dexes for files. Znf. Syst. 1, 4, 141-146.
  173. SCHKOLNICK, M. 1982. Physical database design techniques. In Data Base Design Techniques 71, S. B. Yao and T. L. Kunii. Eds. Snrineer. New ' A y ' York, pp. 229-252.
  174. SCHMIDT, J. W. 1977. Some high level language con- structs for data of type relation. ACM Trans. Database Syst. 2, 3 (Sept.), 247-261.
  175. SCHMIDT, J. W. 1979. Parallel processing of rela- tions: A single-assignment approach. In Proceed- ings of the 5th International Conference on Very Large Data Bases (Rio de Janeiro, Oct. 3-5). IEEE, New York, pp. 398-408.
  176. SCHMIDT, J. W. 1984. Database programming: Lan- guage constructs and execution models. In Pro- grammiersprachen und Programmentwicklung, U. Ammann, Ed. Springer-Verlag, Berlin, pp. l-26.
  177. SELINGER, P. G., AND ADIBA, M. 1980. Access path selection in distributed database systems. IBM Res. Rep. RJ2283, IBM Research Laboratories, San Jose, Calif.
  178. SELINGER, P. G., ASTRAHAN, M. M., CHAMBERLIN, D. D., LORIE. R. A.. AND PRICE. T. G. 1979. 'Access path selection in a relational da- tabase management system. In Proceedings of the ACM-SZGMOD International Conference on Management of Data (Boston, Mass., May
  179. SEVERANCE, D. G., AND CARLIS, J. V. 1977. A prac- tical approach to selecting record access paths. ACM Comput. Surv. 9, 4 (Dec.) 259-272.
  180. SHMUELI, 0. 1981. The fundamental role of tree schemas in relational query processing. Ph.D. thesis, Computer Science Dept., Harvard Univ., Cambridge, Mass.
  181. SHNEIDERMAN, B. 1977. Reduced combined indexes for efficient multiple attribute retrieval. Znf. Syst. 2, 4, 149-154.
  182. SHNEIDERMAN, B., AND GOODMAN, V. 1976. Batched searching of sequential and tree struc- tured tiles. ACM Trans. Database Syst. 1, 3 (Sept.), 268-275, SHOSHANI, A. 1982. Statistical database: Character- istics, problems, and some solutions. In Proceed- ings of the 8th International Conference on Very Large Data Bases (Mexico City). VLDB Endow- ment, Saratoga, Calif., pp. 208-222.
  183. SHULTZ, R. K., AND ZINGG, R. J. 1984. Response time analysis of multiprocessor computers for database support. ACM Trans. Database Syst. 9, 1 (Mar.), 100-132.
  184. SMITH, J. M., AND CHANG, P. Y. T. 1975. Optimizing the performance of a relational algebra database interface. Commtin. ACM 18, 10 (Oct.), 568-579.
  185. SMITH, J. M., BERNSTEIN, P. A., DAYAL, U., GOOD- MAN, N., LANDERS, T., LIN, K. W. T., AND WONG, E. 1981. MULTIBASE-Integrating heterogeneous distributed database systems. In Proceedings of the AFZPS National Computer Conference (Chicago, May 4-7), vol. 50. AFIPS Press, Reston, Va., pp. 487-499.
  186. SOCKUT, G. H., AND GOLDBERG, R. P. 1979. Database reorganization-Principles and prac- tice. ACM Comput. Surv. 11, 4 (Dec.) 371-395.
  187. STONEBRAKER, M. 1975. Implementation of integ- rity constraints and views by query modification. In Proceedings of the ACM-SZGMOD Znterna- tional Conference on Management of Data (San Jose, Calif., May 14-16). ACM, New York, pp. 65-78. STONEBRAKER, M., AND NEUHOLD, E. 1977. A dis- tributed database version of INGRES. In Pro- ceedings of the 2nd Berkeley Workshop on Dis- tributed Data Management-and Computer Net- works (Berkeley. Calif.). Universitv of California. Berkeley. - STONEBRAKER, M., WONG, E., KREPS, P., AND HELD, G. 1976. The design and implementation of INGRES. ACM Trans. Database Syst. 1,3 (Sept.), 189-222.
  188. STROET, J. W. M., AND ENGMANN, R. 1979. Manipulation of expressions in a relational alge- bra. Znf. Syst. 4, 4,195-203.
  189. SU, S. Y. W. 1979. Cellular-logic devices: Concepts and applications. IEEE Comput. 12, 3,11-25.
  190. Su, S. Y. W., AND LIPKOVSKI, G. 1975. CASSM: A cellular system for very large databases. In Pro- ceedings of the 1st Znternational Conference on Very Large Data Bases (Framingham, Mass., Sept. 22-24). ACM, New York, pp. 456-472.
  191. Su, S. Y. W., AND MIKKILINENI, K. P. 1982. Parallel algorithms and their implementation in MICRO- NET. In Proceedings of the 8th International Conference on Very Large Data Bases (Mexico City). VLDB Endowment, Saratoga, Calif., pp. 310-324. TANENBAUM, A. 1981. Computer Networks.
  192. TEOREY, T. J., AND FRY, J. P. 1982. Design of Da- tabase Structures. Prentice-Hall, New York.
  193. TODD, S. 1974. Implementing the join operator in relational data bases. IBM Scientific Center Tech. Note 15, IBM UK Scientific Center, Peter- lee, England.
  194. TSICHRITZIS, D. 1976. LSL: A link and selector lan- guage. In Proceedings of the ACM-SZGMOD Zn- ternational Conference on Management of Data (Washington, D.C., June 2:4). ACM, New York, pp. 123-133.
  195. ULLMAN, J. D. 1982. Principles of Database Systems.
  196. VALDURIEZ, P. 1982. Semi-join algorithms for mul- tinrocessor svstems. In Proceedings of the ACM- SZGMOD Zn~ernational Conference on Mannge- ment of Data (Orlando, Fla., June 2-4). ACM, New York, pp. 225-233.
  197. VALDURIEZ, P., AND GARDARIN, G. 1984. Join and semijoin algorithms for a multiprocessor database machine. ACM Trans. Database Syst. 9. 1 (Mar.), 133-161.
  198. VAN DE RIET, R. P., WASSERMAN, A. I., KERSTEN, M. L., AND DE JONGE, W. 1981. High-level pro- gramming features for improving the efficiency of a relational database system. ACM Trans. Da- tabase Syst. 6, 3 (Sept.), 464-485.
  199. VASSILIOU, Y., AND JARKE, M. 1984. Query lan- guages-A taxonomy. In Human Factors and Zn- teractioe Computer Systems, Y. Vassiliou, Ed.
  200. Ablex, Norwood, N.J. VASSILIOU, Y., AND LOCHOVSKY, F. 1980. DBMS transaction translation. In Proceedings of the IEEE COMPSAC Conference. IEEE, New York, pp. 89-96.
  201. VASSILIOU, Y., CLIFFORD, J., AND JARKE, M. 1984. Access to specific declarative knowledge in expert systems: The impact of logic programming. De- cision SupZIort Syst. 1, 1.
  202. VERHOFSTAD, J. S. M. 1978. Recovery techniques for database systems. ACM Comput. Suru. 10, 2 (June), 167-195.
  203. WALKER, A. 1980. On retrieval from a small version of a large data base. In Proceedings of the 6th International Conference on Very Large Data Bases (Montreal, Oct. l-3). IEEE, New York, pp. 47-54.
  204. WARREN, D. H. D. 1981. Efficient processing of in- teractive relational database queries expressed in logic. In Proceedings of the 7th International Con- ference on Very Large Data Bases (Cannes, Sept. '9-11). IEEE, New York, pp. 272-281.
  205. WELCH, J. W., AND GRAHAM, J. W. 1976. Retrieval using ordered lists in inverted and multilist files. In Proceedings of the ACM-SZGMOD Znterna- tional Conference on Management of Data (Wash- ington, D.C., June 2-4). ACM, New York, pp. 21-
  206. WHANG, K.-Y., WIEDERHOLD, G., AND SAGALOWICZ, D. 1983. Estimating block accesses in database organizations: A closed noniterative formula.
  207. Commun. ACM 26, 11 (Nov.), 940-944.
  208. WILLIAMS. R.. DANIELS, D., HAAS, L., LAPIS, G., LINDSAY, R., NG, P., OBERMARCK, R., SELINGER, P.. WALKER. A.. WILMS, P., AND YOST, R. i982. R*: An overview of the architecture. In Proceedings of the International Conference on Database Systems (Jerusalem, Israel).
  209. WONG, E. 1977. Retrieving dispersed data from SDD-1: A svstem for distributed databases. In Proceedings of the Second Berkeley Workshop on Distributed Data Management and Computer Networks (Berkeley, Calif.), pp. 217-235.
  210. WONG, E. 1983. Dynamic rematerialization: Proc- essing distributed queries using redundant data. IEEE Trans. Softw. Eng. SE-g, 3,228-232.
  211. WONG, E., AND KATZ, R. H. 1983. Distributing a database for parallelism. In SZGMOD 83, Pro- ceedings of the Annual Meeting (San Jose, Calif., May 23-26). ACM, New York, pp. 23-29.
  212. WONG, E., AND YOUSSEFI, K. 1976. Decomposi- tion-A strategy for query processing. ACM Trans. Database Syst. 1, 3 (Sept.), 223-241.
  213. XV, G. D. 1983. Search control in semantic query optimization. Tech. Rep. #83-09, Computer and Information Science Dept., University of Massa- chusetts, Amherst, Mass.
  214. YANG, C.-S. 1977. Avoiding redundant accesses in unsorted multilist file organizations. Znf. Syst. 2, 4,155-158.
  215. YAO, S. B. 1977a. Approximating block accesses in database organizations. Commun. ACM 20, 4 (Apr.), 260-261.
  216. YAO, S. B. 1977b. An attribute based model for da- tabase access cost analysis. ACM Trans. Database Syst. 2. 1 (Mar.), 45-67.
  217. YAO, S. B. 1979. Optimization of query evaluation aleorithms. ACM Trans. Database Syst. 4, 2 (June), 133-155.
  218. YAO, S. B., AND DFJONG, D. 1978. Evaluation of database access paths. In Proceedings of the ACM-SZGMOD Znternational Conference on l M. Jarke and J. Koch Management of Data (Austin, Tex., May 31-June
  219. YOIJSSEFI, K., AND WONG, E. 1979. Query process- ing in a relational database management system. In Proceedings of the 5th International Conference on Very Large Data Bases (Rio de Janeiro, Oct.
  220. YU, yu, C. T., AND OZSOYOGLU, M. 1979, An algorithm for tree query membership of a distributed query. In Proceedings of the IEEE COMPSAC Confer- ence. IEEE, New York, pp. 306-312.
  221. C. T., LUK, W. S., AND SIU, M. K. 1976. On the estimation of the number of desired records with respect to a given query. ACM Trans. Database Syst. 3, 1 (Mar.), 41-56.
  222. Yu, C. T., AND CHANG, C. C. 1963. On the design of ZANIOLO, C. 1979. Design of relational views over a query processing strategy in a distributed da- network schemas. In Proceedings of the ACM- tabase environment. In SIGMOD 83, Proceedings SZGMOD International Conference on Manage- of the Annual Meeting (San Jose, Cahf., May 23- ment of Data (Boston, Mass., May BO-June 1).
  223. Received October 1983; final revision accepted April 1984.
  224. Computing Surveys, Vol. 16, No. 2, June 1964

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