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Techniques for the automatic selection of data structures
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Source Annual Symposium on Principles of Programming Languages archive
Proceedings of the 3rd ACM SIGACT-SIGPLAN symposium on Principles on programming languages table of contents
Atlanta, Georgia
Pages: 58 - 67  
Year of Publication: 1976
Authors
Sponsors
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 15,   Citation Count: 2
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ABSTRACT

We are all aware of the development of increasingly sophisticated, elaborate, and expensive computer programs, particularly in the fields of artificial intelligence, data base management, and intelligent systems. The need for techniques to deal with such complexity has renewed interest in programming language research. Recent work on structured programming, intelligent compilers, automatic program generation and verification, and high-level optimization has resulted. A pattern of approach similar to that of earlier research on programming languages is emerging. The work divides naturally into two parts: the search for good linguistic tools for expressing algorithms and data, and the development of practical methods for translating these to working computer programs. Our emphasis in this paper is in the latter.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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B. Baumgart. Micro Planner Alternate Reference Manual. Stanford Artificial Intelligence Laboratory. Operating Note 67, April 1972.
 
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J. Earley. Comments on SETL (Symmetric Use of Relations). SETL Newsletter 52. Courant Institute NYU. Sept. 1971.
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J. Earley. An Overview of the VERS2 Project. Electronic Research Laboratory, College of Engineering memorandum ERL-M416, Dec. 1973, University of California at Berkeley.
 
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J. Earley. High Level Iterators and a Method of Automatically Designing Data Structure Representation. Electronic Research Laboratory, College of Engineering memorandum ERL-M425, Feb. 1974, University of California at Berkeley.
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J. Low. Automatic Coding: Choice of Data Structures. Technical Report No. 1, Computer Science Department, University of Rochester, Rochester, N.Y.
 
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D. McDermott and G. Sussman. The Conniver Reference Manual. AI Memo No. 259, M.I.T., May 1972.
 
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J. Morris. A Comparion of MADCAP and SETL. University of California, Los Alamos Scientific Laboratory, 1973.
 
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P. Rovner. Automatic Selection of Associative Data Structures. Ph.D. thesis, Department of Mathematics, Harvard University (in preparation).
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J. Schwartz. Optimization of Very High Level Languages—I. Value Transmission and its Corollaries. In Computer Languages, Vol. 1, pp. 161-194, Pergamon Press, 1975.
 
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G. Sussman. Why Conniving is Better than Planning. AI Memo 255. M.I.T. Artificial Intelligence Lab, Feb. 1972.
 
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