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Indexing for data models with constraints and classes (extended abstract)
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Source Symposium on Principles of Database Systems archive
Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems table of contents
Washington, D.C., United States
Pages: 233 - 243  
Year of Publication: 1993
ISBN:0-89791-593-3
Authors
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 3,   Downloads (12 Months): 20,   Citation Count: 40
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ABSTRACT

We examine I/O-efficient data structures that provide indexing support for new data models. The database languages of these models include concepts from constraint programming (e.g., relational tuples are generalized to conjunctions of constraints) and from object-oriented programming (e.g., objects are organized in class hierarchies). Let n be the size of the database, c the number of classes, B the secondary storage page size, and t the size of the output of a query. Indexing by one attribute in the constraint data model (for a fairly general type of constraints) is equivalent to external dynamic interval management, which is a special case of external dynamic 2-dimensional range searching. We present a semi-dynamic data structure for this problem which has optimal worst-case space O(n/B) pages and optimal query I/O time O(logBn+t/B) and has O(logBn+(log2Bn)/B) amortized insert I/O time. If the order of the insertions is random then the expected number of I/O operations needed to perform insertions is reduced to O(logBn). Indexing by one attribute and by class name in an object-oriented model, where objects are organized as a forest hierarchy of classes, is also a special case of external dynamic 2-dimensional range searching. Based on this observation we first identify a simple algorithm with good worst-case performance for the class indexing problem. Using the forest structure of the class hierarchy and techniques from the constraint indexing problem, we improve its query I/O time from O(log2c logBn + t/B) to O(logB + log2B).


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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R. Bayer and E. McCreight, "Organization of Large Ordered Indexes," A cta Informatica 1 (1972), 173-189.
 
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Y.-J. C'hiang and R. Tamassia, "Dynamic Algorithms in Computational Geometry," Proceedings of IEEE, Special Issue on Computational Geometry 80(9)(1992), 362-381.
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E. M. McCreight, "Priority Search Trees," SIAM Journal of Computing 14(2)(May 1985), 257-276.
 
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CITED BY  40

Collaborative Colleagues:
Paris C. Kanellakis: colleagues
Sridhar Ramaswamy: colleagues
Darren E. Vengroff: colleagues
Jeffrey S. Vitter: colleagues