The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Heuristics for trie index minimization
ACM Transactions on Database Systems (TODS)
Hashing and trie algorithms for partial match retrieval
ACM Transactions on Database Systems (TODS)
The Complexity of Trie Index Construction
Journal of the ACM (JACM)
Identifier Search Mechanisms: A Survey and Generalized Model
ACM Computing Surveys (CSUR)
Design of tree structures for efficient querying
Communications of the ACM
Use of tree structures for processing files
Communications of the ACM
Communications of the ACM
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
A compendium of key search references
ACM SIGIR Forum
Burst tries: a fast, efficient data structure for string keys
ACM Transactions on Information Systems (TOIS)
Compression techniques for fast external sorting
The VLDB Journal — The International Journal on Very Large Data Bases
Web-Based Document Classification Using a Trie-Based Index Structure
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Engineering scalable, cache and space efficient tries for strings
The VLDB Journal — The International Journal on Very Large Data Bases
Redesigning the string hash table, burst trie, and BST to exploit cache
Journal of Experimental Algorithmics (JEA)
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We develop an efficient approach to Trie index optimization. A Trie is a data structure used to index a file having a set of attributes as record identifiers. In the proposed methodology, a file is horizontally partitioned into subsets of records using a Trie index whose depth of indexing is allowed to vary. The retrieval of a record from the file proceeds by “stepping through” the index to identify a subset of records in the file in which a binary search is performed. This paper develops a taxonomy of optimization problems underlying variable-depth Trie index construction. All these problems are solvable in polynomial time, and their characteristics are studied. Exact algorithms and heuristics for their solution are presented. The algorithms are employed in CRES-an expert system for editing written narrative material, developed for the Department of the Navy. CRES uses several large-to-very-large dictionary files for which Trie indexes are constructed using these algorithms. Computational experience with CRES shows that search and retrieval using variable-depth Trie indexes can be as much as six times faster than pure binary search. The space requirements of the Tries are reasonable. The results show that the variable-depth Tries constructed according to the proposed algorithms are viable and efficient for indexing large-to-very-large files by attributes in practical applications.