Burst tries

Heinz, Steffen; Zobel, Justin; Williams, Hugh E.

doi:10.1145/506309.506312

Cited by 111 publications

(48 citation statements)

References 51 publications

(80 reference statements)

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“…Compared to DAs that support O(k) time using 2 pointers, ternary search trees are not competitive. Heinz et al 32 proposed the burst trie that achieves high space efficiency by selectively collapsing chains of trie nodes into small buckets of strings that share a common prefix. Askitis and Sinha 33 proposed the HAT-trie that improves the burst trie using cache-conscious hash tables.…”

Section: Related Workmentioning

confidence: 99%

Practical rearrangement methods for dynamic double‐array dictionaries

et al. 2017

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SummaryDouble-array structures have been widely used to implement dictionaries with string keys. Although the space efficiency of dynamic double-array dictionaries tends to decrease with key updates, we can still maintain high efficiency using existing methods. However, these methods have practical problems of time and functionality. This paper presents several efficient rearrangement methods to solve these problems.Through experiments using real-world datasets, we demonstrate that the proposed rearrangement methods are much more practical than existing methods.

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Section: Related Workmentioning

confidence: 99%

Practical rearrangement methods for dynamic double‐array dictionaries

et al. 2017

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“…A special kind of trie, called the suffix tree [39,47,43,37], can be used to index all suffixes in a text in order to carry out fast full or partial text searches. A basic trie implementation has the disadvantage of having single path which is not space efficient.…”

Section: Tree-structured Indexingmentioning

confidence: 99%

Accelerating Queries on Very Large Datasets

Otoo¹,

Wu²

2009

Scientific Data Management

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In this chapter, we explore ways to answer queries on large multi-dimensional data efficiently. Given a large dataset, a user often wants to access only a relatively small number of the records. Such a selection process is typically performed through an SQL query in a database management system (DBMS). In general, the most effective technique to accelerate the query answering process is indexing. For this reason, our primary emphasis is to review indexing techniques for large datasets. Since much of scientific data is not under the management of DBMS systems, our review includes many indexing techniques outside of DBMS systems as well. Among the known indexing methods, bitmap indexes are particularly well suited for answering such queries on large scientific data. Therefore, more details are given on the state of the art of bitmap indexing techniques. This chapter also briefly touches on some emerging data analysis systems that don't yet make use of indexes. We present some evidence that these systems could also benefit from the use of indexes.

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“…Efficient data structure to sort strings of characters are binary search trees (BST), splay trees, judy array and tries. Heinz has developed a new data structure, burst trie [7], to sort large strings in memory. A burst trie consists in an access trie whose leaves are containers.…”

Section: Multiplication Using Burst Triesmentioning

confidence: 99%

“…Hida [6] demonstrates the effects of polynomials representation and blocking loops on the execution time to perform a sparse polynomial multiplication. We investigate here the usage of burst tries [7] as an intermediate representation during the sparse polynomial multiplication. Memory management for this intermediate representation is investigated in order to reduce memory consumption and improve scalability on SMP hardware.…”

Section: Introductionmentioning

confidence: 99%

Development of TRIP: Fast Sparse Multivariate Polynomial Multiplication Using Burst Tries

Gastineau

Laskar

2006

Computational Science – ICCS 2006

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Abstract. Flat vector representation of sparse multivariate polynomials is introduced in the computer algebra system TRIP with specific care to the cache memory. Burst tries are considered as an intermediate storage during the sparse multivariate polynomial multiplication by paying attention to the memory allocations. Timing and memory consumption are examined and compared with other recursive representations and other computer algebra systems.

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Burst tries

Abstract: Many applications depend on efficient management of large sets of distinct

Cited by 111 publications

References 51 publications

Practical rearrangement methods for dynamic double‐array dictionaries

Practical rearrangement methods for dynamic double‐array dictionaries

Accelerating Queries on Very Large Datasets

Development of TRIP: Fast Sparse Multivariate Polynomial Multiplication Using Burst Tries

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