Efficient and Elegant Subword-Tree Construction

WDM Multicast Tree Construction Algorithms and Their Comparative Evaluations

IEICE Transactions on Communications ◽

10.1587/transcom.e93.b.2282 ◽

2010 ◽

Vol E93-B (9) ◽

pp. 2282-2290

Author(s):

Tsutomu MAKABE ◽

Taiju MIKOSHI ◽

Toyofumi TAKENAKA

Keyword(s):

Multicast Tree ◽

Tree Construction ◽

Construction Algorithms ◽

Multicast Tree Construction

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An evolutionary algorithm for data aggregation tree construction in three-dimensional wireless sensor networks

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293861 ◽

2020 ◽

Author(s):

Nguyen Thi Tam ◽

Tran Son Tung

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Evolutionary Algorithm ◽

Data Aggregation ◽

Three Dimensional ◽

Wireless Sensor ◽

Tree Construction ◽

Aggregation Tree

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Practical Wavelet Tree Construction

Journal of Experimental Algorithmics ◽

10.1145/3457197 ◽

2021 ◽

Vol 26 ◽

pp. 1-67

Author(s):

Patrick Dinklage ◽

Jonas Ellert ◽

Johannes Fischer ◽

Florian Kurpicz ◽

Marvin Löbel

Keyword(s):

Parallel Algorithms ◽

Shared Memory ◽

Distributed Memory ◽

Auxiliary Information ◽

Parallel Computers ◽

External Memory ◽

Sequential Algorithms ◽

Bottom Up ◽

Memory Efficiency ◽

Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.

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