Enumeration of nonequivalent substitutional structures using advanced data structure of binary decision diagram

2020 ◽  
Vol 153 (10) ◽  
pp. 104109
Author(s):  
Kohei Shinohara ◽  
Atsuto Seko ◽  
Takashi Horiyama ◽  
Masakazu Ishihata ◽  
Junya Honda ◽  
...  
Keyword(s):  
Data Structure ◽  
Binary Decision Diagram ◽  
Binary Decision

scholarly journals ImplicPBDD: A New Approach to Extract Proper Implications Set from High-Dimension Formal Contexts Using a Binary Decision Diagram †

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 266
Author(s):  
Phillip Santos ◽  
Pedro Ruas ◽  
Julio Neves ◽  
Paula Silva ◽  
Sérgio Dias ◽  
...  
Keyword(s):  
Data Structure ◽  
High Dimension ◽  
Formal Concept Analysis ◽  
Binary Decision Diagram ◽  
High Dimensional ◽  
Formal Context ◽  
Formal Concept ◽  
Binary Decision ◽  
New Approach ◽  
Original Algorithm

Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the PropIm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance—up to 80% faster—than its original algorithm, regardless of the number of attributes, objects and densities.

Embedded Nanowire Network Growth and Node Device Fabrication for GaAs-Based High-Density Hexagonal Binary Decision Diagram Quantum Circuits

2006 ◽  
Vol 45 (4B) ◽  
pp. 3614-3620 ◽  
Author(s):  
Takahiro Tamura ◽  
Isao Tamai ◽  
Seiya Kasai ◽  
Taketomo Sato ◽  
Hideki Hasegawa ◽  
...  
Keyword(s):  
Binary Decision Diagram ◽  
Device Fabrication ◽  
High Density ◽  
Quantum Circuits ◽  
Network Growth ◽  
Binary Decision ◽  
Nanowire Network

scholarly journals DenseZDD: A Compact and Fast Index for Families of Sets

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 128 ◽  
Author(s):  
Shuhei Denzumi ◽  
Jun Kawahara ◽  
Koji Tsuda ◽  
Hiroki Arimura ◽  
Shin-ichi Minato ◽  
...  
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Information Integration ◽  
Decision Diagrams ◽  
Web Information Retrieval ◽  
Binary Decision ◽  
Web Information ◽  
Set Operations ◽  
Succinct Data Structure ◽  
The Family

In this article, we propose a succinct data structure of zero-suppressed binary decision diagrams (ZDDs). A ZDD represents sets of combinations efficiently and we can perform various set operations on the ZDD without explicitly extracting combinations. Thanks to these features, ZDDs have been applied to web information retrieval, information integration, and data mining. However, to support rich manipulation of sets of combinations and update ZDDs in the future, ZDDs need too much space, which means that there is still room to be compressed. The paper introduces a new succinct data structure, called DenseZDD, for further compressing a ZDD when we do not need to conduct set operations on the ZDD but want to examine whether a given set is included in the family represented by the ZDD, and count the number of elements in the family. We also propose a hybrid method, which combines DenseZDDs with ordinary ZDDs. By numerical experiments, we show that the sizes of our data structures are three times smaller than those of ordinary ZDDs, and membership operations and random sampling on DenseZDDs are about ten times and three times faster than those on ordinary ZDDs for some datasets, respectively.

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