A Hardware-Accelerated Solution for Hierarchical Index-Based Merge-Join

2019 ◽  
Vol 31 (1) ◽  
pp. 91-104
Author(s):  
Zimeng Zhou ◽  
Chenyun Yu ◽  
Sarana Nutanong ◽  
Yufei Cui ◽  
Chenchen Fu ◽  
...  
Keyword(s):  
Hierarchical Index

High-Tech Spin-Offs’ Operational Risk Assessment System

2010 ◽  
Vol 40-41 ◽  
pp. 968-973
Author(s):  
Li Ma ◽  
Li Hua Li
Keyword(s):  
Evaluation Model ◽  
Operational Risk ◽  
Sustainable Growth ◽  
Assessment System ◽  
High Tech ◽  
Mathematical Methods ◽  
Operational Risks ◽  
Overall Evaluation ◽  
Hierarchical Index ◽  
Hierarchy Process

This paper analyzes the sources of high-tech spin-offs’ operational risks, establishes a multifactor hierarchical index system and applies Analytical Hierarchy Process and fuzzy mathematical methods to build a fuzzy overall evaluation model. This research can provide a useful tool to help high-tech spin-offs scientifically assess their operational risk degree in order to formulate corresponding countermeasures to evade the risks, and realize sustainable growth.

A Novel Vehicle Type Recognition Based on Hierarchical Matching

2014 ◽  
Vol 602-605 ◽  
pp. 2387-2390
Author(s):  
Zhong Xing Zhang ◽  
Tao Li ◽  
Tao Xiang ◽  
Min Juan Liu
Keyword(s):  
Real Time ◽  
Template Matching ◽  
The Real ◽  
Vehicle Type ◽  
Time Performance ◽  
Lookup Tables ◽  
Novel Method ◽  
Hierarchical Index ◽  
Real Traffic

A novel method of vehicle type recognition based on template matching is proposed to improve the real-time performance of the vehicle type recognition in real traffic scenes. GRM is applied and the template is normalized for realizing parallel template matching. Then, we realize the rapid vehicle type recognition through lookup tables by the hierarchical index of vehicle type template with k-means clustering and size normalization processing. The results show that the algorithm can recognize vehicle type in traffic scenes efficiently.

Efficient Querying Distributed Big-XML Data using MapReduce

2016 ◽  
Vol 8 (3) ◽  
pp. 70-79 ◽  
Author(s):  
Song Kunfang ◽  
Hongwei Lu
Keyword(s):  
Data Storage ◽  
Two Phase ◽  
Xml Data ◽  
Data Intensive ◽  
Labeling Algorithm ◽  
Efficiency And Effectiveness ◽  
Hierarchical Index ◽  
Parallel Queries ◽  
Computing Framework ◽  
Data Intensive Applications

MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. This paper proposed an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. The authors' solution seamlessly integrates data storage, labeling, indexing, and parallel queries to process a massive amount of XML data. Specifically, the authors introduce an SDN labeling algorithm and a distributed hierarchical index using DHTs. More importantly, an advanced two-phase MapReduce solution are designed that is able to efficiently address the issues of labeling, indexing, and query processing on big XML data. The experimental results show the efficiency and effectiveness of the proposed parallel XML data approach using Hadoop.

scholarly journals HISAT-3N: a rapid and accurate three-nucleotide sequence aligner

2020 ◽  
Author(s):  
Yun Zhang ◽  
Chanhee Park ◽  
Christopher Bennett ◽  
Micah Thornton ◽  
Daehwan Kim
Keyword(s):  
Nucleotide Sequence ◽  
Simulated Data ◽  
Alignment Accuracy ◽  
Data Sets ◽  
Cellular Processes ◽  
Sequencing Technologies ◽  
Spliced Alignment ◽  
Hierarchical Index ◽  
Simulated Data Sets ◽  
The Ideal

Nucleotide conversion sequencing technologies such as bisulfite-seq and SLAM-seq are powerful tools to explore the intricacies of cellular processes. In this paper, we describe HISAT-3N (hierarchical indexing for spliced alignment of transcripts - 3 nucleotides), which rapidly and accurately aligns sequences consisting of nucleotide conversions by leveraging powerful hierarchical index and repeat index algorithms originally developed for the HISAT software. Tests on real and simulated data sets demonstrate that HISAT-3N is over 7 times faster, has greater alignment accuracy, and has smaller memory requirements than other modern systems. Taken together HISAT-3N is the ideal aligner for use with converted sequence technologies.

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