Text-Data Reduction Method to Grasp the Sequence of a Disaster Situation: Case Study of Web News Analysis of the 2015 Typhoons 17 and 18

2017 ◽  
Vol 12 (2) ◽  
pp. 329-334
Author(s):  
Shosuke Sato ◽  
◽  
Toru Okamoto ◽  
Shunichi Koshimura ◽  
Keyword(s):  
Big Data ◽  
Data Reduction ◽  
Reduction Method ◽  
Text Data ◽  
News Analysis ◽  
Disaster Situation ◽  
Data Source ◽  
Data Reduction Method ◽  
Web News

This study aims to compress web news, delivered as a big-data source after disasters. In this paper, article clustering, which is a combination of conventional means and an algorithm that selects the representative articles of each cluster, is designed and adopted. Experiments are conducted by evaluators. The proposed algorithm is in accord with the evaluators for 50s% of the clustering and for about 30s% to 40s% of the representative-article selection.

In situ isotopic analysis of uranium using a new data acquisition protocol for 1013 ohm Faraday amplifiers

2021 ◽  
Author(s):  
Kota Yamamoto ◽  
Hisashi Asanuma ◽  
Hiroaki Takahashi ◽  
Takafumi Hirata
Keyword(s):  
Data Acquisition ◽  
Data Reduction ◽  
Reduction Method ◽  
Isotopic Analysis ◽  
High Gain ◽  
Transient Signals ◽  
Acquisition Protocol ◽  
Data Reduction Method ◽  
Isotopic Measurements

New data reduction method for isotopic measurements using high-gain Faraday amplifiers enables precise uranium isotopic analysis even from transient signals.

scholarly journals An efficient data reduction method and its application to cluster analysis

2017 ◽  
Vol 238 ◽  
pp. 234-244 ◽  
Author(s):  
Jianpei Wang ◽  
Shihong Yue ◽  
Xiao Yu ◽  
Yaru Wang
Keyword(s):  
Cluster Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Efficient Data ◽  
Data Reduction Method

A corpus study of variation in written Chinese

2012 ◽  
Vol 8 (1) ◽  
pp. 209-240 ◽  
Author(s):  
Zheng-sheng Zhang,
Keyword(s):  
Feature Selection ◽  
Correspondence Analysis ◽  
Data Reduction ◽  
Reduction Method ◽  
Three Dimensions ◽  
Linguistic Variation ◽  
Corpus Study ◽  
Distributional Patterns ◽  
Preliminary Study ◽  
Data Reduction Method

AbstractThe present paper reports on the findings of a preliminary study of written Chinese, using the Lancaster Corpus of Mandarin Chinese (LCMC, McEnery & Xiao 2004). The first part of the paper introduces the stylistic features, and briefly describes the distributional patterns of these features across the selected written registers. Then, using a multi-feature, multi-dimensional framework (Biber 1988) and the data reduction method of correspondence analysis, three dimensions are identified and interpreted. The study reveals extensive linguistic variation across written Chinese registers, thus complementing previous observations about stylistic differences between spoken and written Chinese. Finally, issues concerning feature selection and dimension interpretation are discussed.

Parallel Data Reduction Techniques for Big Datasets

2013 ◽  
pp. 72-93 ◽  
Author(s):  
Ahmet Artu Yıldırım ◽  
Cem Özdoğan ◽  
Dan Watson
Keyword(s):  
Big Data ◽  
Data Reduction ◽  
Memory Systems ◽  
Discrete Wavelet ◽  
Server Side ◽  
Advantages And Disadvantages ◽  
Reduction Techniques ◽  
Parallel Data ◽  
Processing Techniques

Data reduction is perhaps the most critical component in retrieving information from big data (i.e., petascale-sized data) in many data-mining processes. The central issue of these data reduction techniques is to save time and bandwidth in enabling the user to deal with larger datasets even in minimal resource environments, such as in desktop or small cluster systems. In this chapter, the authors examine the motivations behind why these reduction techniques are important in the analysis of big datasets. Then they present several basic reduction techniques in detail, stressing the advantages and disadvantages of each. The authors also consider signal processing techniques for mining big data by the use of discrete wavelet transformation and server-side data reduction techniques. Lastly, they include a general discussion on parallel algorithms for data reduction, with special emphasis given to parallel wavelet-based multi-resolution data reduction techniques on distributed memory systems using MPI and shared memory architectures on GPUs along with a demonstration of the improvement of performance and scalability for one case study.

Sign in / Sign up

Export Citation Format

Share Document