Multi-Source Large Data Fusion Access Method for Distribution Communication Network

2019 ◽  
Author(s):  
Huanyu Zhang ◽  
Mi Lin ◽  
Weiming Wu ◽  
Zhilong Chen ◽  
Junwang Yao ◽  
...  
Keyword(s):  
Communication Network ◽  
Data Fusion ◽  
Large Data ◽  
Access Method

Application of Hard C-Means and Fuzzy C-Means in Data Fusion

2012 ◽  
Vol 190-191 ◽  
pp. 265-268
Author(s):  
Ai Hong Tang ◽  
Lian Cai ◽  
You Mei Zhang
Keyword(s):  
Data Fusion ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Large Data ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Data Similarity ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Algorithm

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

scholarly journals Method of Wireless Sensor Network Data Fusion

2017 ◽  
Vol 13 (09) ◽  
pp. 114 ◽  
Author(s):  
Lili Ma ◽  
Jiangping Liu ◽  
Jidong Luo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Information Fusion ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Large Data ◽  
Wireless Sensor ◽  
Fusion Algorithm

<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">In order to better deal with large data information in computer networks, a large data fusion method based on wireless sensor networks is designed. Based on the analysis of the structure and learning algorithm of RBF neural networks, a heterogeneous RBF neural network information fusion algorithm in wireless sensor networks is presented. The effectiveness of information fusion processing methods is tested by RBF information fusion algorithm. The proposed algorithm is applied to heterogeneous information fusion of cluster heads or sink nodes in wireless sensor networks. The simulation results show the effectiveness of the proposed algorithm.  Based on the above finding, it is concluded that the RBF neural network has good real-time performance and small network delay. In addition, this method can reduce the amount of information transmission and the network conflicts and congestion.</span></span></p>

scholarly journals Bitmap Indices for Data Warehouses

2008 ◽  
pp. 1590-1605
Author(s):  
Kurt Stockinger ◽  
Kesheng Wu
Keyword(s):  
Time Complexity ◽  
Large Data ◽  
Database Systems ◽  
Large Data Sets ◽  
Future Research ◽  
Data Sets ◽  
Bitmap Index ◽  
Access Method ◽  
Efficient Access ◽  
Efficient Query Processing

In this chapter we discuss various bitmap index technologies for efficient query processing in data warehousing applications. We review the existing literature and organize the technology into three categories, namely bitmap encoding, compression and binning. We introduce an efficient bitmap compression algorithm and examine the space and time complexity of the compressed bitmap index on large data sets from real applications. According to the conventional wisdom, bitmap indices are only efficient for low-cardinality attributes. However, we show that the compressed bitmap indices are also efficient for high-cardinality attributes. Timing results demonstrate that the bitmap indices significantly outperform the projection index, which is often considered to be the most efficient access method for multi-dimensional queries. Finally, we review the bitmap index technology currently supported by commonly used commercial database systems and discuss open issues for future research and development.

scholarly journals Bitmap Indices for Data Warehouses

2011 ◽  
pp. 157-178 ◽  
Author(s):  
Kurt Stockinger ◽  
Kesheng Wu
Keyword(s):  
Time Complexity ◽  
Large Data ◽  
Database Systems ◽  
Large Data Sets ◽  
Future Research ◽  
Data Sets ◽  
Bitmap Index ◽  
Access Method ◽  
Efficient Access ◽  
Efficient Query Processing

In this chapter we discuss various bitmap index technologies for efficient query processing in data warehousing applications. We review the existing literature and organize the technology into three categories, namely bitmap encoding, compression and binning. We introduce an efficient bitmap compression algorithm and examine the space and time complexity of the compressed bitmap index on large data sets from real applications. According to the conventional wisdom, bitmap indices are only efficient for low-cardinality attributes. However, we show that the compressed bitmap indices are also efficient for high-cardinality attributes. Timing results demonstrate that the bitmap indices significantly outperform the projection index, which is often considered to be the most efficient access method for multi-dimensional queries. Finally, we review the bitmap index technology currently supported by commonly used commercial database systems and discuss open issues for future research and development.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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