Approximating Flow Throughput in Complex Data Networks

Patterns that connect: The value of mapping complex data networks

Information Design Journal ◽

10.1075/idj.17.3.03kah ◽

2009 ◽

Vol 17 (3) ◽

pp. 176-187

Author(s):

Paul Kahn

Keyword(s):

Natural Resources ◽

Similar Pattern ◽

Data Networks ◽

Complex Data ◽

Historical Events ◽

Complex Information ◽

Present Complex

Isometric overview diagrams can be used to synthesize and present complex information in a form similar to a geographic atlas. Given the pattern and shape of a geographic boundary, the reader can understand a variety of information – natural resources, population, historical events – superimposed on the underlying pattern. In the same way, information about complex data networks can be represented in a series of isometric diagrams superimposed on a similar pattern.

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Chaotic Models and Anomaly Detection for Complex Data Networks

10.21236/ada563464 ◽

2012 ◽

Author(s):

Brian R. Hunt ◽

Edward Ott ◽

James A. Yorke

Keyword(s):

Anomaly Detection ◽

Data Networks ◽

Complex Data

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Group Reconstruction and Max-Pooling Residual Capsule Network

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/310 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xinpeng Ding ◽

Nannan Wang ◽

Xinbo Gao ◽

Jie Li ◽

Xiaoyu Wang

Keyword(s):

Routing Algorithm ◽

Large Data ◽

Data Networks ◽

Complex Data ◽

Low Level ◽

Max Pooling ◽

The Arts ◽

Routing Mechanism ◽

High Level ◽

Complex Data Structure

In capsule networks, the mapping of low-level capsules to high-level capsules is achieved by a routing-by-agreement algorithm. Since the capsule is made up of collections of neurons and the routing mechanism involves all the capsules instead of simply discarding some of the neurons like Max-Pooling, the capsule network has stronger representation ability than the traditional neural network. However, considering too much low-level capsules' information will cause its corresponding upper layer capsules to be interfered by other irrelevant information or noise capsules. Therefore, the original capsule network does not perform well on complex data structure. What's worse, computational complexity becomes a bottleneck in dealing with large data networks. In order to solve these shortcomings, this paper proposes a group reconstruction and max-pooling residual capsule network (GRMR-CapsNet). We build a block in which all capsules are divided into different groups and perform group reconstruction routing algorithm to obtain the corresponding high-level capsules. Between the lower and higher layers, Capsule Max-Pooling is adopted to prevent overfitting. We conduct experiments on CIFAR-10/100 and SVHN datasets and the results show that our method can perform better against state-of-the-arts.

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Managing and supporting complex data networks

Proceedings of ELECTRO '94 ◽

10.1109/electr.1994.472700 ◽

2002 ◽

Author(s):

E.I. Kaufmann ◽

M. Condry

Keyword(s):

Data Networks ◽

Complex Data

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Two-Layer Network Caching for Different Service Requirements

Future Internet ◽

10.3390/fi13040085 ◽

2021 ◽

Vol 13 (4) ◽

pp. 85

Author(s):

Gianluca Reali ◽

Mauro Femminella

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Greedy Heuristic ◽

Data Networks ◽

Complex Data ◽

Speed Up ◽

User Access ◽

Numerical Performance ◽

Complete Optimization

Network caching is a technique used to speed-up user access to frequently requested contents in complex data networks. This paper presents a two-layer overlay network caching system for content distribution. It is used to define some caching scenarios with increasing complexity, which refers to real situations, including mobile 5G connectivity. For each scenario our aim is to maximize the hit ratio, which leads to the formulation of NP-complete optimization problems. The heuristic solutions proposed are based on the theory of the maximization of monotone submodular functions under matroid constraints. After the determination of the approximation ratio of the greedy heuristic algorithms proposed, a numerical performance analysis is shown. This analysis includes a comparison with the Least-Frequently Used (LFU) eviction strategy adapted to the analyzed systems. Results show very good performance, under the hypotheses of either known or unknown popularity of contents.

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Creating a standard method of controlling digital microscopes: the microscope access protocol (map)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100136453 ◽

1995 ◽

Vol 53 ◽

pp. 16-17

Author(s):

T. A. Dodson ◽

E. Völkl ◽

L. F. Allard ◽

T. A. Nolan

Keyword(s):

Data Storage ◽

Storage Systems ◽

Digital Systems ◽

Data Networks ◽

Digital Microscopy ◽

Command Language ◽

Access Protocol ◽

Analog To Digital ◽

Basic Physics ◽

Scanning Electron

The process of moving to a fully digital microscopy laboratory requires changes in instrumentation, computing hardware, computing software, data storage systems, and data networks, as well as in the operating procedures of each facility. Moving from analog to digital systems in the microscopy laboratory is similar to the instrumentation projects being undertaken in many scientific labs. A central problem of any of these projects is to create the best combination of hardware and software to effectively control the parameters of data collection and then to actually acquire data from the instrument. This problem is particularly acute for the microscopist who wishes to "digitize" the operation of a transmission or scanning electron microscope. Although the basic physics of each type of instrument and the type of data (images & spectra) generated by each are very similar, each manufacturer approaches automation differently. The communications interfaces vary as well as the command language used to control the instrument.

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Implementation of data mining as a support of business application strategy

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.49 ◽

2018 ◽

Vol 5 (1) ◽

pp. 47-55

Author(s):

Florensia Unggul Damayanti

Keyword(s):

Data Mining ◽

Random Forest ◽

Business Strategy ◽

Input Parameter ◽

Data Mining Algorithm ◽

Complex Data ◽

Business Decision ◽

Marketing Department ◽

Business Application ◽

Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

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