User online behavior based on big data distributed clustering algorithm

With the development of big data technology more and more perfect, many colleges and universities have begun to use it to analyze the construction work. In daily life, such as class, study, and entertainment, the campus network exists. The purpose of this article is to study the online behavior of users, analyze students’ use of the campus network by analyzing students, and not only have a clear understanding of the students’ online access but also feedback on the operation and maintenance of the campus network. Based on the big data, this article uses distributed clustering algorithm to study the online behavior of users. This article selects a college online user as the research object and studies and analyzes the online behavior of school users. This study found that the second-year student network usage is as high as 330,000, which is 60.98% more than the senior. In addition, the majority of student users spend most of their online time on the weekend, and the other time is not much different. The duration is concentrated within 1 h, 1–2 h, 2–3 h in these three time periods. By studying the user’s online behavior, you can understand the utilization rate of the campus network bandwidth resources and the distribution of the use of the network, to prevent students from indulging in the virtual network world, and to ensure that the network users can improve the online experience of the campus network while accessing the network resources reasonably. The research provides a reference for network administrators to adjust network bandwidth and optimize the network.

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Game Theory for Wireless Network Resource Management

Game Theory ◽

10.4018/978-1-5225-2594-3.ch015 ◽

2017 ◽

pp. 383-399

Author(s):

Sungwook Kim

Keyword(s):

Game Theory ◽

Computer Network ◽

Limited Resource ◽

Shared Resources ◽

Network Resources ◽

Network Resource ◽

Network Bandwidth ◽

Competitive Game ◽

Network Usage ◽

Fair Sharing

Computer network bandwidth can be viewed as a limited resource. The users on the network compete for that resource. Their competition can be simulated using game theory models. No centralized regulation of network usage is possible because of the diverse ownership of network resources. Therefore, the problem is of ensuring the fair sharing of network resources. If a centralized system could be developed which would govern the use of the shared resources, each user would get an assigned network usage time or bandwidth, thereby limiting each person's usage of network resources to his or her fair share. As of yet, however, such a system remains an impossibility, making the situation of sharing network resources a competitive game between the users of the network and decreasing everyone's utility. This chapter explores this competitive game.

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Incomplete Big Data Distributed Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.1496 ◽

2014 ◽

Vol 687-691 ◽

pp. 1496-1499

Author(s):

Yong Lin Leng

Keyword(s):

Big Data ◽

Incomplete Data ◽

Clustering Algorithm ◽

Design Method ◽

Complete Data ◽

Similarity Metrics ◽

Distributed Clustering ◽

Computing Technology ◽

Data Set ◽

Affinity Propagation Clustering

Partially missing or blurring attribute values make data become incomplete during collecting data. Generally we use inputation or discarding method to deal with incomplete data before clustering. In this paper we proposed an a new similarity metrics algorithm based on incomplete information system. First algorithm divided the data set into a complete data set and non complete data set, and then the complete data set was clustered using the affinity propagation clustering algorithm, incomplete data according to the design method of the similarity metric is divided into the corresponding cluster. In order to improve the efficiency of the algorithm, designing the distributed clustering algorithm based on cloud computing technology. Experiment demonstrates the proposed algorithm can cluster the incomplete big data directly and improve the accuracy and effectively.

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Game Theory for Wireless Network Resource Management

Advances in Wireless Technologies and Telecommunication - Game Theory Applications in Network Design ◽

10.4018/978-1-4666-6050-2.ch008 ◽

2014 ◽

pp. 188-203

Keyword(s):

Game Theory ◽

Computer Network ◽

Limited Resource ◽

Shared Resources ◽

Network Resources ◽

Network Resource ◽

Network Bandwidth ◽

Competitive Game ◽

Network Usage ◽

Fair Sharing

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A NEW SCHEDULING PROTOCOL DESIGN BASED ON DEFICIT WEIGHTED ROUND ROBIN FOR QoS SUPPORT IN IP NETWORKS

Journal of Circuits System and Computers ◽

10.1142/s0218126613500126 ◽

2013 ◽

Vol 22 (03) ◽

pp. 1350012 ◽

Cited By ~ 2

Author(s):

RIDHA OUNI ◽

JAMILA BHAR ◽

KHOLDOUN TORKI

Keyword(s):

Resource Sharing ◽

Packet Scheduling ◽

Systems Design ◽

Service Level ◽

Round Robin ◽

Utilization Rate ◽

Bandwidth Utilization ◽

Network Resources ◽

Weighted Sharing ◽

Network Bandwidth

We present a study of the effects of active queue management (AQM) on the average queue size in routers. In this work, three prominent AQM schemes are considered: packet classification, checking service level agreements (SLA) and queue scheduling. This paper presents several adaptive resource sharing models that use a revenue criterion to allocate bandwidth in an optimal way. The models ensure QoS requirements of data flows and, at the same time, maximize the total revenue by adjusting parameters of the underlying schedulers. Deficit round robin (DRR) and deficit weighted round robin (DWRR) scheduling techniques have shown their ability in providing fair and weighted sharing of network resources for network devices. However, they are unable to use the total allocated network bandwidth even in burst traffic. In this paper, we propose a negative-deficit weighted round robin (N-DWRR) technique as a new packet scheduling discipline to improve the bandwidth utilization rate without increasing the total latency. A fully hardware packet scheduler has been implemented and verified as part of an intellectual property core. This is motivated by the fact that the design and analysis of hardware/software architectures for such techniques requires new models and methods, which do not fall under the domain of traditional embedded-systems design.

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Distributed Clustering Algorithm to Explore Selection Diversity in Wireless Sensor Networks

IEICE Transactions on Communications ◽

10.1587/transcom.e93.b.1232 ◽

2010 ◽

Vol E93-B (5) ◽

pp. 1232-1239

Author(s):

Hyung-Yun KONG ◽

ASADUZZAMAN

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Clustering Algorithm ◽

Wireless Sensor ◽

Distributed Clustering ◽

Selection Diversity

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Evaluating the Performance of Three Popular Web Mapping Libraries: A Case Study Using Argentina’s Life Quality Index

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9100563 ◽

2020 ◽

Vol 9 (10) ◽

pp. 563

Author(s):

Alejandro Zunino ◽

Guillermo Velázquez ◽

Juan Pablo Celemín ◽

Cristian Mateos ◽

Matías Hirsch ◽

...

Keyword(s):

Life Quality ◽

Web Technologies ◽

Web Mapping ◽

Network Bandwidth ◽

Web Map ◽

Raster Maps ◽

Dynamic Web ◽

Network Usage ◽

Overall Performance

Recent Web technologies such as HTML5, JavaScript, and WebGL have enabled powerful and highly dynamic Web mapping applications executing on standard Web browsers. Despite the complexity for developing such applications has been greatly reduced by Web mapping libraries, developers face many choices to achieve optimal performance and network usage. This scenario is even more complex when considering different representations of geographical data (raster, raw data or vector) and variety of devices (tablets, smartphones, and personal computers). This paper compares the performance and network usage of three popular JavaScript Web mapping libraries for implementing a Web map using different representations for geodata, and executing on different devices. In the experiments, Mapbox GL JS achieved the best overall performance on mid and high end devices for displaying raster or vector maps, while OpenLayers was the best for raster maps on all devices. Vector-based maps are a safe bet for new Web maps, since performance is on par with raster maps on mid-end smartphones, with significant less network bandwidth requirements.

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Big Data: Some Ethical Concerns for the Social Sciences

Social Sciences ◽

10.3390/socsci10020036 ◽

2021 ◽

Vol 10 (2) ◽

pp. 36

Author(s):

Michael Weinhardt

Keyword(s):

Social Sciences ◽

Big Data ◽

Ethical Issues ◽

Online Behavior ◽

Ethical Concerns ◽

Ethical Implications ◽

The Social ◽

And Behaviors ◽

State Actors

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

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Research on the university intelligent learning analysis system based on AI

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189820 ◽

2021 ◽

pp. 1-10

Author(s):

Meng Huang ◽

Shuai Liu ◽

Yahao Zhang ◽

Kewei Cui ◽

Yana Wen

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Academic Performance ◽

Clustering Algorithm ◽

Back Propagation ◽

Three Dimensional ◽

Training Model ◽

Future Trend ◽

Artificial Intelligence Technology ◽

Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

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