scholarly journals A Short Video Recommendation System

2021 ◽  
Vol 9 (11) ◽  
pp. 769-774
Author(s):  
Ragu G
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
High Performance ◽  
Recommendation System ◽  
The Internet ◽  
Social Networking Service ◽  
Computing Platform ◽  
Recommendation Algorithms ◽  
Short Video ◽  
Video Recommendation

Abstract: With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favourite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, we propose a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Big Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, we improve the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high-performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system. Keywords: Short, video, recommendation , machine learning

Improve and Optimize Query Recommendation System by MST Algorithm and its MapReduce Implementation

2014 ◽  
Vol 701-702 ◽  
pp. 50-53
Author(s):  
Jian Liang Meng ◽  
Da Wei Li
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Spanning Tree ◽  
Recommendation System ◽  
Minimum Spanning Tree ◽  
Programming Model ◽  
Search Efficiency ◽  
Tree Algorithm ◽  
Query Recommendation ◽  
Parallel Query

Query recommendation as an important tool to enhance the user search efficiency has gradually become a hotspot. In the context of big data, using the MapReduce programming model, combined with distributed minimum spanning tree algorithm, a parallel query recommended method based on MapReduce was proposed in this paper. The final results show that the efficiency of query recommendation was greatly improved through parallel computing.

scholarly journals Hierarchically Authorized Transactions for Massive Internet-of-Things Data Sharing Based on Multilayer Blockchain

2019 ◽  
Vol 9 (23) ◽  
pp. 5159 ◽  
Author(s):  
Shichang Xuan ◽  
Yibo Zhang ◽  
Hao Tang ◽  
Ilyong Chung ◽  
Wei Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Sharing ◽  
Privacy Protection ◽  
High Performance ◽  
The Internet ◽  
Blockchain Technology ◽  
And Storage ◽  
The Internet Of Things

With the arrival of the Internet of Things (IoT) era and the rise of Big Data, cloud computing, and similar technologies, data resources are becoming increasingly valuable. Organizations and users can perform all kinds of processing and analysis on the basis of massive IoT data, thus adding to their value. However, this is based on data-sharing transactions, and most existing work focuses on one aspect of data transactions, such as convenience, privacy protection, and auditing. In this paper, a data-sharing-transaction application based on blockchain technology is proposed, which comprehensively considers various types of performance, provides an efficient consistency mechanism, improves transaction verification, realizes high-performance concurrency, and has tamperproof functions. Experiments were designed to analyze the functions and storage of the proposed system.

Knowledge Discovery and Big Data Analytics

2017 ◽  
pp. 144-164
Author(s):  
Vinoth Kumar Jambulingam ◽  
V. Santhi
Keyword(s):  
Big Data ◽  
Knowledge Discovery ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Massive Datasets ◽  
Computing Platform ◽  
Mining Scheme ◽  
Mining Algorithms ◽  
Performance Computing

The era of big data has come with the ability to process massive datasets from heterogeneous sources in real-time. But the conventional analytics can't be able to manage such a large amount of varied data. The main issue that is being asked is how to design a high-performance computing platform to effectively carry out analytics on big data and how to develop a right mining scheme to get useful insights from voluminous big data. Hence this chapter elaborates these challenges with a brief introduction on traditional data analytics followed by mining algorithms that are suitable for emerging big data analytics. Subsequently, other issues and future scope are also presented to enhance capabilities of big data.

scholarly journals The “Chimera”: An Off-The-Shelf CPU/GPGPU/FPGA Hybrid Computing Platform

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ra Inta ◽  
David J. Bowman ◽  
Susan M. Scott
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
Hardware Acceleration ◽  
Real Data ◽  
Computing System ◽  
Pci Express ◽  
Computing Platform ◽  
Commercial Off The Shelf ◽  
Performance Computing ◽  
Modern Astronomy

The nature of modern astronomy means that a number of interesting problems exhibit a substantial computational bound and this situation is gradually worsening. Scientists, increasingly fighting for valuable resources on conventional high-performance computing (HPC) facilities—often with a limited customizable user environment—are increasingly looking to hardware acceleration solutions. We describe here a heterogeneous CPU/GPGPU/FPGA desktop computing system (the “Chimera”), built with commercial-off-the-shelf components. We show that this platform may be a viable alternative solution to many common computationally bound problems found in astronomy, however, not without significant challenges. The most significant bottleneck in pipelines involving real data is most likely to be the interconnect (in this case the PCI Express bus residing on the CPU motherboard). Finally, we speculate on the merits of our Chimera system on the entire landscape of parallel computing, through the analysis of representative problems from UC Berkeley’s “Thirteen Dwarves.”

Knowledge Discovery and Big Data Analytics

Web Services ◽  
2019 ◽  
pp. 168-183
Author(s):  
Vinoth Kumar Jambulingam ◽  
V. Santhi
Keyword(s):  
Big Data ◽  
Knowledge Discovery ◽  
Data Analytics ◽  
High Performance ◽  
Big Data Analytics ◽  
Massive Datasets ◽  
Computing Platform ◽  
Mining Scheme ◽  
Mining Algorithms ◽  
Performance Computing

The era of big data has come with the ability to process massive datasets from heterogeneous sources in real-time. But the conventional analytics can't be able to manage such a large amount of varied data. The main issue that is being asked is how to design a high-performance computing platform to effectively carry out analytics on big data and how to develop a right mining scheme to get useful insights from voluminous big data. Hence this chapter elaborates these challenges with a brief introduction on traditional data analytics followed by mining algorithms that are suitable for emerging big data analytics. Subsequently, other issues and future scope are also presented to enhance capabilities of big data.

Sign in / Sign up

Export Citation Format

Share Document