scholarly journals A Survey on Big Data in the Media and Entertainment Industry

2019 ◽  
Vol 4 (2) ◽  
pp. 75-88
Author(s):  
Annisaa Nurhayati
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Mobile Devices ◽  
Data Streams ◽  
Big Data Analysis ◽  
The Internet ◽  
Media Industry ◽  
Data Formats ◽  
The Media ◽  
Many Sources

Big Data has affected all industries, including the media dan entertainment industries. The popularity of using mobile devices and the internet has changed the way people enjoy entertainment. This popularity also generates data streams from many sources with various data formats and large volumes, known as big data. Carrying out big data analysis can help the media industry and entertainment achieve its goals, like providing content that makes users happy, provides user experience, and increases profits. Many researchers have conducted research on the use of big data in the media and entertainment industries. The purpose of this paper is to provide an overview of the problems, challenges and various technologies related to Big Data in the media and entertainment industries.

scholarly journals IoT-Fog-Cloud system architecture for big data analysis and cybersecurity systems: a review of fog computing, the introduction to the Internet of Things audit

2020 ◽  
Author(s):  
D. R. Kolisnyk ◽  
◽  
K. S. Misevych ◽  
S. V. Kovalenko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
System Architecture ◽  
Fog Computing ◽  
Big Data Analysis ◽  
Future Research ◽  
The Internet ◽  
Security Issues ◽  
The Internet Of Things

The article considers the issues of system architecture IoT-Fog-Cloud, considers the interaction between the three levels of IoT, Fog and Cloud for the effective implementation of programs for big data analysis and cybersecurity. The article also discusses security issues, solutions and directions for future research in the field of the Internet of Things and nebulous computing.

scholarly journals Information Security Terminal Architecture of Power Transportation Mobile Internet of Things Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianzhi Tang ◽  
Chunyan Ding
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Information Security ◽  
Internet Of Things ◽  
Power System ◽  
Big Data Analysis ◽  
The Internet ◽  
Mobile Information ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The progress of the social economy and the rapid development of the power field have created more favorable conditions for the construction of my country’s power grid. In this network age, how to further realize the connection between the power system and the Internet of Things is the key content of many scholars’ research. In the Internet of Things environment, there have been many excellent results in the collection, storage, and management of electric power big data, but the problem of information security has not been completely solved. Based on big data analysis and Internet of Things technology, this paper studies the architecture design of power information security terminals. In view of the diverse types of power grid mobile information and the large amount of data, this paper designs a power transportation mobile information security management system structure, which improves the effective management of power data by the system through big data, smart sensors, and wireless communication technology. According to the experiment, the power information security terminal constructed in this paper can effectively reduce communication resources and save communication costs in the process of aggregating multidimensional data. In the user satisfaction survey, residents’ satisfaction with the convenience and safety of the intelligent power system is also as high as 9.312 and 9.233. On the whole, the application of big data and Internet of Things technology to the construction of power information security terminals can indeed improve the service efficiency of power companies under the premise of ensuring safety and allow users to have a better experience.

scholarly journals Programming big data analysis: principles and solutions

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Loris Belcastro ◽  
Riccardo Cantini ◽  
Fabrizio Marozzo ◽  
Alessio Orsino ◽  
Domenico Talia ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Message Passing ◽  
Global Analysis ◽  
Big Data Analysis ◽  
Digital Data ◽  
Advantages And Disadvantages ◽  
Depth Analysis ◽  
Social Media Platforms ◽  
Many Sources

AbstractIn the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.

scholarly journals Networkmetrics: multivariate big data analysis in the context of the internet

2016 ◽  
Vol 30 (9) ◽  
pp. 488-505 ◽  
Author(s):  
José Camacho ◽  
Roberto Magán-Carrión ◽  
Pedro García-Teodoro ◽  
James J. Treinen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
The Internet

scholarly journals Innovation Path of Leasing Trade under Smart Logistics Technology by Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanan Song ◽  
Xiaolong Hua
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Electric Vehicles ◽  
Big Data Analysis ◽  
Experimental Results ◽  
Utilization Rate ◽  
The Internet ◽  
Modern Logistics ◽  
Productivity Level ◽  
Smart Logistics

With the continuous development of big data and the increasing maturity of e-commerce, people’s requirements for modern logistics are becoming increasingly diversified. Conventional modern logistics service level is insufficient to meet the needs of consumers. Therefore, research on the innovative path of big data analysis of leasing trade under smart logistics technology is increasingly important. Smart logistics pays much attention to the integration of the Internet of Things, sensor networks, and the existing internet and realizes the automation, visualization, controllability, intelligence, and networking of logistics through sophisticated, dynamic, and scientific management, thereby improving resource utilization rate and productivity level, creating a more comprehensive connotation of social value. This work aimed to study the use of smart logistics technology to make electric car leasing to a higher level and to innovate the promotion and use mode of leasing trade. A shared business model of electric vehicles was proposed based on the internet and smart logistics technology. The experimental results showed that smart logistics technology contributes to exploring and expanding the role of electric vehicles in solving group users’ short-distance travel and improving an urban transportation system. It could offer a basis for nationwide promotion, thus promoting the development of the new energy vehicle industry and testing the practicability of the model. Regarding the improvement of the sample information system, the experimental results showed that the user’s full satisfaction is approximately 50%, while there remain the customers who would ask for further improvements. Besides, most customers believe that the accuracy and sensitivity of the existing in-vehicle information display need to be enhanced.

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