Design of Computer Multimedia Intelligent Platform Using Big Data Analysis

2022 ◽  
Author(s):  
Zhenna Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Multimedia Information ◽  
Real Time ◽  
Data Storage ◽  
Big Data Analysis ◽  
Intelligent Vehicle ◽  
Time Data ◽  
Vehicle Operation ◽  
Big Data Technology

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.

scholarly journals Overview of Construction and Application of Big Data Analysis Platform

2021 ◽  
Vol 4 (6) ◽  
Author(s):  
Difei Zhang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Resource Sharing ◽  
Data Exchange ◽  
Big Data Analysis ◽  
Time Data ◽  
Data Platform ◽  
Complete Data Analysis ◽  
Analysis Platform ◽  
Big Data Technology

Big data technology is widely spread around the world, and is constantly developing and applying. In order to enhance the application value of big data analysis platform, it is necessary to constantly improve the data analysis and processing capacity of big data platform, so as to build a complete data analysis platform, realize resource sharing and real-time data collection. As a key point of contemporary information development, big data analysis platform is of great significance to promote social data exchange. Based on this, this paper focuses on two aspects: first, it describes the construction process and content of big data platform; second, it summarizes the relevant application and development of big data platform for reference.

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

Towards an integrated regulatory framework for water and energy security with digital maps: a case study of the Guangdong-Hong Kong-Macao Greater Bay Area, China

2020 ◽  
Author(s):  
Pingyu Fan ◽  
Kwok Pan Chun ◽  
Ana Mijic ◽  
Daphne Ngar-Yin Mah
Keyword(s):  
Big Data ◽  
Information Retrieval ◽  
Data Analysis ◽  
Real Time ◽  
Energy Security ◽  
Regulatory System ◽  
Big Data Analysis ◽  
Regulatory Framework ◽  
Digital Maps ◽  
Bay Area

<p>Digital water and energy maps allow fast information retrieval, big data analysis and resources demand prediction for real time responses in 5-G networks. A regulatory systems framework is needed to enable and promote integrated actions grounded on map-based feedback information, to facilitate resources movements and knowledge transfer for water and energy security. At the same time, the proposed regulatory system needs to safeguard national security and personal privacy when general public and the private sectors have access to big databases.</p><p>The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China is an initiative on regional economic development involving nine mainland cities and two Special Administrative Regions (SARs). As central policies cannot be efficiently executed in the whole regions, institutional fragmentation could be a prominent barrier to achieve regional water and energy optimum rather than individual city maxima for the water and energy nexus.</p><p>In this study, we propose a systems regulatory framework that integrates natural, urban and social systems across multiple scales in which the relevant laws, policies, decisions and actions are supported by digital maps. On a planning scale, our new regulatory system based on spatial map information promotes optimum uses of natural capitals and ecosystem services (ES). For linking different urban spatial processes on different scales, satellite images and Local Climate Zone (LCZ) maps are used to describe natural environment and urban characteristics from 200km to 10km resolutions for supporting land-use planning laws and estimating regional development carrying capacity to mitigate water and energy insecurity.</p><p>On an operational scale, smart meters and remote sensor systems provide real time water and energy information from a fast developing 5-G network for the proposed digital maps. Forecasted energy and water demands from the digital maps can be used for regional or local environment regulation reinforcement. Proposed spatial maps also improve transboundary collaboration by providing visualisation of legal targets and emission limits. Through digital maps, key agencies and sectors will have a capacity to share transboundary knowledge, information and responsibility, to foster smooth system flows in terms of culture, economy, policy and technology, by active participations and decentralized actions.</p><p>On an evaluation scale, open map information increases the transparency of legal targets and pollution limits. By rapid information retrieval and big data analysis from digital maps, regulators can assess the performance of water and energy security practices.</p><p>In summary, the proposed framework based on LCZ maps for the GBA can be applied to other rapidly developing regions with emerging 5-G networks. The integrated regulatory framework also guides water and energy security practices and transfer central policies to local actions by rapid information retrieval, big data analysis and prediction of demand for real time responses based on digital water and energy maps.</p><p></p><p></p><p></p><p></p><p></p>

Establishing the Advanced Disaster Reduction Management System by Fusion of Real-Time Disaster Simulation and Big Data Assimilation

2016 ◽  
Vol 11 (2) ◽  
pp. 164-174 ◽  
Author(s):  
Shunichi Koshimura ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Assimilation ◽  
Real Time ◽  
Big Data Analysis ◽  
Disaster Mitigation ◽  
Observation Data ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Analysis Platform

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

Development of real-time big data analysis system for RHIPE-based marketing in the automobile industry

2018 ◽  
Vol 7 (3.33) ◽  
pp. 248
Author(s):  
Young-Woon Kim ◽  
Hyeopgeon Lee
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Real Time ◽  
Automobile Industry ◽  
Relational Databases ◽  
Distributed Processing ◽  
Big Data Analysis ◽  
Distributed Structure ◽  
Analysis System ◽  
Data Analysis System

In the automobile industry, the contract information of vehicles contracted through sales activities, as well as the order data of customers who purchased cars, and vehicle maintenance history information all accumulate in relational databases over time. Although accumulated customer and vehicle information is used for marketing purposes, processing and analyzing this massive data is difficult, as its volume con-stantly increases. This problem of managing big data is commonly solved by utilizing the MapReduce distributed structure of Hadoop, which uses big data distributed processing technology, and R, which is a widely used big data analysis technology. Among the methods that interconnect Hadoop and R, the R and Hadoop integrated programming environment (RHIPE) was developed in this study as a real-time big data analysis system for marketing in the automobile industry. RHIPE allows us to maintain an interactive environment and use the powerful analytical features of R, which is an interpreter language, while achieving a high processing speed using Map and Reduce func-tions. In this study, we developed a real-time big data analysis system that can analyze the orders, reservations, and maintenance history contained in big data using the RHIPE method. 

scholarly journals Enhancing Competitive Capabilities of Healthcare SCM through the Blockchain: Big Data Business Model’s Viewpoint

2022 ◽  
Author(s):  
Dae Hyun Jung
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analysis ◽  
Supply Chain Management ◽  
Information Sharing ◽  
Real Time ◽  
Big Data Analysis ◽  
Logistics System ◽  
Chain Management ◽  
Competitive Capabilities

This study emphasizes the necessity of introducing a blockchain-based joint logistics system to strengthen the competency of medical supply chain management (SCM) and tries to develop a healthcare supply chain management (HSCM) competency measurement item through an analytic hierarchy process. The variables needed for using blockchain-based joint logistics are the performance expectations, effort expectations, promotion conditions, and social impact of the UTAUT model, and the HSCM competency results in increased reliability and transparency, enhanced SCM, and enhanced scalability. Word cloud results, analyzing the most important considerations to realize work efficiency among medical industry-related agencies, mentioned numerous words, including sudden situations, delivery, technology trust, information sharing, effectiveness, urgency, etc. This might imply the need to establish a system that can respond immediately to emergency situations during holidays. It could also suggest the importance of real-time information sharing to increase the efficiency of inventory management. Therefore, there is a need of a business model that can increase the visibility of real-time medical SCM through big data analysis. By analyzing the importance of securing reliability based on the blockchain technology in the establishment of a supply chain network for HSCM competency, we reveal that joint logistics can be achieved and synergistic effects can be created by implementing the integrated database to secure HSCM competency. Strengthening partnerships, such as joint logistics, will eventually lead to HSCM competency. In particular, HSCM should seek ways to upgrade its competitive capabilities through big data analysis based on the establishment of a joint logistics system.

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