• Title/Summary/Keyword: BigData Platform

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Analysis of Success Factors of OTT Original Contents Through BigData, Netflix's 'Squid Game Season 2' Proposal (빅데이터를 통한 OTT 오리지널 콘텐츠의 성공요인 분석, 넷플릭스의 '오징어게임 시즌2' 제언)

  • Ahn, Sunghun;Jung, JaeWoo;Oh, Sejong
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.18 no.1
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    • pp.55-64
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    • 2022
  • This study analyzes the success factors of OTT original content through big data, and intends to suggest scenarios, casting, fun, and moving elements when producing the next work. In addition, I would like to offer suggestions for the success of 'Squid Game Season 2'. The success factor of 'Squid Game' through big data is first, it is a simple psychological experimental game. Second, it is a retro strategy. Third, modern visual beauty and color. Fourth, it is simple aesthetics. Fifth, it is the platform of OTT Netflix. Sixth, Netflix's video recommendation algorithm. Seventh, it induced Binge-Watch. Lastly, it can be said that the consensus was high as it was related to the time to think about 'death' and 'money' in a pandemic situation. The suggestions for 'Squid Game Season 2' are as follows. First, it is a fusion of famous traditional games of each country. Second, it is an AI-based planned MD product production and sales strategy. Third, it is casting based on artificial intelligence big data. Fourth, secondary copyright and copyright sales strategy. The limitations of this study were analyzed only through external data. Data inside the Netflix platform was not utilized. In this study, if AI big data is used not only in the OTT field but also in entertainment and film companies, it will be possible to discover better business models and generate stable profits.

A Study on Deep Learning Model-based Object Classification for Big Data Environment

  • Kim, Jeong-Sig;Kim, Jinhong
    • Journal of Software Assessment and Valuation
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    • v.17 no.1
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    • pp.59-66
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    • 2021
  • Recently, conceptual information model is changing fast, and these changes are coming about as a result of individual tendency, social cultural, new circumstances and societal shifts within big data environment. Despite the data is growing more and more, now is the time to commit ourselves to the development of renewable, invaluable information of social/live commerce. Because we have problems with various insoluble data, we propose about deep learning prediction model-based object classification in social commerce of big data environment. Accordingly, it is an increased need of social commerce platform capable of handling high volumes of multiple items by users. Consequently, responding to rapid changes in users is a very significant by deep learning. Namely, promptly meet the needs of the times, and a widespread growth in big data environment with the goal of realizing in this paper.

Renewable energy trends and relationship structure by SNS big data analysis (SNS 빅데이터 분석을 통한 재생에너지 동향 및 관계구조)

  • Jong-Min Kim
    • Convergence Security Journal
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    • v.22 no.1
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    • pp.55-60
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    • 2022
  • This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

Development of Big-data Management Platform Considering Docker Based Real Time Data Connecting and Processing Environments (도커 기반의 실시간 데이터 연계 및 처리 환경을 고려한 빅데이터 관리 플랫폼 개발)

  • Kim, Dong Gil;Park, Yong-Soon;Chung, Tae-Yun
    • IEMEK Journal of Embedded Systems and Applications
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    • v.16 no.4
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    • pp.153-161
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    • 2021
  • Real-time access is required to handle continuous and unstructured data and should be flexible in management under dynamic state. Platform can be built to allow data collection, storage, and processing from local-server or multi-server. Although the former centralize method is easy to control, it creates an overload problem because it proceeds all the processing in one unit, and the latter distributed method performs parallel processing, so it is fast to respond and can easily scale system capacity, but the design is complex. This paper provides data collection and processing on one platform to derive significant insights from various data held by an enterprise or agency in the latter manner, which is intuitively available on dashboards and utilizes Spark to improve distributed processing performance. All service utilize dockers to distribute and management. The data used in this study was 100% collected from Kafka, showing that when the file size is 4.4 gigabytes, the data processing speed in spark cluster mode is 2 minute 15 seconds, about 3 minutes 19 seconds faster than the local mode.

A Data-driven Approach for Computational Simulation: Trend, Requirement and Technology

  • Lee, Sunghee;Ahn, Sunil;Joo, Wonkyun;Yang, Myungseok;Yu, Eunji
    • Journal of Internet Computing and Services
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    • v.19 no.1
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    • pp.123-130
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    • 2018
  • With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

A Study on Research Trends in Metaverse Platform Using Big Data Analysis (빅데이터 분석을 활용한 메타버스 플랫폼 연구 동향 분석)

  • Hong, Jin-Wook;Han, Jung-Wan
    • Journal of Digital Convergence
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    • v.20 no.5
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    • pp.627-635
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    • 2022
  • As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

Development of Information Technology Infrastructures through Construction of Big Data Platform for Road Driving Environment Analysis (도로 주행환경 분석을 위한 빅데이터 플랫폼 구축 정보기술 인프라 개발)

  • Jung, In-taek;Chong, Kyu-soo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.3
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    • pp.669-678
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    • 2018
  • This study developed information technology infrastructures for building a driving environment analysis platform using various big data, such as vehicle sensing data, public data, etc. First, a small platform server with a parallel structure for big data distribution processing was developed with H/W technology. Next, programs for big data collection/storage, processing/analysis, and information visualization were developed with S/W technology. The collection S/W was developed as a collection interface using Kafka, Flume, and Sqoop. The storage S/W was developed to be divided into a Hadoop distributed file system and Cassandra DB according to the utilization of data. Processing S/W was developed for spatial unit matching and time interval interpolation/aggregation of the collected data by applying the grid index method. An analysis S/W was developed as an analytical tool based on the Zeppelin notebook for the application and evaluation of a development algorithm. Finally, Information Visualization S/W was developed as a Web GIS engine program for providing various driving environment information and visualization. As a result of the performance evaluation, the number of executors, the optimal memory capacity, and number of cores for the development server were derived, and the computation performance was superior to that of the other cloud computing.

An Assessment System for Evaluating Big Data Capability Based on a Reference Model (빅데이터 역량 평가를 위한 참조모델 및 수준진단시스템 개발)

  • Cheon, Min-Kyeong;Baek, Dong-Hyun
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.39 no.2
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    • pp.54-63
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    • 2016
  • As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

A GPU-enabled Face Detection System in the Hadoop Platform Considering Big Data for Images (이미지 빅데이터를 고려한 하둡 플랫폼 환경에서 GPU 기반의 얼굴 검출 시스템)

  • Bae, Yuseok;Park, Jongyoul
    • KIISE Transactions on Computing Practices
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    • v.22 no.1
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    • pp.20-25
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    • 2016
  • With the advent of the era of digital big data, the Hadoop platform has become widely used in various fields. However, the Hadoop MapReduce framework suffers from problems related to the increase of the name node's main memory and map tasks for the processing of large number of small files. In addition, a method for running C++-based tasks in the MapReduce framework is required in order to conjugate GPUs supporting hardware-based data parallelism in the MapReduce framework. Therefore, in this paper, we present a face detection system that generates a sequence file for images to process big data for images in the Hadoop platform. The system also deals with tasks for GPU-based face detection in the MapReduce framework using Hadoop Pipes. We demonstrate a performance increase of around 6.8-fold as compared to a single CPU process.

Development of a Platform Using Big Data-Based Artificial Intelligence to Predict New Demand of Shipbuilding (선박 신수요 예측을 위한 빅데이터 기반 인공지능 알고리즘을 활용한 플랫폼 개발)

  • Lee, Sangwon;Jung, Inhwan
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.1
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    • pp.171-178
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    • 2019
  • Korea's shipbuilding industry is in a critical condition due to changes in the domestic and international environment. To overcome this crisis, preemptive development of products and technologies through prediction of new demand for ships is necessary. The goal of this research is to develop an artificial intelligence algorithm based on ship big data in order to predict new demand for ships. We intend to develop a big data analytics platform specialized in predicting ship demand and to utilize the forecast results of new ship demand through data analysis for planning/development of new products. By doing so, the development of sustainable new business models for equipment and equipment manufacturers will create new growth engines for shipyard and shipbuilders. Furthermore, it is expected that shipbuilders will be able to create business cases based on measurable performance, plan market-oriented products and services, and continuously achieve innovation that has high market destructive power.