• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.475-480
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• 2021

With the rapid development of mobile, cloud computing technology and social network services, we are in the flood of huge data and realize that these large-scale data contain very precious value and important information. Big data, however, have both latent useful value and critical risks, so, nowadays, a lot of researches and applications for big data has been executed actively in order to extract useful information from big data efficiently and make the most of the potential information effectively. At this moment, the data analysis technique that can extract precious information from big data efficiently is the most important step in big data computing process. In this study, we investigate various data analysis techniques that can extract the most useful information in big data computing process efficiently, compare pros and cons of those techniques, and propose proper data analysis method that can help us to find out the best solution of the big data analysis in the peculiar situation.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.406-411
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• 2013

Big data issue has been considered in diverse fields. Also, big data learning has been required in all areas such as engineering and social science. Statistics and machine learning algorithms are representative tools for big data learning. In this paper, we study learning tools for big data and propose an efficient methodology for big data learning via legacy data to practical application. We apply our big data learning to patent analysis, because patent is one of big data. Also, we use patent analysis result for technology forecasting. To illustrate how the proposed methodology could be applied in real domain, we will retrieve patents related to big data from patent databases in the world. Using searched patent data, we perform a case study by text mining preprocessing and multiple linear regression of statistics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2022-2042
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• 2014

The term "big data" has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance today's big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.285-304
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• 2017

Big data information and pattern analysis have applications in many industrial sectors. To reduce energy consumption effectively, the eco-driving method that reduces the fuel consumption of vehicles has recently come under scrutiny. Using big data on commercial vehicles obtained from digital tachographs (DTGs), it is possible not only to aid traffic safety but also improve eco-driving. In this study, we estimate fuel consumption efficiency by processing and analyzing DTG big data for commercial vehicles using parallel processing with the MapReduce mechanism. Compared to the conventional measurement of fuel consumption using the On-Board Diagnostics II (OBD-II) device, in this paper, we use actual DTG data and OBD-II fuel consumption data to identify meaningful relationships to calculate fuel efficiency rates. Based on the driving pattern extracted from DTG data, estimating fuel consumption is possible by analyzing driving patterns obtained only from DTG big data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.87-92
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• 2014

We have noted a possibility of big data as a solution of social problem and pending issue. At the same time big data has a problem of privacy. Big data and privacy were in conflict. In this paper we pointed out that issue and propose a planning of big data based on privacy using case study of advanced country.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.261-266
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• 2021

The spread of cloud computing, digital computing, and the popular social media platforms have led to increased growth of data. That growth of data results in what is known as big data (BD), which seen as one of the most strategic resources. The analysis of these BD has allowed generating value from massive raw data that helps in making effective decisions and providing quality of service. With Vision 2030, Saudi Arabia seeks to invest in BD technologies, but many challenges and barriers have led to delays in adopting BD. This research paper aims to search in the state of Big Data Analytics (BDA) in Saudi higher education sector, identify the barriers by reviewing the literature, and then to apply the design-reality gap model to assess these barriers that prevent effective use of big data and highlights priority areas for action to accelerate the application of BD to comply with Vision 2030.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.95-106
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• 2016

The major thrust of this research focuses on the development of phased big data distribution model based on the big data ecosystem. This model consists of 3 phases. In phase 1, data intermediaries are participated in this model and transaction functions are provided. This system consists of general control systems, registrations, and transaction management systems. In phase 2, trading support systems with data storage, analysis, supply, and customer relation management functions are designed. In phase 3, transaction support systems and linked big data distribution portal systems are developed. Recently, emerging new data distribution models and systems are evolving and substituting for past data management system using new technology and the processes in data science. The proposed model may be referred as criteria for industrial standard establishment for big data distribution and transaction models in the future.