• The Journal of the Convergence on Culture Technology
• /
• v.5 no.2
• /
• pp.361-366
• /
• 2019

The amount of data created in the present intelligence information society is beyond imagination. Big data has a great diversity from every information via SNS and internet to the one created by government and enterprises. This various data is close at hand having infinite value as same as crude oil. Big data analysis and utilization by data mining over every areas in the modern industrial society is getting more important for finding useful correlation and strengthening forecasting power against the future uncertainty. Efficient management and utilization of big data produced by complex modern society will be researched in this paper. Also it addresses strategies and methods for securing overall industrial competitiveness, synergy creation among industries, cost reduction and effective application based on big data in the $4^{th}$ industrial revolution era.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.4
• /
• pp.87-99
• /
• 2009

The top-down method for SOA delivery is recommended as a best way to take advantage of SOA. The core step of SOA delivery is the step of service modeling including service analysis and design based on ontology. Most enterprises know that the top-down approach is the best but they are hesitant to employ it because it requires them to invest a great deal of time and money without it showing any immediate results, particularly because they use well-defined component based systems. In this paper, we propose a service identification method to use a well-defined components maximally as a bottom-up approach. We assume that user's inputs generates events on a GUI and the approximate business process can be obtained from concatenating the event paths. We first find the core GUIs which have many outgoing event calls and form event paths by concatenating the event calls between the GUIs. Next, we adapt sequential pattern mining algorithms to find the maximal frequent event paths. As an experiment, we obtained business services with various granularity by applying a cohesion metric to extracted frequent event paths.

• East Asian Journal of Business Economics (EAJBE)
• /
• v.10 no.3
• /
• pp.51-61
• /
• 2022

Purpose - The purpose of this study is to establish the government's logistics policy by calculating the logistics cost of the company and grasping the management status, to reduce the logistics cost of the related companies and to provide basic statistical data necessary for the management strategy. This work examines some associations between reasons for reducing corporate logistics costs (RCLC) and corporate classification such as industry and sales size. Research design, data, and methodology - The survey was conducted in 2018 for 2,000 companies based on the business of mining, manufacturing and wholesale and retail industries since 2010. The survey population is 94,976, of which 92,708 are small and medium enterprises and 2,268 are large corporations. The association among factors may be statistically and visually explored by using chi-squared test and correspondence analysis. Result - This study reveals the association between reasons for RCLC and corporate classification and properties and closeness that exist between the categories of each factor can be mined. Conclusion - As a task to reduce logistics costs of industrial products, expansion and operation of joint logistics business, establishment of cooperative logistics network, and establishment of ordinance on support for smart distribution logistics can be proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.157-158
• /
• 2021

Big data in the supply chain mainly comes from four aspects. One is the relevant data inevitably generated in the process of product value transfer of enterprises in the supply chain, such as production equipment quality data, planned procurement data, product data, etc; On the other hand, it is derived from the ERP data of various companies in the supply chain; The third is e-commerce data from the customer, and the last is data from external or manually entered data. A third-party data service center analysis and mining the data to predict and control the inventory in the process of supply chain operation. It brings innovation and change of management technology and way of thinking to the whole supply chain in many aspects, and finally achieves the goal of coordinated inventory and zero inventory of the whole supply chain.

• Informatization Policy
• /
• v.23 no.4
• /
• pp.76-94
• /
• 2016

Recently in Korea, the importance of information security awareness has been receiving a growing attention. Attacks such as social engineering and ransomware are hard to be prevented because it cannot be solved by information security technology. Also, the profitability of information security industry has been decreasing for years. Therefore, many companies try to find a new growth-engine and an entry to the foreign market. The main purpose of this paper is to draw out some information security issues and to analyze them. Finally, this study identifies issues and suggests how to improve the situation in Korea. For this, topic modeling analysis has been used to find information security issues of each country. Moreover, the score of sentiment analysis has been used to compare them. The study is exploring and explaining what critical issues are and how to improve the situation based on the identified issues of the Korean information security industry. Also, this study is also demonstrating how text mining can be applied to the context of information security awareness. From a pragmatic perspective, the study has the implications for information security enterprises. This study is expected to provide a new and realistic method for analyzing domestic and foreign issues using the analysis of real data of the Twitter API.

• Archives of design research
• /
• v.16 no.2
• /
• pp.281-290
• /
• 2003

In the knowledge and information-based age of today, it would be fair to say that the compatibility of each person, enterprise, and nation can be evaluated by how each of them manages and maintains the knowledge created from data and information. Since the importance and necessity of knowledge management has been acknowledged, there have been studies to create, apply, and evaluate the knowledge concerning design. Previous studies done on this subject can be divided into three main categories - CRM, online statistical research, and eCRM - according to the materials used to create knowledge. These studies are meaningful in that they can create knowledge in their respective fields, although they are somewhat inadequate because the designers can't create as much knowledge as can be applied in business; design-related consumers demand composite knowledge integrating the characteristics of all three fields. In other words, they want to know the ordinary customers'preferences in the previous off-line market in the CRM field, the research results of statistical questionnaires to the various elements of design in statistical research fields, and even the pattern of preference and consumption of many and unspecified persons transcending the time and place in eCRU field. This study proposes to solve the problem related with web-based design knowledge maintenance through the synthetic application of CRM, Statistical Research, and eCRM The information proposed in the solution can De expected to help designers working at design-related enterprises, as well as research institutes, to develop the knowledge necessary to design more consumer-oriented products.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.87-107
• /
• 2016

Understanding market convergence has became essential for small and mid-size enterprises. Identifying convergence items among heterogeneous markets could lead to product innovation and successful market introduction. Previous researches have two limitations. First, traditional researches focusing on patent databases are suitable for detecting technology convergence, however, they have failed to recognize market demands. Second, most researches concentrate on identifying the relationship between existing products or technology. This study presents a platform to identify the opportunity of market convergence by using product databases from a global B2B marketplace. We also attempt to identify convergence opportunity in different industries by applying Structural Hole theory. This paper shows the mechanisms for market convergence: attributes extraction of products and services using text mining and association analysis among attributes, and network analysis based on structural hole. In order to discover market demand, we analyzed 240,002 e-catalog from January 2013 to July 2016.

• The KIPS Transactions:PartD
• /
• v.15D no.4
• /
• pp.485-494
• /
• 2008

Huge amounts of various web items such as keywords, images, and web pages are being made widely available on the Web. The popularities of such web items continuously change over time, and mining temporal patterns in popularities of web items is an important problem that is useful for several web applications. For example, the temporal patterns in popularities of search keywords help web search enterprises predict future popular keywords, enabling them to make price decisions when marketing search keywords to advertisers. However, presence of millions of web items makes it difficult to scale up previous techniques for this problem. This paper proposes an efficient method for mining temporal patterns in popularities of web items. We treat the popularities of web items as time-series, and propose gapmeasure to quantify the similarity between the popularities of two web items. To reduce the computation overhead for this measure, an efficient method using the Fast Fourier Transform (FFT) is presented. We assume that the popularities of web items are not necessarily following any probabilistic distribution or periodic. For finding clusters of web items with similar popularity trends, we propose to use a density-based clustering algorithm based on the gap measure. Our experiments using the popularity trends of search keywords obtained from the Google Trends web site illustrate the scalability and usefulness of the proposed approach in real-world applications.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.39-70
• /
• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• IE interfaces
• /
• v.13 no.3
• /
• pp.503-511
• /
• 2000

The main theme of this research is a case of e-Business implementation in part manufacturing industry, especially in a PCB manufacturing company. The characteristics of part manufacturing industry are as follows. First, an ERP system runs as a legacy system that is ready to be combined with e-Business system. Secondly, the number of customers is very small. The customers are not many individuals but only a few big electronic enterprises that are strategically affiliated with the part manufacturing company. This means that the e-Business of the part manufacturing industry needs to focus on sharing pertinent information throughout the transactions with the customers, not on data-warehousing or data-mining customers' potential needs or requests. In this paper, we extracted e-Business opportunity domains from a PCB manufacturing company, a typical part manufacturing industry. We are intended to enhance information sharing between customers and the company, and provide functions of transactions necessary in the whole value chain from order to shipment. Implementing the e-Business system on the Web can increase the visibility of customers, and further, the company can be transformed into an extended enterprise where the relationship with the customers becomes very close and interleaved. Also, the Cyber Office functionality of the e-Business system can support the salespersons effectively, so that they can spend more time on customer satisfaction. Such efforts, in the future, can be a basis for active adaptation to the industry transformations such as forming e-community and participating in the marketplace.