• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.3
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• pp.53-64
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• 2006

Worm analysis report currently produced by anti-virus companies closely resemble those of virus report and do not properly characterize the specific attributes of worms. In this paper, we propose formalized presentation method based on time-based behavioral sequences to more accurately characterize worms. we define a format based on the behavior and communication patterns that occur between an infected host and a target host. we also propose a method for presently worm analysis data with that format. We also compare our framework with analysis data provided by Symantec.

• Journal of Distribution Science
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• v.21 no.4
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• pp.35-45
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• 2023

Purpose: The study's objective was to examine the impact of brand personality dimensions on consumer behavior for laptop purchases in Nepal. Research Materials and Methods: The study included descriptive and explanatory research designs. A structured questionnaire with a purposive sampling method was employed to gather the necessary information for the study. The survey data were analyzed using a quantitative approach. The study used descriptive statistics to characterize the response conditions. Correlation analysis was used to investigate the relationship between brand personality dimensions and consumer behavior. Regression path analysis was employed to identify the effect of brand personality dimensions and consumer behavior. Results: The result of regression path analysis showed that the three dimensions - competency, ruggedness, and sophistication, have a significant effect on consumer behavior, and the two dimensions- sincerity and excitement do not have a substantial impact on consumer behavior in laptop buying in Nepal. Conclusions and Implications: Such findings can serve as pioneering empirical evidence and provide a framework for marketers and future studies in various scenarios. The study's findings can help marketing managers in handling information management. Manufacturers, wholesalers, and retailers can also use the results in formulating marketing strategies, and marketers need to be aware of such considerations for influencing consumer behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2966-2986
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• 2023

Climate change is a constant threat to human life, and it is important to understand the public perception of this issue. Previous studies examining climate change have been based on limited survey data. In this study, the authors used big data such as news articles and social media data, within which the authors selected specific keywords related to climate change. Using these natural language data, topic modeling was performed for discourse analysis regarding climate change based on various topics. In addition, before applying topic modeling, sentiment analysis was adjusted to discover the differences between discourses on climate change. Through this approach, discourses of positive and negative tendencies were classified. As a result, it was possible to identify the tendency of each document by extracting key words for the classified discourse. This study aims to prove that topic modeling is a useful methodology for exploring discourse on platforms with big data. Moreover, the reliability of the study was increased by performing topic modeling in consideration of objective indicators (i.e., coherence score, perplexity). Theoretically, based on the social amplification of risk framework (SARF), this study demonstrates that the diffusion of the agenda of climate change in public news media leads to personal anxiety and fear on social media.

• Journal of KIISE
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• v.44 no.5
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• pp.460-468
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• 2017

In a real-time cloud environment, the data analysis framework plays a pivotal role. Spark Streaming meets most real-time requirements among existing frameworks. However, the framework does not meet the second scale real-time fault recovery requirement. Spark Streaming fault recovery time increases in proportion to the transformation history length called lineage. This is because it recovers the last state data based on the cumulative lineage recorded during normal operation. Therefore, fault recovery time is not bounded within a limited time. In addition, it is impossible to achieve a second-scale fault recovery time because it costs tens of seconds to read initial state data from fault-tolerant storage. In this paper, we propose two techniques to solve the problems mentioned above. We apply the proposed techniques to Spark Streaming 1.6.2. Experimental results show that the fault recovery time is bounded and the average fault recovery time is reduced by up to 41.57%.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.523-530
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• 2009

Despite the growing need for customized operating system kernels for embedded devices, kernel development continues to suffer from insufficient reliability and high testing cost for several reasons such as the high complexity of the kernel code. To alleviate these difficulties, this study proposes the MOdel-based KERnel Testing (MOKERT) framework for detection of concurrency bugs in the kernel. MOKERT translates a given C program into a corresponding Promela model, and then tries to find a counter example with regard to a given requirement property, If found, MOKERT executes that counter example on the real kernel code to check whether the counter example is a false alarm or not, The MOKERT framework was applied to the Linux proc file system and confirmed that the bug reported in a ChangeLog actually caused a data race problem, In addition, a new data race bug in the Linux proc file system was found, which causes kernel panic.

• Journal of KIISE
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• v.42 no.12
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• pp.1575-1583
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• 2015

With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users' health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users' IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.114-123
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• 2009

Context-aware systems are reactive computing systems, aware of external context. Considering various sensors and actuators connected, application programming on top of such systems is known to be much more complex than in contentional ones. This paper suggests a formal framework for context-aware systems, by extracting their common properties. That makes a useful birds-eye view for the behaviors of a whole complex system, as a base for a convenient developing environment and systematic analysis. In this framework, reactive-ness is handled by event-condition-action rules and global state-transitions, which are essential in a lot of context-aware applications. In addition, behaviors of each elementary device are modelled with its own state-transitions, and tightly bound to the entire task.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.83-91
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• 2006

Internet shopping mall has become a huge distribution channel with dramatic growth in recent years. The number of consumers has exponentially increased as the scale of shopping mall has been large so that shopping malls with thousands or millions of consumers become a general case. However, it is essential to evaluate whether current assortment of consumers is proper or not in the strategic aspect in order to operate Internet shopping mall effectively and gain profits. That is, it is important to evaluate whether consumer strategy of corporation is proper or not from the corporation. Despite this business importance, consumer assortment has not been evaluated well and related study is not sufficient. This study supposes a framework for consumer assortment evaluation, which evaluates whether consumer assortment of Internet shopping mall is proper or not. In the framework for consumer assortment evaluation, analysis data based on order data and consumer data in database is made. Then, four factors, consumer maintenance rate, consumer profitability, consumer securing rate and consumer conversion are setup, and 22 measurement indexes are drawn. Finally, a consumer assortment evaluation score card is made by integrating them. This study has applied a supposed framework to a domestic typical community based shopping mall, and it is expected that the evaluation result will be used as informant strategic information to operate the shopping mall effectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3407-3411
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• 2015

In the process of extending the UML model for a various domain, comply with the UML metamodel and it is possible to obtain a number of advantages. However, the UML metamodel is necessary to redefine the diagram must be defined via the Profile in order to expand and transformation the UML metamodel from a variety of sources using the UML metamodel is becoming increase massive scale. it is necessary to use only those extracts the element relative to the target to be used to extend a UML metamodel. Used to re-define the extension of the UML Metamodel and Profile based UML modeling tools and metadata repositories by analysis tools, can develop more quickly and easily, by utilizing these tools can improve the quality of development in the SW industry. In this paper, we present an algorithm that of the profile through the expansion of the UML metamodel and shows the results in actually applying e-government standards framework.