• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.76-84
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• 2009

Since too much information has been generated, it became very difficult to find out valuable and necessary information. In order to deal with the problem of information overload, the taxonomy for information visualization techniques has been based upon visualized shapes such as tree map, fisheye view and parallel coordinates, so that it was difficult to choose the right representation technique by data characteristics. Therefore, this study was designed to introduce a new taxonomy for the information visualization by data characteristics which defined by space (3D vs. multi-dimensions), time (continuous vs. discrete), and relations of data (qualitative vs. quantitative). To verify the new taxonomy, forensic data which were generated to investigate the culprit of network security was used. The result showed that the new taxonomy was found to be very efficient and effective to choose the right visualized shape for forensic data for network security. In conclusion, the new taxonomy was proven to be very helpful to choose the right information visualization technique by data characteristics.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.163-176
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• 2010

The representative method to efficiently manage the organization's data is to avoid data duplication through the promotion of sharing and reusing existing data. The systematic structuring of existing data and efficient searching should be supported in order to promote the sharing and reusing of data. Without regard for these points, the data for the system development would be duplicated, which would deteriorate the quality of the data. Data taxonomy provides some methods that can enable the needed data elements to be searched quickly with a systematic order of managing data. This paper proposes that the Origin data taxonomy method can best maximize data sharing, reusing, and consolidation, and it can be used for Meta Data Registry (MDR) and Semantic Web efficiently. The Origin data taxonomy method constructs the data taxonomy structure built upon the intrinsic nature of data, so it can classify the data with independence from business classification. Also, it shows a deployment method for data elements used in various areas according to the Origin data taxonomy structure with a data taxonomic procedure that supports the proposed taxonomy. Based on this case study, the proposed data taxonomy and taxonomic procedure can be applied to real world data efficiently.

• The Journal of Information Systems
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• v.29 no.3
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• pp.1-34
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• 2020

Purpose Due to the complexity of policy environment in modern society, it is accepted as common basics of policy design to mix up a variety of policy instruments aiming the multiple functions. However, under the current situation of written-down policy specification, not only the public officers but also the policy researchers cannot easily grasp such frameworks as policy portfolio. The purpose of this study is to develop "Policy Attributes Taxonomy" identifying and classifying the public programs to help making decisions for allocative efficiency with effectiveness-based information. Design/methodology/approach To figure out the main scheme and classification criteria of Policy Attributes Taxonomy which represents characteristics of public policies, previous theories and researches on policy components were explored. In addition, to test taxonomic feasibility of certain information system, a set of "Feasibility Standards" was drawn from "requirements for well-organized criteria" of eminent taxonomy literatures. Finally, current government classification system in the area of social service was tested to visualize the application of Taxonomy and Standards. Findings Program Taxonomy Schemes were set including "policy goals", "policy targets", "policy tools", "logical relation" and "delivery system". Each program and project could be condensed into these attributes, making their design more easily distinguishable. Policy portfolio could be readily made out by extracting certain characteristics according to this scheme. Moreover, this taxonomy could be used for rearrangement of present "Program Budget System" or estimation of "Basic Income".

• ALGAE
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• v.21 no.4
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• pp.349-375
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• 2006

The application of modern ecological, ultrastructural and molecular methods, aided by the cultivation of numerous cyanobacterial morphotypes, has substantially changed our knowledge of these organisms. It has led to major advances in cyanobacterial taxonomy and criteria for their phylogenetic classification. Molecular data provide basic criteria for cyanobacterial taxonomy; however, a correct phylogenetic system cannot be constructed without combining genetic data with knowledge from the previous 150 years research of cyanobacterial diversity. Thus, studies of morphological variation in nature, and modern morphological, ultrastructural, ecophysiological and biochemical characters need to be combined in a “polyphasic” approach. Taxonomic concepts for generic and infrageneric ranks are re-evaluated in light of combined phenotypic and molecular criteria. Despite their usefulness in experimental studies, the limitations of using strains from culture collections for systematic and nomenclatural purposes is highlighted. The need for a continual revision of strain identification and proper nomenclatural practice associated with either the bacteriological or botanical codes is emphasized. Recent advances in taxonomy are highlighted in the context of prospects for understanding cyanobacterial diversity from natural habitats, and the evolutionary and adaptational processes that cyanobacteria undergo.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.995-1001
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• 2020

With the development of digital instrumentation and control (I&C) devices, cyber security at nuclear power plants (NPPs) has become a hot issue. The Stuxnet, which destroyed Iran's uranium enrichment facility in 2010, suggests that NPPs could even lead to an accident involving the release of radioactive materials cyber-attacks. However, cyber security research on industrial control systems (ICSs) and supervisory control and data acquisition (SCADA) systems is relatively inadequate compared to information technology (IT) and further it is difficult to study cyber-attack taxonomy for NPPs considering the characteristics of ICSs. The advanced research of cyber-attack taxonomy does not reflect the architectural and inherent characteristics of NPPs and lacks a systematic countermeasure strategy. Therefore, it is necessary to more systematically check the consistency of operators and regulators related to cyber security, as in regulatory guide 5.71 (RG.5.71) and regulatory standard 015 (RS.015). For this reason, this paper attempts to suggest a template for cyber-attack taxonomy based on the characteristics of NPPs and exemplifies a specific cyber-attack case in the template. In addition, this paper proposes a systematic countermeasure strategy by matching the countermeasure with critical digital assets (CDAs). The cyber-attack cases investigated using the proposed cyber-attack taxonomy can be used as data for evaluation and validation of cyber security conformance for digital devices to be applied, and as effective prevention and mitigation for cyber-attacks of NPPs.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.897-910
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• 2012

Inappropriate communications can cause a lack of necessary information exchange between operators and lead to serious consequences in large process systems such as nuclear power plants (NPPs). In this regard, various kinds of taxonomies of inappropriate communications have been developed to prevent inappropriate communications. However, there seems to be difficult to identify inappropriate communications from verbal protocol data between operators. Because the existing taxonomies were developed for use in report analysis, there is a problem of 'uncertainty'. In consequence, this paper proposes a new taxonomy of inappropriate communications and provides some insights to prevent inappropriate communications. In order to develop the taxonomy, existing taxonomies for four industries from 1980 to 2010 were collected and a new taxonomy is developed based on the simplified one-way communication model. In addition, the ratio of inappropriate communications from 8 samples of audio-visual format verbal protocol data recorded during emergency training sessions by operating teams is compared with performance scores calculated based on the task analysis. As a result, inappropriate communications can be easily identified from the verbal protocol data using the suggested taxonomy, and teams with a higher ratio of inappropriate communications tend to have a lower performance score.

• Genomics & Informatics
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• v.16 no.4
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• pp.22.1-22.5
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• 2018

In 1966, the International Classification of Viruses (ICNV) was established to standardize the naming of viruses. In 1975, the organization was renamed "International Committee on Taxonomy of Viruses (ICTV)," by which it is still known today. The primary virus classification provided by ICTV in 1971 was for viruses infecting vertebrates, which includes 19 genera, 2 families, and 24 unclassified groups. Presently, the 10th virus taxonomy has been published. However, the early classification of viruses was based on clinical results "in vivo" and "in vitro," as well as on the shape of the Phenotype virus. Due to the development of next-generation sequencing and the accompanying bioinformatics analysis pipelines, a reconstruction of the classification system has been proposed. At a meeting held in Boston, USA between June 9-11, 2016, there was even an in-depth discussion regarding the classification of viruses using metagenomic data. One suggested activity that arose from the meeting was that viral taxonomy should be reconstructed, based on genotype and bioinformatics analysis "in silico." This article describes our efforts to achieve this goal by construction of a web-based system and the extension of an associated database, based on ICTV taxonomy. This virus taxonomy web system was designed specifically to extend the virus taxonomy up to strain and isolation, which was then connected with the NCBI database to facilitate searches for specific viral genes; there are also links to journals provided by the EMBL RESTful API that improves accessibility for academic groups.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.6
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• pp.1142-1160
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• 2022

Text data plays a significant role in understanding and analyzing trends in consumer, business, and social sectors. For text analysis, there must be a corpus that reflects specific domain knowledge. However, in the field of fashion, the professional corpus is insufficient. This study aims to develop a taxonomy and thesaurus that considers the specialty of fashion products. To this end, about 100,000 fashion vocabulary terms were collected by crawling text data from WSGN, Pantone, and online platforms; text subsequently was extracted through preprocessing with Python. The taxonomy was composed of items, silhouettes, details, styles, colors, textiles, and patterns/prints, which are seven attributes of clothes. The corpus was completed through processing synonyms of terms from fashion books such as dictionaries. Finally, 10,294 vocabulary words, including 1,956 standard Korean words, were classified in the taxonomy. All data was then developed into a web dictionary system. Quantitative and qualitative performance tests of the results were conducted through expert reviews. The performance of the thesaurus also was verified by comparing the results of text mining analysis through the previously developed corpus. This study contributes to achieving a text data standard and enables meaningful results of text mining analysis in the fashion field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2357-2364
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• 2008

In this paper, we consider the problem of exploiting a taxonomy of propositionalized attributes in order to generate compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data set, show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.