• Genomics & Informatics
• /
• v.14 no.1
• /
• pp.34-40
• /
• 2016

Due to advances in omics technologies, numerous genome-wide studies on human samples have been published, and most of the omics data with the associated clinical information are available in public repositories, such as Gene Expression Omnibus and ArrayExpress. While analyzing several public datasets, we observed that errors in gender information occur quite often in public datasets. When we analyzed the gender description and the methylation patterns of gender-specific probes (glucose-6-phosphate dehydrogenase [G6PD], ephrin-B1 [EFNB1], and testis specific protein, Y-linked 2 [TSPY2]) in 5,611 samples produced using Infinium 450K HumanMethylation arrays, we found that 19 samples from 7 datasets were erroneously described. We also analyzed 1,819 samples produced using the Affymetrix U133Plus2 array using several gender-specific genes (X (inactive)-specific transcript [XIST], eukaryotic translation initiation factor 1A, Y-linked [EIF1AY], and DEAD [Asp-Glu-Ala-Asp] box polypeptide 3, Y-linked [DDDX3Y]) and found that 40 samples from 3 datasets were erroneously described. We suggest that the users of public datasets should not expect that the data are error-free and, whenever possible, that they should check the consistency of the data.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.18 no.2
• /
• pp.39-59
• /
• 2007

With the rapid spread of the practice of collecting and treating data by using a data base system, it's increasingly more critical to approach data sets in the same manner as general records in the collection, evaluation, preservation, and utilization process. Despite the importance, however, the interest level in data sets in Korea's records management is very low. In order to suggest basic items to regard data sets as records and manage them systematically, this study examined the descriptive elements of data set records. descriptive elements of data set records were suggested by comparing and analyzing those ones adopted by the agencies that regarded data sets as records and provided the concerned service as well as the descriptive rules of electronic records set by the advanced nations in records management based on the descriptive areas of ISAD(G).

• Journal of Information Processing Systems
• /
• v.16 no.5
• /
• pp.1083-1094
• /
• 2020

Recognizing plant species based on leaf images is challenging because of the large inter-class variation and inter-class similarities among different plant species. The effective extraction of leaf descriptors constitutes the most important problem in plant leaf recognition. In this paper, a multi-scale angular description method is proposed for fast and accurate leaf recognition and retrieval tasks. The proposed method uses a novel scale-generation rule to develop an angular description of leaf contours. It is parameter-free and can capture leaf features from coarse to fine at multiple scales. A fast Fourier transform is used to make the descriptor compact and is effective in matching samples. Both support vector machine and k-nearest neighbors are used to classify leaves. Leaf recognition and retrieval experiments were conducted on three challenging datasets, namely Swedish leaf, Flavia leaf, and ImageCLEF2012 leaf. The results are evaluated with the widely used standard metrics and compared with several state-of-the-art methods. The results and comparisons show that the proposed method not only requires a low computational time, but also achieves good recognition and retrieval accuracies on challenging datasets.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.10
• /
• pp.33-43
• /
• 2018

In this paper, we propose a method to automatically generate the description of an RDF graph structure. The description is expressed in Shape Expression Language (ShEx), which is developed by W3C and provides the syntax for describing the structure of RDF data. The RDF graphs to which this method can be applied are limited to those generated by the direct mapping, which is an algorithm for transforming relational data into RDF by W3C. A relational database consists of its schema including integrity constraints and its instance data. While the instance data can have been published in RDF by some standard methods such as the direct mapping, the translation of the schema has been missing so far. Unlike the users on relational databases, the ones on RDF datasets were forced to write repeated vague SPARQL queries over the datasets to acquire the exact results. This is because the schema for RDF data has not been provided to the users. The ShEx documents generated by our method can be referred as the schema on writing SPARQL queries. They also can validate data on RDF graph update operations with ShEx validators. In other words, they can work as the integrity constraints in relational databases.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.6
• /
• pp.57-68
• /
• 2017

The Linked Open Data(LOD) cloud is quickly becoming one of the largest collections of interlinked datasets and the de facto standard for publishing, sharing and connecting pieces of data on the Web. Data publishers from diverse domains publish their data using Resource Description Framework(RDF) data model and provide SPARQL endpoints to enable querying their data, which enables creating a global, distributed and interconnected dataspace on the LOD cloud. Although it is possible to extract structured data as query results by using SPARQL, users have very poor in analysis and visualization of RDF data from SPARQL query results. Therefore, to tackle this issue, based on Formal Concept Analysis, we propose a novel approach for analyzing and visualizing useful information from the LOD cloud. The RDF data analysis and visualization technique proposed in this paper can be utilized in the field of semantic web data mining by extracting and analyzing the information and knowledge inherent in LOD and supporting classification and visualization.

• Journal of Information Science Theory and Practice
• /
• v.9 no.2
• /
• pp.66-82
• /
• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.3
• /
• pp.59-70
• /
• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1413-1432
• /
• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

• Journal of the Korean Society for Library and Information Science
• /
• v.56 no.4
• /
• pp.5-28
• /
• 2022

In this study, LD construction cases of overseas libraries were analyzed with focus on published datasets, reused vocabulary, and interlinked external datasets, and based on the analysis results, basic data on LD construction plans of domestic libraries were obtained. As a result of the analysis of 21 library cases, overseas libraries have established a faithful authority LD and conducted new services using published LDs. To this end, overseas libraries collaborated with other libraries and cultural institutions within the region, within the country, and nationally under the leadership of the library, and based on this cooperation, a specialized dataset was published. Overseas libraries used Schema.org to increase the visibility of published LDs, and used BIBFRAME for subdivision of description to define various entities and build LDs based on the defined entities. Overseas libraries have utilized various defined entities to link related information, display results, browse, and download in bulk. Overseas libraries were interested in the continuous up-to-date of interlinked external datasets, and directly utilized external data to reinforce catalog information. In this study, based on the derived implications, points to be considered when issuing LDs by domestic libraries were proposed. The research results can be used as basic data when future domestic libraries plan LD services or upgrade existing services.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.7
• /
• pp.3756-3777
• /
• 2019

Recent internet development is helping malware researchers to generate malicious code variants through automated tools. Due to this reason, the number of malicious variants is increasing day by day. Consequently, the performance improvement in malware analysis is the critical requirement to stop the rapid expansion of malware. The existing research proved that the similarities among malware variants could be used for detection and family classification. In this paper, a Cross-Platform Malware Variant Classification System (CP-MVCS) proposed that converted malware binary into a grayscale image. Further, malicious features extracted from the grayscale image through Combined SIFT-GIST Malware (CSGM) description. Later, these features used to identify the relevant family of malware variant. CP-MVCS reduced computational time and improved classification accuracy by using CSGM feature description along machine learning classification. The experiment performed on four publically available datasets of Windows OS and Android OS. The experimental results showed that the computation time and malware classification accuracy of CP-MVCS was higher than traditional methods. The evaluation also showed that CP-MVCS was not only differentiated families of malware variants but also identified both malware and benign samples in mix fashion efficiently.