• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.27-36
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• 2020

In this paper, we propose a method to derive valuable but hidden infromation from the data which is the core foundation in the 4th Industrial Revolution to pursue knowledge-based service fusion. The hyper-connected societies characterized by IoT inevitably produce big data, and with the data in order to derive optimal services for trouble situations it is first processed by discovering valuable information. A data-centric IoT platform is a platform to collect, store, manage, and integrate the data from variable devices, which is actually a type of middleware platforms. Its purpose is to provide suitable solutions for challenged problems after processing and analyzing the data, that depends on efficient and accurate algorithms performing the work of data analysis. To this end, we propose specially designed structures to store IoT data without losing the semantics and provide algorithms to discover the useful information with several definitions and proofs to show the soundness.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.374-386
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• 2016

Provenance means the meta data that represents the history or lineage of a data in collaboration storage environments. Therefore, as provenance has been accruing over time, it takes several ten times as large as the original data. The schemes for effciently compressing huge amounts of provenance are required. In this paper, we propose a provenance compression scheme considering the RDF graph patterns. The proposed scheme represents provenance based on a standard PROV model and encodes provenance in numeric data through the text encoding. We compress provenance and RDF data using the graph patterns. Unlike conventional provenance compression techniques, we compress provenance by considering RDF documents on the semantic web. In order to show the superiority of the proposed scheme, we compare it with the existing scheme in terms of compression ratio and the processing time.

• Journal of Korean Library and Information Science Society
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• v.50 no.3
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• pp.97-116
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• 2019

This study is to analyze the characteristics of 2019 revised RDA and suggest the consideration in aspects of cataloging using the literature reviews. The following 3 things are suggested with analyzing the revised RDA. First, high quality data such as supplementing cataloging data and constructing vocabulary encoding schemes should be needed to transform bibliographic data to linked data for the semantic web. Second, MARC should be expanded to accept the new conept of LRM and linked data being reflected in revised RDA because MARC is the unique encoding format untile linked data will be transformed from MARC data. Third, the policy statement and the application profile are needed for describing resource consistently because each entity and element has own condition and option, and there are different elements for applying rules in revised RDA. Based on this study, the RDA related researches should be in progress such as exapanding BIBFRAME as well as MARC to accept the new concepts in revised RDA, and, also, reflecting and accepting RDA being able to use revised RDA rules and registries in libraries and nations that have been faced to revise their own cataloging rules.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06b
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• pp.307-310
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• 2011

정보생산량의 폭발적 증가(2008년 4,870억GB에서 2010년 12,000억GB로 62% 증가, 미국 EMC & 스위츠 IMD 기준) 및 각 정보(콘텐츠) 간 연관 지식의 요구 증대는 다양한 디지털 콘텐츠에 대한 효율적인 통합 관리의 필요성을 더욱더 증대시키고 있으며, 또한 더 나은, 그리고 새로운 서비스를 위한 다양한 정보 간 연계에 대한 필요성이 더욱더 요구되고 있다. 본 고(考)에서는 문헌데이터와 시맨틱 데이터에 대한 체계화되고, 표준화되며, 일원화된 정보관리 수행과 두 정보 간 연계를 통한 SMART 서비스를 실현할 수 있도록 디지털 콘텐츠 관리체계를 새로이 설계하고, 이를 구축하였다.

• Journal of KIISE
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• v.43 no.6
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• pp.700-709
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• 2016

Recently, with the rapid growth of LOD (Linked Open Data) existing methods based on a single machine have limitation in performance. Existing solutions use distributed framework such as Mapreduce in order to improve the performance. However, the MapReduce framework for processing SPARQL queries involves multiple MapReduce jobs and additional costs incurred. In addition, the problem of unnecessary data processing arises. In this study, we proposed a method to reduce the number of MapReduce jobs during SPARQL query processing and join indexes based on Bitmap for minimizing the costs of processing unnecessary data.

• 한국IT서비스학회:학술대회논문집
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• 2009.05a
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• pp.393-396
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• 2009

기존의 웹을 확장한 시맨틱 웹의 등장으로 웹 온톨로지의 구축이 중요시 되고 있다. 이로 인하여 현재 웹 온톨로지의 관리 및 활용을 위한 편집기, 웹 온톨로지 기술언어, 저장소 및 추론 엔진 등 다양한 기술 및 시스템들이 개발되어 웹 온톨로지의 구축이 용이해졌다. 이제는 구축된 웹 온톨로지를 응용 시스템에 활용하기 위한 웹 온톨로지 클래스에 대한 인스턴스를 풍부하게 할 수 있는 웹 온톨로지의 확장에 대한 연구가 요구된다. 웹 온톨로지의 확장을 위해서는 먼저 웹 온톨로지를 보다 정확하게 정의해야 하며 웹 온톨로지를 보다 풍부하게 확장할 수 있는 방법이 개발되어야 한다. 웹 온톨로지의 보다 정확한 정의를 위해서는 표준화 된 공통 개념을 이용하여 웹 온톨로지 스키마를 생성해야하며 이를 기반으로 한 웹 온톨로지 간 상호운용성 향상되어야 한다. 따라서 이 논문에서는 표준화 된 공통 개념을 관리하는 메타데이터 레지스트리(Metadata Registry)를 기반으로 구조화 된 데이터를 이용한 웹 온톨로지의 학습 및 확장 모델을 제안한다. 또한, 제안 모델을 위한 프로토타입을 구현하고 제안 모델의 평가에 대하여 기술한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1259-1273
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• 2012

As a society of knowledge and information has been developed rapidly, because of changing from web environment to ubiquitous environment, a lot of countries across the world as well as S. Korea for national information with electronic Government have a variety of changes with big data. So this study is about development for national information and e-government of S. Korea with public data as big data analysed by the application of scenario planning. And then this research focused on the strategy consulting of national information with e-Government of S. Korea for utilization of public data as big data analysed by the application of 'scenario planning' as a foresight method. As a result, the future policy for utilization of public data as big data for national information with electronic government of S. Korea is to further spur the development of technology for linked data with semantic web for 'understanding of machine' than 'understanding of man'.

• Journal of KIISE
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• v.42 no.5
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• pp.580-590
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• 2015

Recently, the issue of efficient power management in datacenters as a part of green computing has gained prominence. For realizing efficient power management, effective power monitoring and analysis must be conducted for servers in a datacenter. However, an administrator should know the exact structure of the datacenter and its associated databases, and is required to analyze relationships among the observed data. This is because according to previous monitoring approaches, servers are usually managed using only databases. In addition, it is not possible to monitor data that are not indicated in databases. To overcome these drawbacks, we proposed an ontology-based monitoring approach. We constructed domain ontology for management servers at a datacenter, and mapped observed data onto the constructed domain ontology by using semantic annotation. Moreover, we defined query creation rules and server state rules. To demonstrate the proposed approach, we designed an ontology based monitoring system architecture, and constructed a knowledge system. Subsequently, we implemented the pilot system to verify its effectiveness.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.203-219
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• 2011

Previously common users just want to watch the video contents without any specific requirements or purposes. However, in today's life while watching video user attempts to know and discover more about things that appear on the video. Therefore, the requirements for finding multimedia or browsing information of objects that users want, are spreading with the increasing use of multimedia such as videos which are not only available on the internet-capable devices such as computers but also on smart TV and smart phone. In order to meet the users. requirements, labor-intensive annotation of objects in video contents is inevitable. For this reason, many researchers have actively studied about methods of annotating the object that appear on the video. In keyword-based annotation related information of the object that appeared on the video content is immediately added and annotation data including all related information about the object must be individually managed. Users will have to directly input all related information to the object. Consequently, when a user browses for information that related to the object, user can only find and get limited resources that solely exists in annotated data. Also, in order to place annotation for objects user's huge workload is required. To cope with reducing user's workload and to minimize the work involved in annotation, in existing object-based annotation automatic annotation is being attempted using computer vision techniques like object detection, recognition and tracking. By using such computer vision techniques a wide variety of objects that appears on the video content must be all detected and recognized. But until now it is still a problem facing some difficulties which have to deal with automated annotation. To overcome these difficulties, we propose a system which consists of two modules. The first module is the annotation module that enables many annotators to collaboratively annotate the objects in the video content in order to access the semantic data using Linked Data. Annotation data managed by annotation server is represented using ontology so that the information can easily be shared and extended. Since annotation data does not include all the relevant information of the object, existing objects in Linked Data and objects that appear in the video content simply connect with each other to get all the related information of the object. In other words, annotation data which contains only URI and metadata like position, time and size are stored on the annotation sever. So when user needs other related information about the object, all of that information is retrieved from Linked Data through its relevant URI. The second module enables viewers to browse interesting information about the object using annotation data which is collaboratively generated by many users while watching video. With this system, through simple user interaction the query is automatically generated and all the related information is retrieved from Linked Data and finally all the additional information of the object is offered to the user. With this study, in the future of Semantic Web environment our proposed system is expected to establish a better video content service environment by offering users relevant information about the objects that appear on the screen of any internet-capable devices such as PC, smart TV or smart phone.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.