• Journal of the Korean association of regional geographers
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• 제3권2호
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• pp.105-120
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• 1997

One of the most important issues of object-oriented geographical database system is to develop an indexing technique which enables more efficient I/O processing within aggregation hierarchy or inheritance hierarchy. Up to present, several indexing schemes have been developed for this purpose. However, they have separately focused on aggregation hierarchy or inheritance hierarchy of object-oriented data model. A recent research is proposing a nested-inherited index which combines these two hierarchies simultaneously. However, this new index has some weak points. It has high storage costs related to its use of auxiliary index. Also, it cannot clearly represent the inheritance relationship among classes within its index structure. To solve these problems, this thesis proposes a pointer-chain index. Using pointer chain directory, this index composes a hierarchy-typed chain to show the hierarchical relationship among classes within inheritance hierarchy. By doing these, it could fetch the OID list of objects to be retrieved more easily than before. In addition, the pointer chain directory structure could accurately recognize target cases and subclasses and deal with "select-all" typed query without collection of schema semantic information. Also, it could avoid the redundant data storing, which usually happens in the process of using auxiliary index. This study evaluates the performance of pointer chain indexing technique by way of simulation method to compare nested-inherited index. According to this simulation, the pointer chain index is proved to be more efficient with regard to storage cost than nested-inherited index. Especially in terms of retrieval operation, it shows efficient performance to that of nested-inherited index.

• Journal of Internet Computing and Services
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• 제6권2호
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• pp.99-115
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• 2005

Extensible Markup Language (XML) is an Internet standard that is used to express and convert data, In order to find the necessary information out of XML documents, you need a search system for XML documents, In this research, we have developed a search system that can find documents that matches the structure and content of a given XML document, making the best use of XML structure, Search metrics take account of the similarity in tag names, tag values, and the structure of tags, After a search, the system displays the ranked results in the order of aggregate similarity, Three methods of query are provided: keyword search which is conventional; search with tag names and their values; and search with XML documents, These three methods enable users to choose the method that best suits their preference, resulting in the increase of the usefulness of the system.

• The Journal of Society for e-Business Studies
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• 제13권2호
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• pp.213-223
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• 2008

The efficient discovery of services from a large scale collection of services has become an important issue[7, 24]. We studied a syntactic method for Web service discovery, rather than a semantic method. We regarded a service discovery as a retrieval problem on the proprietary XML formats, which were service descriptions in a registry DB. We modeled services and queries as probabilistic values and devised similarity-based retrieval techniques. The benefits of our way are follows. First, our system supports ranked service retrieval by keyword search. Second, we considers both of UDDI data and WSDL definitions of services amid query evaluation time. Last, our technique can be easily implemented on the off-theshelf DBMS and also utilize good features of DBMS maintenance.

• Journal of KIISE
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• 제42권5호
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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 KIISE:Databases
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• 제29권2호
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• pp.110-118
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• 2002

This paper presents a 3-D graphic database system based on XML that supports content-based retrievals of 3-D images, Most of graphics application systems are currently centered around the processing of 2-D images and research works on 3-D graphics are mainly concerned about the visualization aspects of 3-D image. They do not support the semantic modeling of 3-D objects and their spatial relations. In our data model, 3-D images are represented as compositions of 3-D graphic objects with associated spatial relations. Complex 3-D objects are mode]ed using a set of primitive 3-D objects rather than the lines and polygons that are found in traditional graphic systems. This model supports content-based retrievals of scenes containing a particular object or those satisfying certain spatial relations among the objects contained in them. 3-D images are stored in the database as XML documents using 3DGML DTD that are developed for modeling 3-D graphic data. Finally, this paper describes some examples of query executed in our Web-based prototype database system.

• Journal of the Korea Society of Computer and Information
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• 제11권4호
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• pp.1-7
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• 2006

Recently, studies of relevance feedback to increase the performance of image retrieval has been activated. In this Paper a new region weighting method in region based image retrieval with relevance feedback is proposed to reduce the semantic gap between the low level feature representation and the high level concept in a given query image. The new weighting method determines the importance of regions according to the spatial locations of regions in an image. Experimental results demonstrate that the retrieval quality of our method is about 18% in recall better than that of area percentage approach. and about 11% in recall better than that of region frequency weighted by inverse image frequency approach and the retrieval time of our method is a tenth of that of region frequency approach.

• Smart Structures and Systems
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• 제29권1호
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• pp.195-206
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• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• Journal of Intelligence and Information Systems
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• 제17권3호
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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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• 제41권10호
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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.

• Journal of KIISE
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• 제44권8호
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• pp.813-821
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• 2017

Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user's query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.