• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2160-2179
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• 2015

In many Radio Frequency Identification (RFID) applications, the reader recognizes the tags within its scope repeatedly. For these applications, some algorithms such as the adaptive query splitting algorithm (AQS) and the novel semi-blocking AQS (SBA) were proposed. In these algorithms, a staying tag retransmits its ID to the reader to be identified, even though the ID of the tag is stored in the reader's memory. When the length of tag ID is long, the reader consumes a long time to identify the staying tags. To overcome this deficiency, we propose a slot allocated blocking anti-collision algorithm (SABA). In SABA, the reader assigns a unique slot to each tag in its range by using a slot allocation mechanism. Based on the allocated slot, each staying tag only replies a short data to the reader in the identification process. As a result, the amount of data transmitted by the staying tags is reduced greatly and the identification rate of the reader is improved effectively. The identification rate and the data amount transmitted by tags of SABA are analyzed theoretically and verified by various simulations. The simulation and analysis results show that the performance of SABA is superior to the existing algorithms significantly.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.39-46
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• 2002

In distributed spatial database systems, users nay issue a query that joins two relations stored at different sites. The sheer volume and complexity of spatial data bring out expensive CPU and I/O costs during the spatial join processing. This paper shows a new spatial join method which joins two spatial relation in a parallel way. Firstly, the initial join operation is divided into two distinct ones by partitioning one of two participating relations based on the region. This two join operations are assigned to each sites and executed simultaneously. Finally, each intermediate result sets from the two join operations are merged to an ultimate result set. This method reduces the number of spatial objects participating in the spatial operations. It also reduces the scope and the number of scanning spatial indices. And it does not materialize the temporary results by implementing the join algebra operators using the iterator. The performance test shows that this join method can lead to efficient use in terms of buffer and disk by narrowing down the joining region and decreasing the number of spatial objects.

• The Journal of Korean Association of Computer Education
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• v.8 no.6
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• pp.113-121
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• 2005

Since documents on the Web are naturally partitioned into many document databases, the efficient information retrieval (IR) process requires identifying the document databases that are most likely to provide relevant documents to the query and then querying the identified document databases. We first introduce a neural net agent for such an efficient IR, and then propose the hierarchically organized multi-agent IR system in order to scale our agent with the large number of document databases. In this system, the hierarchical organization of neural net agents reduced the total training cost at an acceptable level without degrading the IR effectiveness in terms of precision and recall. In the experiment, we introduce two neural net IR systems based on single agent approach and multi-agent approach respectively, and evaluate the performance of those systems by comparing their experimental results to those of the conventional statistical systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.101-108
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• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• The Journal of Society for e-Business Studies
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• v.10 no.3
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• pp.111-134
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• 2005

Knowledge management is a core factor to achieve competitive advantage and improve the business performance. New information technology is also a core factor enabling the innovation of knowledge management. Semantic Web of which the goal is to realize machine-processable Web can't help affecting the knowledge management. Therefore, we empirically analyze the relationship between user's dissatisfaction and barriers or limitations of knowledge management and present methods allowing Semantic Web to overcome the limitations and to support knowledge management processes. Based on a questionnaire survey of 222 respondents, we found that the limitations of system qualities such as user inconvenience of knowledge management systems, search and integration limitations, and the limitations of knowledge qualities such as inappropriateness and untrust significantly affected the user dissatisfaction of knowledge management systems. Finally, we suggest a conceptual model of knowledge management systems of which components are resources, metadata, ontologies, and user & query layers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.8
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• pp.1750-1755
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• 2011

Recently, it is possible for users to acquire necessary data easily as the various schemes of the searching information are developed. Since these data rise continuously like stream data, it is required to extract the appropriate data for the user's needs from the mass data on the internet. In the traditional scheme, they are acquired by processing the user queries after the occurred data are stored at a database. However, it is inefficient to process the user queries over the large volume of continuous data by using the traditional scheme. In this paper, we propose the Event-Profile Model to define the data occurrence on the internet as the events and the user's requirements as the profiles. We also propose and implement the filtering scheme to process the events and the profiles efficiently. We evaluate the performance of the proposed scheme and our experiments show that the new scheme outperforms the other on various dataset.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1156-1166
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• 1999

의사결정 시스템은 전사적인 의사결정과 전략적 정보수집을 위해 거대한 량의 정보를 빠른 시간내에 제공할 것을 요구한다. 데이타 웨어하우스는 이러한 정보를 신속히 제공하기 위해 여러 지역 데이타베이스로부터 필요한 정보를 사전에 추출하고 가공 및 통합하여 별도의 저장공간에 저장한다. 일반적으로, 웨어하우스 내의 정보는 지역 데이타베이스에 저장된 정보에 대한 실체화된 뷰로서 간주하며 지역 데이타의 변경에 따라 일관성을 유지하도록 반영해야 한다. 본 논문에서는 일관성을 유지하기 위해 정보 공유가 가능한 데이타 웨어하우스 시스템의 구조와 비-보상 실체 뷰 관리 기법을 제안한다. 본 논문에서 제안한 데이타 웨어하우스 시스템의 구조는 지역 데이타베이스에서 추출된 정보를 관리하는 별도의 지역 정보 관리자를 두어 뷰 관리자들 간의 정보 공유가 가능하게 한다. 비-보상 실체 뷰 관리 기법은 지역 데이타 변경 사건에 따른 뷰 관리 시 다른 사건에 의해 영향을 받지 않도록 하기 때문에 기본의 사전 보상이나 나중 보상 기법과는 달리 추가적인 질의 처리를 요구하지 않는 기법이다.Abstract A decision support system(DSS) commonly requires fast access to tremendous volume of information. A data warehouse is a database storing the information that is extracted, filtered and integrated from several relevant local databases to reply upon aggregated queries. The information stored in the data warehouse can be regarded as materialized views. The materialized view has to be modified according to the change of the corresponding local databases to preserve the data consistency. In this paper, we propose a data warehousing system architecture allowing information sharing (DAWINS), and a non-compensating materialized view maintenance algorithm(NCA). DAWINS architecture allows relevant information to be shared by individual view managers with local data manager for each local database. Unlikely to the pre- or post-compensating algorithms, which are required to remove the effects of some events to other view in the process of view maintenance, NCA does not require any additional query processing, since a local data manager in DAWINS already maintains the effects of update events occurring in local systems.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1374-1385
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• 2004

The relevance feedback process uses information obtained from a user about an initially retrieved set of documents to improve subsequent search formulations and retrieval performance. In the extended Boolean model, the relevance feedback Implies not only that new query terms must be identified, but also that the terms must be connected with the Boolean AND/OR operators properly Salton et al. proposed a relevance feedback method for the extended Boolean model, called the DNF (disjunctive normal form) method. However, this method has a critical problem in generating a reformulated queries. In this study, we investigate the problem of the DNF method and propose a relevance feedback method using hierarchical clustering techniques to solve the problem. We show the results of experiments which are performed on two data sets: the DOE collection in TREC 1 and the Web TREC 10 collection.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.157-164
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• 2006

Since XML is emerging as the de facto standard for exchanging and representation of data on the web, the amount of XML data has rapidly increased. Thus, the need for effective store and retrieval of U data has arisen. Since the existing techniques such as XRel which is an XML storage and management technique using RDBMS simply record the existing all label paths, diverse classes of label path expressions could not be efficiently supported. In this paper, we present a technique which supports storage and retrieval for XML data using RDBMS efficiently compared with the existing approaches. Since the proposed technique keeps the XML path index on the relational database and replace label paths with path identifiers, diverse XML queries can be evaluated compared with existing approaches. Also, the proposed technique does not require the modification of the relational database engine and consumes the disk space less. Our experimental result demonstrates the better query performance compared with existing techniques.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.732-736
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• 2010

Processing a large amount of data becomes more important than ever. Particularly, the information queries which require multi-dimensional record scan can be efficiently implemented with SIMD instruction sets. In this article, we present a SIMD record scan technique which employs row-based scanning. Our technique is different from existing SIMD techniques for predicate processes and aggregate operations. Those techniques apply SIMD instructions to the attributes in the same column of the database, exploiting the column-based record organization of the in-memory database systems. Whereas, our SIMD technique is useful for multi-dimensional record scanning. As the sizes of registers and the memory become larger, our row-based SIMD scan can have bigger impact on the performance. Moreover, since our technique is orthogonal to the parallelization techniques for multi-core processors, it can be applied to both uni-processors and multi-core processors without too many changes in the software architectures.