• Journal of KIISE:Databases
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• v.31 no.3
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• pp.243-251
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• 2004

Most programs of bioinformatics provide biochemists and biologists retrieve and analysis services of gene and protein database. As these services retrieve database for each arrival of user's request, it takes a long time and increases server's load and response time. In this paper. by utilizing database retrieval patterns of sequence alignment programs in bioinformatics, grouping method is proposed to share database retrieval between many requests. Carpool method is also proposed to reduce response time as well as to increase system expandability by combining new arriving requests with the previous on going requests. The performance of our two proposed schemes is verified by mathematic analysis and simulation.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.297-306
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• 2004

Push-based broadcasting in wireless information services is a very effective technique to disseminate information to a massive number of clients when the number of data items is small. When the database is large, however, it nay be beneficial to integrate a pull-based (client-to-server) backchannel with the push-based broadcast approach, resulting in a hybrid data delivery. In this paper, we analyze the performance behavior of a predeclaration-based transaction processing, which was originally devised for a push-based data broadcast, in the hybrid data delivery through an extensive simulation. Our results show that the use of predeclaration-based transaction processing can provide significant performance improvement not only in a pure push data delivery, but also in a hybrid data delivery.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.445-456
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• 2004

The database schema is assumed to be stable enough to remain valid even as the modeled environment changes. However, in practice. data models are not nearly as stable as commonly assumed by the database designers. Even though a rich set of schema change operations is provided in current database systems, the users suffer from the problem that schema change usually impacts existing application programs that have been written against the schema. In this paper, we are exploring the possible solutions to overcome this problem of impacts on the application programs. We believe that for continued support of the existing programs on the old schema, the old schema should continue to allow updates and queries, as before. Furthermore, its associated data has to be kept up-to-date. We call this the program independency property of schema change tools. For this property. we devise so-called program independency schema evolution (PISE) methodology. For each of the set of schema change operations in the relational schemas, the sketch of the additional algorithms due to the PISE compliance is presented in order to prove the comprehensiveness and soundness of our PISE methodology.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.84-97
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• 2007

Selectivity estimation techniques in query optimization have been used in commercial databases and histograms are popularly used for the selectivity estimation. Recently, the techniques for spatio-temporal databases have been restricted to existing temporal and spatial databases. In addition, the selectivity estimation techniques focused on time-series data such as moving objects. It is also impossible to estimate selectivity for range queries with a time interval. Therefore, we construct two histograms, CMH (current multidimensional histogram) and PMH (past multidimensional histogram), to estimate the selectivity of multidimensional sequence data in spatio-temporal databases and propose effective selectivity estimation methods using the histograms. Furthermore, we solve a problem about the range query using our proposed histograms. We evaluated the effectiveness of histograms for range queries with a time interval through various experimental results.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.563-573
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• 2006

Cyber-infrastructures for scientific and engineering applications require integrating heterogeneous legacy data in different formats and from various domains. Such data integration raises challenging issues: (1) Support for multiple independently-managed schemas, (2) Ease of schema evolution, and (3) Simple schema mappings. In order to address these issues, we propose a novel approach to semantic integration of scientific data which uses XML schemas and RDF-based schema mappings. In this approach, XML schema al-lows scientists to manage data models intuitively and to use commodity XML DBMS tools. A simple RDF-based ontological representation scheme is used for only structural relations among independently-managed XML schemas from different institutes or domains We present the design and implementation of a prototype system developed for the national cyber-environments for civil engi-neering research activities in Korea (similar to the NEES project in USA) which is called KOCEDgrid (http://www.koced.net).

• Journal of KIISE:Information Networking
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• v.30 no.2
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• pp.167-178
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• 2003

In mobile computing environments, queries based on the location of mobile clients (MCs) may cause different results. We say that the data of these results are location dependent data (LDD). Location-dependent queries to LDD need to be processed in conjunction with the geographical distance. The efficiency of query processing may also be increased by LDD relationship, etc. But there is the problem of fuzziness about how the distance used in location-dependent queries is evaluated and the data regions are organized. In this paper, we quantify the fuzziness of a location-dependent fuery on LDD. And we propose data regions for LDD, called LDD regions, by relationship of accessed data and the degree of distance between data objects and MCs' locations. In simulation studies we show that the number of database access for location-dependent queries, which have several settings on MCs' favor and two granularity of regions, can be smaller in proposed LDD regions than that in geographical regions.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.362-372
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• 2004

One of the most difficult problems in building a distributed GIS lies in the heterogeneity of spatial databases. In particular, positional mismatches between spatial databases, which arise due to several reasons, may incur incorrect query results. They result in unreliable outputs of query processing. One simple solution is to correct positional data in spatial databases at each site, according to the most accurate one. This solution is however not practical in cases where the autonomy of each database should be respected. In this paper, we propose a spatial query processing method without correcting positional data in each spatial database. Instead of correcting positional data, we dynamically transform a given query region or position onto each space where spatial objects of each site are located. Our proposed method is based on an elastic transformation method by using delaunay triangulation. Accuracy of this method is proved mathematically, and is confirmed by an experiment. Moreover, we implemented using common use database system for usefulness verification of this method.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.464-475
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• 2003

few sample objects and compressed histogram information of image databases. The histogram information is used to estimate the selectivity of spherical range queries and a small number of sample objects is used to compensate the selectivity error due to the difference of the similarity measures between meta server and local image databases. An extensive experiment on a large number of image data demonstrates that our proposed method performs well in the distributed heterogeneous environment.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.667-680
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• 2007

The bio-data processing is used for a suitable purpose with bio-signals, which are obtained from human individuals. Recently, there is increasing demand that the bio-data has been widely applied to various applications. However, it is often that the number of data within each class is limited and the number of classes is large due to the property of problem domain. Therefore, the conventional pattern recognition systems and classification methods are suffering form low generalization performance because the system using the lack of data is influenced by noises of that. To solve this problem, we propose a modified additive factor model for bio-data generation, with two factors; the class factor which affects properties of each individuals and the environment factor such as noises which affects all classes. We then develop a classification system through defining a new similarity function using the proposed model. The proposed method maximizes to use an information of the class classification. So, we can expect to obtain good generalization performances with robust noises from small number of datas for bio-data. Experimental results show that proposed method outperforms significantly conventional method with real bio-data.