• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.568-571
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• 2004

In this study, we propose an efficient expert system (ES) construction mechanism by using dynamic knowledge map (DKM) and database management systems (DBMS). Generally, traditional ES and ES developing tools has some limitations such as, 1) a lot of time to extend the knowledge base (KB), 2) too difficult to change the inference path, 3) inflexible use of inference functions and operators. First, to overcome these limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. Then, elation database (RDB) and its management systems will help to transform the relationships from diagram to relational table. Therefore, our mechanism can help the ES or KBS (Knowledge-Based Systems) developers in several ways efficiently. In the experiment section, we used medical data to show the efficiency of our mechanism. Experimental results with various disease show that the mechanism is superior in terms of extension ability and flexible inference.

• Journal of Information Science Theory and Practice
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• v.1 no.4
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• pp.48-66
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• 2013

The aim of this study is to examine the user's online searching behavior in IR interfaces of e-journal database systems. The study is purely based on survey methods and tries to analyse the online searching behavior of respondents of social science disciplines who were doing research in three target central universities of Delhi (i.e. DU, JMI, and JNU). For measuring the responses of the respondents in IR interfaces of e-journal database systems, a total of 396 questionnaires were distributed among the students and out of all, 305 responses were used for the study. The findings of the study reveal that most of the students were not using all the facilities offered in IR interfaces of e-journal database systems for their retrieval process and also encourages menu based searches rather than command based searching.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.47
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• pp.139-148
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• 1998

CALS IDB (Integrated database) is one of core technologies that embodies the principle of a shared data environment for the life cycle related data in CALS environment. In this study, to successfully share the data, we first classified the data types employed in the CALS environment and then discussed the data heterogeneity issued in data integration processes. To effectively solve this heterogeneity, we proposed the federated database systems as a candidate system especially focusing on the major functions and core element technologies.

• Journal of Information Technology Applications and Management
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• v.12 no.2
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• pp.29-43
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• 2005

The paper presents the EBORD (Extended Benchmark for Object-Relational Databases) benchmark, which is an extension of the BORD benchmark for object-relational databases. The EBORD benchmark is developed to evaluate the database common functions that should be supported in modern database systems. Besides the 36 test queries already defined in the BORD benchmark, totally 22 test queries in five categories are newly defined in order to measure the index-relevant performance issues and database import capabilities. The EBORD benchmark also features scalability, use of a synthesized database, and a query-oriented evaluation. In order to show the feasibility of the proposed benchmark, we implement it with two commercial database systems. The experimental results and analyses are also reported.

• IE interfaces
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• v.17 no.2
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• pp.226-232
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• 2004

In shipbuilding area, data sharing is one of the crucial issues. Recently, for collaborative design, ship structural CAD systems adopt the database as its primary storage. Database is useful to deal with the large amount of design information among the heterogeneous design department and design stage. To make the database-based CAD system object-oriented database(OODB) and object-relational database(ORDB) can be used. It is important to select proper database because the CAD system performance mainly depends on access performance of database. In this research, using prototype CAD system from other research, access performance of OODB and ORDB form CAD system was evaluated. STEP application protocol was used as the database schema and experiment was made in query by property and query by region. The results give some idea of how to choose the database for CAD systems.

• The KIPS Transactions:PartD
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• v.11D no.4
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• pp.747-754
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• 2004

Database administrators should be aware of performance characteristics of database systems in order to manage database system effectively. The usages of system resources in database systems could be quite different under database workloads. The objective of this paper is to identify and analyze performance characteristics of database systems in different workloads, which could help database tuners tune database systems Under the TPC-C and TPC-W workloads, which represent typical workloads of online transaction processing and electronic commerce respectively, we investigated usage types of resource that are determined by fourteen performance indicator, and are behaved in response to changes of four tuning parameters (data buffer, private memory, I/O process, shared memory). Eight out of the fourteen performance indicators cleary show the performance differences under the workloads. Changes of data buffer parameter give a influences to database system. The tuning parameter that affects the system performance significantly is the database buffer size in the both workloads.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.46-51
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• 2007

In the proteomics research using mass spectrometry, the protein database search gives the protein information from the peptide sequences that show the best match with the tandem mass spectra. The protein sequence database has been a powerful knowledgebase for this protein identification. However, as we accumulate the protein sequence information in the database, the database size gets to be huge. Now it becomes hard to consider all the protein sequences in the database search because it consumes much computing time. For the high-throughput analysis of the proteome, usually we have used the non-redundant refined database such as IPI human database of European Bioinformatics Institute. While the non-redundant database can supply the search result in high speed, it misses the variation of the protein sequences. In this study, we have concerned the proteomics data in the point of protein similarities and used the network analysis tool to build a new analysis method. This method will be able to save the computing time for the database search and keep the sequence variation to catch the modified peptides.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.49-52
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• 2003

In this research, we propose a mechanism to develop an inference engine and expert systems based on relational database and SQL (structured query language). Generally, former researchers had tried to develop an expert systems based on text-oriented knowledge base and backward/forward (chaining) inference engine. In these researches, however, the speed of inference was remained as a tackling point in the development of agile expert systems. Especially, the forward inference needs more times than backward inference. In addition, the size of knowledge base, complicate knowledge expression method, expansibility of knowledge base, and hierarchies among rules are the critical limitations to develop an expert systems. To overcome the limitations in speed of inference and expansibility of knowledge base, we proposed a relational database-oriented knowledge base and forward inference engine. Therefore, our proposed mechanism could manipulate the huge size of knowledge base efficiently, and inference with the large scaled knowledge base in a short time. To this purpose, we designed and developed an SQL-based forward inference engine using relational database. In the implementation process, we also developed a prototype expert system and presented a real-world validation data set collected from medical diagnosis field.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.885-890
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• 2002

As Geographic Information Systems represents three-dimensional topological Information, the systems provide accurate and delicate services for user. In order to execute three-dimensional topological operations, a dimensional transformation and heterogeneous spatial models should be used. However, the existing systems that use the dimensional transformation and the heterogeneous models, are not only difficult to operate the spatial operators, but also happened to support non- interoperability. Therefore, in order to support the spatial operation as well as interoperability between dimensions, we propose three-dimensional spatial operators for the proposed models. We defined the three-dimensional spatial operators prior to designing the proposed model. We also implemented the operators of proposed model and evaluated the implemented model on the component environment. Finally, the proposed model is able to not only support interoperability among systems but also execute spatial queries efficiently on three-dimensional spatial objects.