• The Journal of Information Technology
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• v.12 no.1
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• pp.15-20
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• 2009

In this paper, we aim that it solve to problem of the database hoarding because a week connectivity of wireless networks and cutting of link, Consistency of shared data, optimizing of the log include to Mobile Continuous Query Processing System under the mobile client and server environment. and we demonstrate of the superiority for the new Mobile Continuous Query Processing System compare C-I-S(Client-Intercept -Server)model with performance. and we perform to various experiment in order to establishment of superiority compare the index architecture and method for the realtime Continuous Query Processing. in this paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.222-226
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• 2003

Information retrieval must be able to search the most suitable document that user need from document set. If foretell document adaptedness by similarity degree about QL(Query Language) of document, documents that search person does not require are searched. In this paper, showed that can search the most suitable document on user's request searching document of the whole space using genetic algorithm and used knowledge-base operator to solve various model's problem.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.231-242
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• 2008

W3C has selected OWL as a standard for Web ontology description and a necessity of research on storage models that can store OWL ontologies effectively has been issued. Until now, relational model-based storage systems such as Jena, Sesame, and DLDB, have been developed, but there still remain several issues. Especially, they lead inefficient query processing performance. The structural problems of their low query processing performance are as follow: Jena has a simple structure which is not normalized and also stores most information in a single table. It exponentially decreases the performance because of comparison with unnecessary information for processing queries requiring join operations as well as simple search. The structures of storages(e.g., Sesame) have been completely normalized. Therefore it executes many join operations for query processing. The storages require many join operations to find simply a specific class. This paper proposes a storage model to resolve the problems that the query processing performance is decreased because of non-normalization or complete normalization of the existing storages. To achieve this goal, we analyze the problems of existing storage models as well as relations of OWL elements and query patterns. The proposed model, defined with the analysis results, provides an optimal normalized structure to minimize join operations or unnecessary information comparison. For the experiment of query processing performance, a LUBM data sets are used and query patterns are defined considering search targets and their hierarchical relations. In addition, this paper conducts experiments on correctness and completeness of query results to verify data loss of the proposed model, and the results are described. With the comparative evaluation results, our proposal showed a better performance than the existing storage models.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.140-150
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• 2024

Database-as-a-Service is one of the prime services provided by Cloud Computing. It provides data storage and management services to individuals, enterprises and organizations on pay and uses basis. In which any enterprise or organization can outsource its databases to the Cloud Service Provider (CSP) and query the data whenever and wherever required through any devices connected to the internet. The advantage of this service is that enterprises or organizations can reduce the cost of establishing and maintaining infrastructure locally. However, there exist some database security, privacychallenges and query performance issues to access data, to overcome these issues, in our recent research, developed a database security model using a deterministic encryption scheme, which improved query execution performance and database security level.As this model is implemented using a deterministic encryption scheme, it may suffer from chosen plain text attack, to overcome this issue. In this paper, we proposed a new model for cloud database security using nondeterministic encryption, order preserving encryption, homomorphic encryptionand database distribution schemes, andour proposed model supports execution of queries with equality check, range condition and aggregate operations on encrypted cloud database without decryption. This model is more secure with optimal query execution performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.1-8
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• 1997

In this paper, we propose a new intelligent query processing system for relational database !systems. By analyzing previous research results related with fuzzy queries, a new intelligent query processing sysytem is developed and the role of each module including intelligent query processor is defined and :some algorithms for parser, query translation module, inference engine, semantic DB and result com-poser are suggested. By applying a typical example to the proposed intelligent query processing liysytem, reasonable results for the ambiguous query are drawn, and therefore it shows a promising model returning ordered result for both the ambiguous queries and general queries.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• The Journal of Information Systems
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• v.8 no.1
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• pp.27-44
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. CBR has several research issues which can be divided into two categories : (1) static issues and (2) dynamic issues. The static issues are related to case representation scheme and case data model, that is, focus on casebase which is a repository of cases. The dynamic issues, on the other hand, are related to case retrieval procedure and problem solving process, i.e. case adaptation phase. This research is forcused on retrieval procedure Traditional query processing accepts precisely specified queries and only provides exact answers, thus requiring users to fully understand the problem domain and the casebase schema, but returning limited or even null information if the exact answer is not available. To remedy such a restriction, extending the classical notion of query answering to approximate query answering(AQA) has been explored. AQA can be achieved by neighborhood query answering or associative query answering. In this paper, neighborhood query answering technique is used for AQA. To reinforce the CBR process, a new retrieval procedure(cooperative CBR) using neighborhood query answering is proposed. An neighborhood query answering relaxes a query scope to enlarge the search range, or relaxes an answer scope to include additional information. Computer Aided Process Planning(CAPP) is selected as cooperative CBR application domain for test. CAPP is an essential key for achieving CIM. It is the bridge between CAD and CAM and translates the design information into manufacturing instructions. As a result of the test, it is approved that the problem solving ability of cooperative CBR is improved by relaxation technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1168-1188
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• 2018

Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.268-273
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• 2013

In this paper, we propose a document classification model using Web documents as a part of the training corpus in order to resolve the imbalance of the training corpus size per category. For the purpose of retrieving the Web documents closely related to each category, the proposed document classification model calculates the matching score between word features and each category, and generates a Web search query by combining the higher-ranked word features and the category title. Then, the proposed document classification model sends each combined query to the open application programming interface of the Web search engine, and receives the snippet results retrieved from the Web search engine. Finally, the proposed document classification model adds these snippet results as Web documents to the training corpus. Experimental results show that the method that considers the balance of the training corpus size per category exhibits better performance in some categories with small training sets.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.244-246
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• 2002

The rapid increase of E-commerce market requires a solution to assist the buyer to find his or her interested items. The intelligent agent model is one of the approaches to help the buyers in purchasing items in outline market. In this paper, the user query processing model in the item recommendation agent is proposed. In the proposed model, the retrieval result is affected by the automatically generated queries from user preference information as well as the queries explicitly given by user. Therefore, the proposed model can provide the customized search results to each user.