• YAKHAK HOEJI
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• v.52 no.4
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• pp.283-287
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• 2008

Drug identification service and other drug-related query service are becoming increasingly important in hospital pharmacy. The goal of this research was to investigate current situation of the service in hospital pharmacy, which recently implemented the services as part of provision of advanced hospital pharmacy service in order to assure national health improvement. We investigated the report performed from November 2006 through April 2007 in a university hospital located in Daegu, Korea. Number of drug identification service performed was 81 cases during the first three months period (period I), but it increased to 222 cases during the second three months period (period II), which suggested that the service was welcomed by medical staff in the hospital. Time to process each case was about 30 minutes in the period I while it was only 16 minutes in the period II. Proportion of the unidentifiable cases remained at about 25% during the entire period, which suggests that the system for the identification task appears to have some limitations such as unsatisfactory support from the Korea Pharmaceutical Association, laws, and regulations. A vast majority of drug-related queries were mostly from physicians (60.5%) followed by nurses and pharmacists. Time to process each drug-related query was 10.6 minutes in the period I while it was 6.9 minutes in the period II. Queries answered immediately were about 70% of all queries in the period I, but increased to about 85% in the period II.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1541-1550
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• 2017

The increasing number of multimedia content offered to the user demands content recommendation. In this paper, we propose a system for recommending content related to the content that user is watching. In the proposed system, relationship information between content is generated using relationship information between representative keywords of content. Relationship information between keywords is generated by analyzing keyword collocation frequencies in Internet news corpus. In order to handle big corpus data, we design an architecture that consists of a distributed search engine and a distributed data processing engine. Furthermore, we store relationship information between keywords and relationship information between keywords and content in NoSQL to handle big relationship data. Because the query optimizer of NoSQL is not as well developed as RDBMS, we propose query optimization techniques to efficiently process complex queries for recommendation. Experimental results show that the performance is improved by up to 69 times by using the proposed techniques, especially when the number of requested related keywords is small.

• Journal of KIISE
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• v.43 no.12
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• pp.1437-1457
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• 2016

In outsourced databases, the cloud provides an authorized user with querying services on the outsourced database. However, sensitive data, such as financial or medical records, should be encrypted before being outsourced to the cloud. Meanwhile, k-Nearest Neighbor (kNN) query is the typical query type which is widely used in many fields and the result of the kNN query is closely related to the interest and preference of the user. Therefore, studies on secure kNN query processing algorithms that preserve both the data privacy and the query privacy have been proposed. However, existing algorithms either suffer from high computation cost or leak data access patterns because retrieved index nodes and query results are disclosed. To solve these problems, in this paper we propose a new kNN query processing algorithm on the encrypted database. Our algorithm preserves both data privacy and query privacy. It also hides data access patterns while supporting efficient query processing. To achieve this, we devise an encrypted index search scheme which can perform data filtering without revealing data access patterns. Through the performance analysis, we verify that our proposed algorithm shows better performance than the existing algorithms in terms of query processing times.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.220-230
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• 2013

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.

• Journal of Information Processing Systems
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• v.5 no.1
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• pp.11-18
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• 2009

As well as providing various APIs for the development of inference engines and storage models, Jena is widely used in the development of systems or tools related with Web ontology management. However, Jena still has several problems with regard to the development of real applications, one of the most important being that its query processing performance is unacceptable. This paper proposes a storage model to improve the query processing performance of the original Jena storage. The proposed storage model semantically classifies OWL elements, and stores an ontology in separately classified tables according to the classification. In particular, the hierarchical knowledge is managed, which can make the processing performance of inferable queries enhanced and stores information. It enhances the query processing performance by using hierarchical knowledge. For this paper an experimental evaluation was conducted, the results of which showed that the proposed storage model provides a improved performance compared with Jena.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.11-20
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• 1993

In the new database applications such as office information systems, CAD/CAM, and AI, it is required to support not only fixed Is-A and Part-Of relationships but also various user-defined dynamic relationships including complicate constraints. However, existing object-oriented systems have many weaknesses in managing those complex relationships. This paper presents the Object-Oriented Relationship data Model (OORM) which is designed to provide facilities for modeling complex relationships into object oriented databases using abstraction concept. In the model, various integrity and consistency constraints related to the relationships can be also represented. And this paper presents a query language, ORSQL(Object Relationship SQL). ORSQL is a nonprocedural query language having similiar syntax to the standard SQL and supporting OORM's operations.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1269-1275
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• 2003

This paper addresses several issues on data caching and consistency of cached data in order to process client's queries efficiently in client/server environments. For the purpose, first of all, materialized spatial views are adapted in a client side for data caching, which is called client views. Also, an incremental update scheme using derivation relationships is applied to keep cached data of clients consistent with the rest of server databases. Materialized views support efficient query processing in a client side, however, it is difficult to keep consistent their contents by the update of a server database. In this paper, we devise cost functions on query execution and view maintenance based the cost of spatial operators so as to process client's queries efficiently. When request the client's query, in our query processing scheme, the server determines whether or not materialize it as a view due to evaluation using the related cost functions. Since the scheme supports a hybrid approach based on both view materialization and re-execution, hence, it should improve query execution times in client/server environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.139-144
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of IoT technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatial-temporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.