• The Korea Journal of Herbology
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• v.31 no.4
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• pp.93-100
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• 2016

Objectives : The internet is the most common method to investigate information. It is showed that 75.2% of Internet users of 20s had health information search experience. So this study is aim to understanding of interest of public about the herbal medicine using internet search query volume data.Methods : The Naver that is the top internet portal web service of the Republic of Korea has provided an Internet search query volume data from January 2007 to the current through the Naver data lab (http://datalab.naver.com) service. We have collected search query volume data which was provided by the Naver in 606 herbal medicine names and sorted the data by peak and total search volume.Results : The most frequently searched herbal medicines which has less bias and sorted by peak search volume is 'wasong (와송)'. And the most frequently searched herbal medicines which has less bias and sorted by total search volume is 'hasuo (하수오)'.Conclustions : This study is showed that the rank of interest of public about herbal medicines. Among the above herbal medicines, some herbal medicines had supply issue. And there are some other herbal medicines that had very little demand in Korean medicine market, but highly interested public. So it is necessary to monitor for these herbal medicines which is highly interested of the public. Furthermore if the reliability of the data obtained on the basis of these studies, it is possible to be utilizing herbal medicine monitoring service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.38-43
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• 2004

We designed and implemented a medical image query system, including a relational database and DBMS (database management system), which can visualize image data and can achieve spatial, attribute, and mixed queries. Image data used in querying can be visualized in slice, MPR(multi-planner reformat), volume rendering, and overlapping on the query system. To reduce spatial cost and processing time in the system. brain images are spatially clustered, by an adaptive Hilbert curve filling, encoded, and stored to its database without loss for spatial query. Because the query is often applied to small image regions of interest(ROI's), the technique provides higher compression rate and less processing time in the cases.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.79-88
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• 2009

The volume of memory to store real-time data stream is varied dynamically. Continuous queries processing the data stream must manage the storage volume dynamically. In previous research, according to current volume of data a general memory manager which allocates and releases memory by a page unit is researched.However, the method frequently executes page allocation and release to store data stream. Moreover, particularly delayed queries can monopolize many of pages because the method directly allocates pages when a query has not enough memory. Focusing on the problems in memory management systems, this research proposes a memory management method which reduces the frequency of allocation and release and uniformly distributes pages for queries. The method can reduce the frequency of allocation and release through allocation based on utilization ratio of pages in each query and prevent memory monopoly through memory allocation which considers query delay.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.457-460
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• 2011

For the mobile meteorological services, sensed data should be gathered at a server from various clients like as Ubiquitous Sensor Network, mobile phone or public traffic vehicle by wireless network. The gathered data at server have huge volume and increase continuously. Therefore, a special query method and data structure should be considered. This paper studies on all possible query type on the data and processing steps for the mobile meteorological services. Some query spaces will be discussed. After that, this paper proposes effective data structure for the sensed data to support the query types.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.44-53
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• 2008

According to the widespread use of ontology-based applications, it is critical to efficiently store and process semantic information. Even though several related systems have been developed, they have some limitations in perspectives of the volume of target semantic data, the performance of semantic query processing, and the semantic data maintenance. In this paper we propose the OWL-aware relational model for the ontology management system and SQL-based semantic query processing mechanism. Also, to verify the query processing performance, we show that the proposed query professing mechanism is more efficient than sesame.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.33-40
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• 2010

In this study, it has been intended to present the ways to improve some problems such as the difficulty of using the program which had got from the existing study, the computation and application of a lot of parameter and the complicated processing which need to be more simplified. Also It has been tried to bring up the ways to make a flood inundation map and a detailed inundation analysis which could reduce the risk factors. We selected an Anseong-Cheon basin, and wrote a flood inundation scenario based on extreme flood to exceed the planned frequency to consider only overflow and levee break and executed inundation simulation. Researchers conducted an analysis of overflow and levee break using function of HEC-RAS Storage with a One-Dimensional model. It applied Elevation versus Volume Curve for more correct inundation simulation than a method of Area-Time-Depth which used in popular. This study will suggest a mathematical method of SURFER with a little difference of inundation area more simplified and precise flood inundation than complicated Arcview 3.2a which used Query method of Arcview 3.2a.

• Korean Management Science Review
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• v.32 no.3
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• pp.91-103
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• 2015

This study recognizes that there is a correlation between the movement of the financial market and the sentimental changes of the public participating directly or indirectly in the market, and applies the relationship to investment strategies for stock market. The concerns that market participants have about the economy can be transformed to the search terms that internet users query on search engines, and search volume of a specific term over time can be understood as the economic trend of big data. Under the hypothesis that the time when the economic concerns start increasing precedes the decline in the stock market price and vice versa, this study proposes three investment strategies using casuality between price of domestic stock market and search volume from Naver trends, and verifies the hypothesis. The computational results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior in domestic stock market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2211-2232
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• 2018

The unstructured and semi-structured big data in social network poses new challenges in query retrieval. This requirement needs to be met by introducing quality retrieval time measures like indexing. Due to the huge volume of data storage, there originate the need for efficient index algorithms to promote query processing. However, conventional algorithms fail to index the huge amount of frequently obtained information in real time and fall short of providing scalable indexing service. In this paper, a new LIndex algorithm, which is a heuristic on Lucene is built on Neo4jHA architecture that holds the social network Big data. LIndex is a flexible and simplified adaptive indexing scheme that ascendancy decomposed shortest paths around term neighbors as basic indexing unit. This newfangled index proves to be effectual in query space pruning of graph database Neo4j, scalable in index construction and deployment. A graph query is processed and optimized beyond the traditional Lucene in a time-based manner to a more efficient path method in LIndex. This advanced algorithm significantly reduces query fetch without compromising the quality of results in time. The experiments are conducted to confirm the efficiency of the proposed query retrieval in Neo4j graph NoSQL database.

• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.33-39
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• 2014

How to transfer spatial data from server to client in wireless broadcasting environment is shown as following: A server arranges data information that client wants and transfers data by one-dimensional array for broadcasting cycle. Client listens data transferred by the server and returns resulted value only to server. Recently number of users using location-based services is increasing alongside number of objects, and data volume is changing into large amount. Large volume of data in wireless broadcasting environment may increase query time of client. Therefore, we propose Client based Data Scheduling (CDS) for efficient data scheduling in wireless broadcasting environment. CDS divides map and then calculates total sum of objects for each grid by considering number of objects and data size within divided grids. It carries out data scheduling by applying hot-cold method considering total data size of objects for each grid and number of client. It's proved that CDS reduces average query processing time for client compared to existing method.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.119-121
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• 2008

Pattern matching is increasingly being employed in various researches as health care service, RFID-based system, facility management, and surveillance. Geosensor filter correlates a data stream to match specific patterns in distribution environments. In this paper, we present a geosensor query language to represent efficiently declarative geosensor query. Geosensor operators are proposed to use for fast query processing in terms of spatial and temporal area in distribution environments. We also propose a geosensor filter to match new query predicates into incoming stream predicates. Our filter can reduce the volume of transmission data and save power consumption of sensors. It can be utilized the stream data mining system to process in real-time various data as location, time, and geosensor information in distribution environments.