• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1259-1276
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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 Internet of Things (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 spatialtemporal 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.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.59-66
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• 2008

GeoSensor network means sensor network infra and related software of specific form monitoring a variety of circumstances over geospatial. And these GeoSensor network is implemented by mixing data stream with spatial attribute, spatial relation. But, until a recent date sensor network system has been concentrated on a store and search method of sensor data stream except for a spatial information. In this paper, we propose a definition of spatial data stream and its join strategy model at GeoSensor network, which combine data stream with spatial data. Spatial data stream s defining in this paper are dynamic spatial data stream of a moving object type and static spatial data stream of a fixed type. Dynamic spatial data stream is data stream transmitted by moving sensor as GPS, while static spatial data stream is generated by joining a data stream of general sensor and a relation with location values of these sensors. This paper propose joins of dynamic spatial data stream and static spatial data stream, and cost models estimating join cost. Finally, we show verification of proposed cost models and performance by join strategy.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.137-142
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• 2017

Recently, along with the development of IoT technology, technologies for wirelessly sensing various data, such as sensor nodes, RFID, CCTV, smart phones, etc., have rapidly developed, and in the field of multiple applications, to utilize sensor network related technology Have been actively pursued in various fields. Therefore, as GeoSensor utilization increases, query processing systems for efficiently processing 2D data such as spatial sensor data are actively researched. 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.767-785
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• 2021

With the rapid development of information technology, it is now possible to analyze the spatial patterns of cultivated land and its evolution by combining GIS, geostatistical analysis models and spatiotemporal big data for the dynamic monitoring and management of cultivated land resources. The spatial pattern of cultivated land and its evolutionary patterns in Luoyang City, China from 2009 to 2019 were analyzed using spatial autocorrelation and spatial autoregressive models on the basis of GIS technology. It was found that: (1) the area of cultivated land in Luoyang decreased then increased between 2009 and 2019, with an overall increase of 0.43% in 2019 compared to 2009, with cultivated land being dominant in the overall landscape of Luoyang; (2) cultivated land holdings in Luoyang are highly spatially autocorrelated, with the 'high-high'-type area being concentrated in the border area directly north and northeast of Luoyang, while the 'low-low'-type area is concentrated in the south and in the municipal area of Luoyang, and being heavily influenced by topography and urbanization. The expansion determined during the study period mainly took place in the Luoyang City, with most of it being transferred from the 'high-low'-type area; (3) elevation, slope and industrial output values from analysis of the bivariate spatial autocorrelation and spatial autoregressive models of the drivers all had significant effects on the amount of cultivated land holdings, with elevation having a positive effect, and slope and industrial output having a negative effect.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.99-115
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• 2003

In this paper, we present a knowledge-based object-oriented spatial database system called KOBOS. A knowledge-based approach is introduced to the object-oriented spatial database system for data modeling and approximate query answering. For handling the structure of spatial objects and the approximate spatial operators, we propose three levels of object-oriented data model: (1) a spatial shape model; (2) a spatial object model; (3) an internal description model. We use spatial type abstraction hierarchies(STAHs) to provide the range of the approximate spatial operators. We then propose SOQL, a spatial object-oriented query language. SOQL provides an integrated mechanism for the graphical display of spatial objects and the retrieval of spatial and aspatial objects. To support an efficient hybrid query evaluation, we use the top-down spatial query processing method.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.113-115
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• 2000

A GIS software GEO Millennium SystemTM has been developed to integrated with spatial database that combines conventional and spatially related data. As we known well the standard query language lacks the support of spatial data type and predicate, and can not serve as the query language in the spatial database directly; some extended strategies have been proposed, but some of them need their own storage manager, some introfuce new clause into the SELECT-FROM-WHERE structure, and some is very complex and available to us. So we designed our own query language on the conventional storage manager system. It supports the Spatial Data Type and predicate, and provides the full query capabilities of SQL on the non-spatial part of the database while being tightly integrated with the spatial part, without changing the standard SQL structure.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.162-169
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• 2009

The real-time traffic data is generated continuous and unbounded stream data type while intelligent transport system (ITS) needs to provide various and high quality services by combining with spatial information. Traditional database techniques in ITS has shortage for processing dynamic real-time stream data and static spatial data simultaneously. In this paper, we design and implement an advanced traffic monitoring system (ATMS) with the integration of existed data stream management system (DSMS) and spatial DBMS using IntraMap. Besides, the developed ATMS can deal with the stream data of DSMS, the trajectory data of relational DBMS, and the spatial data of SDBMS concurrently. The implemented ATMS supports historical and one time query, continuous query and combined query. Application programmer can develop various intelligent services such as moving trajectory tracking, k-nearest neighbor (KNN) query and dynamic intelligent navigation by using components of the ATMS.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.89-96
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• 2016

By reviewing preceding studies of big data and spatial big data, spatial big data was defined as one part of big data, which spatialize location information and systematize time series data. Spatial big data, as one part of big data, should not be separated with big data and application methods within the system is to be examined. Therefore in this study, services that spatial big data is required to provide were suggested. Spatial big data must be available of various spatial analysis and is in need of services that considers present and future spatial information. Not only should spatial big data be able to detect time series changes in location, but also analyze various type of big data using attribute information of spatial data. To successfully provide the requirements of spatial big data and link various type of big data with spatial big data, methods of forming sample points and extracting attribute information were proposed in this study. The increasing application of spatial information related to big data is expected to attribute to the development of spatial data industry and technological advancement.

• Journal of Korea Spatial Information System Society
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• v.2 no.2 s.4
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• pp.89-97
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• 2000

This paper deals with the method which can apply spatial-data warehouses into the spatial data infrastructure in the view of clearinghouse. First, we classify the type of available data warehouses in the consider with patterns and properties of spatial data infrastructure. Then we suggest strategies for building and expanding data warehouses into various organizations. After considering all the factors, we suppose how the national spatial data warehouse come into being.

• Journal of the Korean Institute of Educational Facilities
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• v.18 no.5
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• pp.61-71
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• 2011

The purpose of this paper is to analyze university students' types of spatial consumptive decision behavior in order to provide a guideline on development of creative squares on campus. In particular, it is investigated how student's general characteristics, such as gender, age and major, awareness of creative squares, and preferred spatial types have influence on types of spatial decision consumptive behavior. In addition, correlations among the factors and types are analyzed. As a specific method for gathering information, a questionnaire composed of 46 questions was drawn up to conduct a survey of 206 students of S University in Seoul. The collected data from the survey were analyzed using SPSS windows v17.0. Based on a few previous works, the spatial consumptive decision behavior is classified into four types with Crombach ${\alpha}=0.794$ : 'Exploration' type, 'Experience' type, 'Showing-off type, and 'Trend-Following' type. The main results of this paper can be summarized as follows. First, all factors that affect spatial consumptive decision behavior are more or less correlated with one another. Second, awareness of creative square is significantly different according to the age and major of students. Third, in general, the 'Exploration' type embraces the largest number of students, followed by 'Experience' type, 'Showing-off type, and 'Trend-Following' type. Fourth, the portion of students belonging to each type is a little different according to the major of students. Finally, each type of spatial consumptive decision behavior is highly correlated with the gender and preferred spatial types of students. It was also found that all types of students prefer three to five specific spatial types. Accordingly, the identified spatial types can be exploited in developing a creative square in campus. The results of this paper are expected to expedite follow-up research on creative squares on campus under various conditions.