• Journal of KIISE:Computing Practices and Letters
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• v.6 no.1
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• pp.1-10
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• 2000

Spatial analysis in GIS provides an important way that helps the end-users decision-making. For such a reason, query model for analysis should be able to support the users view conceptually in constructing query statements. The traditional approaches in design of query model used to extend the functionality of model that basically designed for manipulation of attribute-operations by appending operators for spatial operations to the query statements of model. However, by the reason of spatial operation's characteristics that are different from those of attribute operations In nature, the structures of query statements in previous approaches are unnatural, inconsistent, and therefore those query models in previous approaches are not able to support the users view in retrieving analysis. In this paper, we proposed the methodology for constructing of user query and internal processing this query based on object-oriented paradigm, in the view of spatial operations by using the basic concept that spatial query is a methodology for spatial analysis. In addition, we presented a strong possibility of designing spatial query model that might actively have interaction with its user by implementing CIW(Class-Information Window) query interface corresponded with the methodology proposed in this paper.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.396-417
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• 2018

GR-tree and query aggregation techniques have been proposed for spatial query processing in conventional spatial query processing for wireless sensor networks. Although these spatial query processing techniques consider spatial query optimization, time query optimization is not taken into consideration. The index reorganization cost and communication cost for the parent sensor nodes increase the energy consumption that is required to ensure the most efficient operation in the wireless sensor node. This paper proposes itinerary-based R-tree (IR-tree) for more efficient spatial-temporal query processing in wireless sensor networks. This paper analyzes the performance of previous studies and IR-tree, which are the conventional spatial query processing techniques, with regard to the accuracy, energy consumption, and query processing time of the query results using the wireless sensor data with Uniform, Gauss, and Skew distributions. This paper proves the superiority of the proposed IR-tree-based space-time indexing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.1-8
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• 2017

In this paper we present a spatial big data query processing system that can store spatial data in Hadoop and query the data with SQL-based query language. The system stores large-scale spatial data in HDFS-based storage system, and supports spatial queries expressed in SQL-based query language extended for spatial data processing. It supports standard spatial data types and functions defined in OGC simple feature model in the query language. This paper presents the development of core functions of the system including query language parsing, query validation, query planning, and connection with storage system. We compares the performance of the suggested system with an existing system, and our experiments show that the system shows about 58% performance improvement of query execution time over the existing system when executing region query for spatial data stored in Hadoop.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.89-98
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• 2009

As data stream is entered into system continuously and the memory space is limited, the data exceeding the memory size cannot be processed. In order to solve the problem, load shedding methods which drop a part of data to prevent exceeding the storage space have been researched. Generally, a traditional load shedding method uses random sampling with optimized rate according to data deviation. The method samples data not to distinguish those used in spatial query because the method uses only a random sampling with optimized rate according to data deviation. Therefore, the accuracy of query was reduced in u-GIS environment including spatial query. In this paper, we researched a new load shedding method improving accuracy of the query in u-GIS environment which runs spatial query and aspatial query simultaneously. The method uses a new sampling method that samples data having low probability used in query. Therefore proposed method improves spatial query accuracy and query processing speed as applying spatial filtering operation to sampling operator.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.860-879
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• 2002

Traditionally, Geographical Information Systems can only process spatial data in a procedure-oriented way, and the data can't be treated integrally. This method limits the development of spatial data applications. A new and promising method to solve this problem is the spatial structural query language, which extends SQL and provides integrated accessing to spatial data. In this paper, the theory of spatial structural query language is discussed, and a new geographical data model based on the concepts and data model in OGIS is introduced. According to this model, we implemented a spatial structural query language G/SQL. Through the studies of the 9-Intersection Model, G/SQL provides a set of topological relational predicates and spatial functions for GIS application development. We have successfully developed a Web-based GIS system-WebGIS-using G/SQL. Experiences show that the spatial operators G/SQL offered are complete and easy-to-use. The BNF representation of G/SQL syntax is included in this paper.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.452-457
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• 2015

In this paper, we propose a spatial query processor, SQUERY, which can derive rich query results through spatial reasoning on the initial knowledge base, as well as, process both qualitative and quantitative queries about the topological and directional relationships between spatial objects. In order to derive richer query results, the query processor expands the knowledge base by applying forward spatial reasoning into the initial knowledge base in a preprocessing step. The proposed query processor uses not only qualitative spatial knowledge describing topological/directional relationship between spatial objects, but also utilizes quantitative spatial knowledge including geometric data of individual spatial objects through geometric computation. The results of an experiment with the OSM(Open Street Map) spatial knowledge base demonstrates the high performance of our spatial query processing system.

• 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 KIISE:Databases
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• v.30 no.3
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• pp.286-295
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• 2003

The shape of query windows for spatial selection queries is a rectangle in many cases. However, it can be issued for spatial selection queries with not only rectangular query widow, but also polygonal query window. Moreover, as the applications like GIS can manage much more spatial data, they can support the more various applications. Therefore it is valuable for considering about the query processing method suitable for not only rectangle query window, but also general polygonal one. It is the general state-of-the-art approach to use the plane- sweep technique as the computation algorithm in the refinement step as the spatial join queries do. However, from the observation on the characteristics of spatial data and query windows, we can find in many cases that the shape of query window is much simpler than that of spatial data. From these observations, we suggest a new refinement process approach which is suitable for this situation. Our experiments show that, if the number of vertices composing the query window is less than about 20, the new approach we suggest is superior to the state-of-the-art approach by about 20% in general cases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8370-8377
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• 2015

In spatial data stream management systems, it is needed appropriate load shedding algorithm because real-time input spatial data streams could exceed the limitation of main memory. However previous researches, lack regard for input ratio and spatial utilization rates of spatial data streams, or the characteristics of data source which generates data streams with spatial information efficiently, can lead to decrease the performance and accuracy of spatial data stream query. Therefore, multi-level load shedding scheme for spatial data stream management systems is proposed to increase the spatial query performance and accuracy. This proposed scheme limits overloads in relation to the input rate and the characteristics of data source first, and then, if needed, query data representing low query participation probability based on spatial utilizations are dropped relatively. Our experiments show that the proposed method could decrease load shedding frequency for previous researches by more than 11% despite query results accuracy and query performance are superior at 0.04% and 3%.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.290-299
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• 2008

We propose the query optimization technique using query classification in hybrid spatial DBMS. In our approach, user queries should to be classified into three types: memory query, disk query, and hybrid query. Specialty, In the hybrid query processing, the query predicate is divided by comparison between materialized view creating conditions and user query conditions. Then, the deductions of the classified queries' cost formula are used for the query optimization. The optimization is mainly done by the selection algorithm of the smallest cost data access path. Our approach improves the performance of hybrid spatial DBMS than traditional disk-based DBMS by $20%{\sim}50%$.