• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.81-90
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• 2017

This study aimed to construct an integrated database using the same spatio-temporal unit by employing various public-data types with different real-time information provision cycles and spatial units. Towards this end, three temporal interpolation methods (piecewise constant interpolation, linear interpolation, nonlinear interpolation) and a spatial matching method by district boundaries was proposed. The case study revealed that the linear interpolation is an excellent method, and the spatial matching method also showed good results. It is hoped that various prediction models and data analysis methods will be developed in the future using different types of data in the analysis database.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.384-387
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• 2003

Most of the real world phenomena change over time. The ability to represent and to reason geographic data becomes crucial. A large amount of non-standard applications are dealing with data characterized by spatial, temporal and/or uncertainty features. Non-standard data like spatial and temporal data have an inner complex structure requiring sophisticated data representation, and their operations necessitate sophisticated and efficient algorithms. Current GIS technology is inefficient to model and to handle complex geographic phenomena, which involve space, time and uncertainty dimensions. This paper concentrates on developing a fuzzy spatio-temporal data model based on fuzzy set theory and relational data models. Fuzzy spatio-temporal operators are also provided to support dynamic query.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.105-111
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• 2015

Domestic 2D maps includes only most up-to-date information at the time of production without historical information. Therefore, it is hard to identify the change history of real world objects. In this research, Spatio-temporal model for 2D map were developed and it's compatibility was verified through the pilot project conducted on the Gwanggyo area of Gyeonggi province. Also, the procedure to generate 2D spatio-temporal database using maps made periodically on the same target area was introduced for showing the possibility of realizing nation wide spatio-temporal 2D map using the national base map updated periodically.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.39-52
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• 2006

With the recent the use of spatio-temporal data mining which can extract various knowledge such as movement patterns of moving objects in history data of moving object gets increasing. However, the existing movement pattern extraction methods create lots of candidate movement patterns when the minimum support is low. Therefore, in this paper, we suggest the STMPE(Spatio-Temporal Movement Pattern Extraction) algorithm in order to efficiently extract movement patterns of moving objects from the large capacity of spatio-temporal data. The STMPE algorithm generalizes spatio-temporal and minimizes the use of memory. Because it produces and keeps short-term movement patterns, the frequency of database scan can be minimized. The STMPE algorithm shows more excellent performance than other movement pattern extraction algorithms with time information when the minimum support decreases, the number of moving objects increases, and the number of time division increases.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.309-312
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• 2005

The need for LBS (Loc,ation Based Services) is increasing due to the wnespread of mobile computing devices and positioning technologies~ In LBS, there are many applications that need to manage moving objects (e.g. taxies, persons). The moving object join operation is to make pairs with spatio-temporal attribute for two sets in the moving object database system. It is import and complicated operation. And processing time increases by geometric progression with numbers of moving objects. Therefore efficient methods of spatio-temporal join is essential to moving object database system. In this paper, we apply spatial join methods to moving objects join. We propose two kind of join methods with TB- Tree that preserves trajectories of moving objects. One is depth first traversal spatio-temporaljoin and another is breadth-first traversal spatio-temporal join. We show results of performance test with sample data sets which are created by moving object ,generator tool.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.35-48
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• 2005

An architectural framework is developed for integrating geospatial and temporal data with relational information from which a spatio-temporal data warehouse (STDW) system is built. In order to implement the STDW, a generic conceptual model was designed that accommodated six dimensions: spatial (map object), temporal (time), agent (contractor), management (e.g. planting) and tree species (specific species) that addressed the 'where', 'when', 'who', 'what', 'why' and 'how' (5W1H) of the STDW information, respectively. A formal algebraic notation was developed based on a triplet schema that corresponded with spatial, temporal, and relational data type objects. Spatial object structures and spatial operators (spatial selection, spatial projection, and spatial join) were defined and incorporated along with other database operators having interfaces via the generic model.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.39-47
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• 2004

Spatio-temporal databases have been mostly studied in the area of access methods. However, without considering an extraordinary update maintenance overhead after building up a spatio-temporal index, most indexing techniques have focused on fast query processing only. In this paper, we propose an efficient update management method that reduces the number of disk accesses required in order to apply the updates of moving objects to a spatio-temporal index. We consider realistic update patterns that can represent the movements of objects properly. We present a memory based structure that can efficiently maintain a small number of very frequently updating objects. For an experimental environment with realistic update patterns, the number of disk accesses of our method is about 40% lower than that of a general update method of existing spatio-temporal indexes.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.59-77
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• 2007

Advances in mobile techknowledges and supporting techniques require an effective representation and analysis of moving objects. Similarity search of moving object trajectories is an active research area in data mining. In this paper, we propose a trajectory search algorithm for spatio-temporal similarity of moving objects on road network. For this, we define spatio-temporal distance between two trajectories of moving objects on road networks, and propose a new method to measure spatio-temporal similarity based on the real road network distance. In addition, we propose a similar trajectory search algorithm that retrieves spatio-temporal similar trajectories in the road network. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently. Finally, we provide performance analysis to show the efficiency of the proposed algorithm.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.176-183
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• 2003

Various techniques including histograms, sampling and parametric techniques have been proposed to estimate query result sizes for the query optimization. Histogram-based techniques are the most widely used form for the selectivity estimation in relational database systems. However, in the spatio-temporal databases for the moving objects, the continual changes of the data distribution suffer the direct utilization of the state of the art histogram techniques. Specifically for the future queries, we need another methodology that considers the updated information and keeps the accuracy of the result. In this paper we propose a novel approach based upon the duality and the marginal distribution to construct a histogram with very little time since the spatio-temporal histogram requires the data distribution defined by query predicates. We use data synopsis method in the dual space to construct spatio-temporal histograms. Our method is robust to changing data distributions during a certain period of time while the objects keep the linear movements. An additional feature of our approach supports the dynamic update incrementally and maintains the accuracy of the estimated result.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.54-66
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• 2008

A spatio-temporal join is an expensive operation that is commonly used in spatio-temporal database systems. In order to generate an efficient query plan for the queries involving spatio-temporal join operations, it is crucial to estimate accurate selectivity for the join operations. Given two dataset $S_1,\;S_2$ of discrete data and a timestamp $t_q$, a spatio-temporal join retrieves all pairs of objects that are intersected each other at $t_q$. The selectivity of the join operation equals the number of retrieved pairs divided by the cardinality of the Cartesian product $S_1{\times}S_2$. In this paper, we propose aspatio-temporal histogram to estimate selectivity of spatio-temporal join by extending existing geometric histogram. By using a wide spectrum of both uniform dataset and skewed dataset, it is shown that our proposed method, called Spatio-Temporal Histogram, can accurately estimate the selectivity of spatio-temporal join. Our contributions can be summarized as follows: First, the selectivity estimation of spatio-temporal join for discrete data has been first attempted. Second, we propose an efficient maintenance method that reconstructs histograms using compression of spatial statistical information during the lifespan of discrete data.