• Spatial Information Research
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• v.22 no.5
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• pp.9-18
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• 2014

According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.69-82
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• 2004

A moving object has a special feature that it's spatial location, shape and size are changed as time goes. These changes of the object accompany the continuous movement that is called the trajectory. In this paper, we propose an index structure that users can retrieve the trajectory of a moving object with the access of a page. We also propose the multi-complex query that is a new query type for trajectory retrieval. In order to prove the excellence of our method, we compare and analyze the performance for query time and storage space through experiments in various environments. It is shown that our method outperforms the existing index structures when processing spatio-temporal trajectory queries on moving objects.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.43-54
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• 2009

As R&D(Research and Development) is going on actively to develop technologies for the ubiquitous computing environment, which Is the human-oriented future computing environment, GIS dealing with spatio-temporal data is emerging as a promising technology. This also increases the necessity of the middleware for providing services to give interoperability in various heterogeneous environments. The core technologies of the middleware are real-time processing technology of data streams coming unceasingly from positioning systems and data stream processing technology developed for non-spatio-temporal data. However, it has problems in processing queries on spatio-temporal data efficiently. Accordingly, this paper designed and implemented the spatio-temporal middleware that provides interoperability between a mobile spatio-temporal DBMS(DataBase Management System) and a server spatio-temporal MMDBMS(Main Memory DataBase Management System). The spatio-temporal middleware maintains interoperability among heterogeneous devices and guarantees data integrity in query processing through real-time processing of unceasing spatio-temporal data streams and two way synchronization of spatio-temporal DBMSs. In addition, it manages session for the connection of each spatio-temporal DBMS and manages resources for its stable operation. Finally, this paper proved the usability of the spatio-temporal middleware by applying it to a real-time position tracking system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5042-5048
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• 2010

In ubiquitous environments, many continuous query processing techniques make use of operator network and sharing methods on continuous data stream generated from various sensors. Since similar continuous queries with the location information intensively occur in specific regions, we suggest a new operator sharing method based on grid partition for the spatial continuous query processing for location-based applications. Due to the proposed method shares moving objects by the given grid cell without sharing spatial operators individually, our approach can not only share spatial operators including similar conditions, but also increase the query processing performance and the utilization of memory by reducing the frequency of use of spatial operators.

• 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 Digital Contents Society
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• v.15 no.4
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• pp.527-532
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• 2014

Recently, due to the proliferation of mobile smart devices, the inovation of bigdata, which analyzes and processes massive data collected from various sensors implaned in smart devices, expands to LBSs. Many location prediction techniques for moving objects have been studied in literature. However, as the majority of studies perform location prediction which depends on specific applications, they hardly reflect the technical requirements of next-generation spatio-temporal information services. Therefore, this paper proposes the design of general-purpose distributed moving object prediction query processing framework that is capable of performing primitive and various types of queries effectively based on massive spatio-temporal data of moving objects in real-world space networks.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.129-141
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• 2005

Although spatio-temporal databases have received considerable attention recently, there has been little work on processing range sum queries on the historical records of moving objects despite their importance. Since to answer range sum queries, the direct access to a huge amount of data incurs prohibitive computation cost, materialization techniques based on existing index structures are recently suggested. A simple but effective solution is to apply the materialization technique to the MVR-tree known as the most efficient structure for window queries with spatio-temporal conditions. However, the MVR-tree has a difficulty in maintaining pre-aggregated results inside its internal nodes due to cyclic paths between nodes. Aggregate structures based on other index structures such as the HR-tree and the 3DR-tree do not provide satisfactory query performance. In this paper, we propose a new indexing technique called the Adaptive Partitioned Aggregate R-Tree (APART) and query processing algorithms to efficiently process range sum queries in many situations. Experimental results show that the performance of the APART is typically above 2 times better than existing aggregate structures in a wide range of scenarios.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1529-1541
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• 2006

Moving objects have been widely employed in traffic and logistic applications. Spatio-temporal aggregations mainly describe the moving object's behavior in the spatial data warehouse. The previous works usually express the object moving in some certain region, but ignore the object often moving along as the trajectory. Other researches focus on aggregation and comparison of trajectories. They divide the spatial region into units which records how many times the trajectories passed in the unit time. It not only makes the storage space quite ineffective, but also can not maintain spatial data property. In this paper, a spatio-temporal aggregation index structure for moving object trajectory in constrained network is proposed. An extended B-tree node contains the information of timestamp and the aggregation values of trajectories with two directions. The network is divided into segments and then the spatial index structure is constructed. There are the leaf node and the non leaf node. The leaf node contains the aggregation values of moving object's trajectory and the pointer to the extended B-tree. And the non leaf node contains the MBR(Minimum Bounding Rectangle), MSAV(Max Segment Aggregation Value) and its segment ID. The proposed technique overcomes previous problems efficiently and makes it practicable finding moving object trajectory in the time interval. It improves the shortcoming of R-tree, and makes some improvement to the spatio-temporal data in query processing and storage.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.31-42
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• 2002

In this paper, we propose a new spatio-temporal representation scheme which can model moving objets trajectories effectively in video data and a new signature-based access method for moving objects trajectories which can support efficient retrieval on user query based on moving objects trajectories. The proposed spatio-temporal representation scheme supports content-based retrieval based on moving objects trajectories and concept-based retrieval based on concepts(semantics) which are acquired through the location information of moving objects trajectories. Also, compared with the sequential search, our signature-based access method can improve retrieval performance by reducing a large number of disk accesses because it access disk using only retrieved candidate signatures after it first scans all signatures and performs filtering before accessing the data file. Finally, we show the experimental results that proposed scheme is superior to the Li and Shan's scheme in terns of both retrieval effectiveness and efficiency.