• Journal of Information Processing Systems
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• v.2 no.2
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• pp.124-129
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• 2006

With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.635-652
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• 2011

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.137-147
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• 2006

Spatiotemporal data are records of the spatial changes of moving objects over time. Most data in corporate databases have a spatiotemporal nature, but they are typically treated as merely descriptive semantic data without considering their potential visual (or cartographic) representation. Businesses such as geographical CRM, location-based services, and technologies like GPS and RFID depend on the storage and analysis of spatiotemporal data. Effectively handling the data analysis process may be accomplished through spatiotemporal data warehouse and spatial OLAP. This paper proposes a multidimensional model for spatiotemporal data analysis, and cartographically represents the results of the analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.551-554
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• 2003

Aggregate operator which belongs to query operations are important in specialized systems such as geographic information system(GIS) and spatial database system. Most of data describing objects in the real world are characterized by space and time attributes. Till now, however, works on aggregate operations have only dealt with spatial or temporal aspect of object. The current demand of aggregate operations relates to spatiotemporal data which are contained both spatial and temporal data concurrently. Therefore, work on spatiotemporal operations is focused on database area. In this paper, we propose spatiotemporal aggregate functions that operate on spatiotemporal data. Above all, we support spatiotemporal aggregate functions on the basis of three dimensional spatiotemporal models that are defined with the linear one dimensional temporal domain. The proposed algorithms are evaluated through some implementation results. We are sure that the achievement of our work is useful and efficient.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.375-384
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• 2005

Most of data objects in the real world have spatial and temporal information simultaneously. One of the objectives of spatiotemporal databases is to provide a very expressive framework for spatiotemporal database applications. The object oriented spatiotemporal model can manage spatiotemporal data efficiently because the model is able to store the complete history of any objects. Therefore, in order to handle the spatiotemporal data in object oriented fashion, we proposed a low dimensional spatiotemporal model in the object oriented fashion that extended dimensionally two dimensional spatial model of OpenGIS to fit into the real world information and to include temporal information. In order to proof the proposed model, we design a data provider component which consists of the data source, the session, the command, and the rowset component, and implement it. The proposed four dimensional spatiotemporal data model and provider in the object oriented fashion can be used to store, retrieve and manage the object oriented spatiotemporal data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.721-723
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• 2003

Aggregation is an operation that returns a result value through a computational process on the data which satisfy a certain condition. Recently many applications use aggregation to analyze spatiotemporal data. Although spatiotemporal data change its states over time, previous aggregation works have only dealt with spatial or temporal aspect of object. In this paper we propose spatiotemporal aggregate functions that operate on spatiotemporal data. The proposed algorithms are evaluated through some implementation results. The experiment results show that the proposed aggregate functions are applicable to spatiotemporal data efficiently.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.61-66
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• 2005

Spatiotemporal data mining represents the confluence of several fields including spatiotemporal databases, machine loaming, statistics, geographic visualization, and information theory. Exploration of spatial data mining and temporal data mining has received much attention independently in knowledge discovery in databases and data mining research community. In this paper, we introduce an algorithm Max_MOP for discovering moving sequences in mobile environment. Max_MOP mines only maximal frequent moving patterns. We exploit the characteristic of the problem domain, which is the spatiotemporal proximity between activities, to partition the spatiotemporal space. The task of finding moving sequences is to consider all temporally ordered combination of associations, which requires an intensive computation. However, exploiting the spatiotemporal proximity characteristic makes this task more cornputationally feasible. Our proposed technique is applicable to location-based services such as traffic service, tourist service, and location-aware advertising service.

• Journal of KIISE:Databases
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• v.32 no.1
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• pp.43-55
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• 2005

Spatiotemporal databases support methods of recording and querying for spatiotemporal data to user by offering both spatial management and historical information on various types of objects in the real world. We can answer to the following query in real world: 'What is the average of volume of pesticide sprayed for cach farm land from April to August on 2001, within some query window' Such aggregation queries have both temporal and spatial constraint. However, previous works for aggregation are attached only to temporal aggregation or spatial aggregation. So they have problems that are difficult to apply for spatiotemporal data directly which have both spatial and temporal constraint. Therefore, in this paper, we propose spatiotemporal aggregate functions for analysis of spatiotemporal data which have spatiotemporal characteristic, such as stCOUNT, stSUM, stAVG, stMAX, stMIN. We also show that our proposal resulted in the convenience and improvement of query in application systems, and facility of analysis on spatiotemporal data which the previous temporal or spatial aggregate functions are not able to analyze, by applying to the estate management system. Then, we show the validity of our algorithm performance through the evaluation of spatiotemporal aggregate functions.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.205-218
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• 2004

As the development of database technology for managing spatiotemporal data, new types of spatiotemporal application services that need the spatiotemporal knowledge discovery from the large volume of spatiotemporal data are emerging. In this paper, a new 3-layered discovery framework for the development of spatiotemporal knowledge discovery techniques is proposed. The framework supports the foundation model in order not only to define spatiotemporal knowledge discovery problem but also to represent the definition of spatiotemporal knowledge and their relationships. Also the components of spatiotemporal knowledge discovery system and its implementation model are proposed. The discovery framework proposed in this paper satisfies the requirement of the development of new types of spatiotemporal knowledge discovery techniques. The proposed framework can support the representation model of each element and relationships between objects of the spatiotemporal data set, information and knowledge. Hence in designing of the new types of knowledge discovery such as spatiotemporal moving pattern, the proposed framework can not only formalize but also simplify the discovery problems.

• Journal of Biomedical Engineering Research
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• v.30 no.5
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• pp.373-380
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• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.