• 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.

• 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 Korea Spatial Information System Society
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• v.11 no.4
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• pp.74-84
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• 2009

GIS based new postal marketing method is presented in this paper with spatiotemporal mining to cope with domestic mail volume decline and to strengthening competitiveness of postal business. Market segmentation technique for socialogy of population and spatiotemporal prediction of consumer propensity to purchase through spatiotemporal multi-dimensional analysis are suggested to provide meaningful and accurate marketing information with customers. Internal postal acceptance & external statistical data of local districts in the Seoul Metropolis are used for the evaluation of geo-lifestyle clustering and spatiotemporal cube mining. Successfully optimal 14 maketing clusters and spatiotemporal patterns are extracted for the prediction of consumer propensity to purchase.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.307-316
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• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.

• ETRI Journal
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• v.30 no.3
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• pp.421-431
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• 2008

In this paper, we offer a new technique to discover frequent spatiotemporal patterns from a moving object database. Though the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and timing constraints on moving sequences makes the computation feasible. The proposed technique includes two algorithms, AllMOP and MaxMOP, to find all frequent patterns and maximal patterns, respectively. In addition, to support the service provider in sending information to a user in a push-driven manner, we propose a rule-based location prediction technique to predict the future location of the user. The idea is to employ the algorithm AllMOP to discover the frequent movement patterns in the user's historical movements, from which frequent movement rules are generated. These rules are then used to estimate the future location of the user. The performance is assessed with respect to precision and recall. The proposed techniques could be quite efficiently applied in a location-based service (LBS) system in which diverse types of data are integrated to support a variety of LBSs.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.179-186
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• 2008

The converge of location-aware devices, GIS functionalities and the increasing accuracy and availability of positioning technologies pave the way to a range of new types of location-based services. The field of spatiotemporal data mining where relationships are defined by spatial and temporal aspect of data is encountering big challenges since the increased search space of knowledge. Therefore, we aim to propose algorithms for mining spatiotemporal patterns in mobile environment in this paper. Moving patterns are generated utilizing two algorithms called All_MOP and Max_MOP. The first one mines all frequent patterns and the other discovers only maximal frequent patterns. Our proposed approach is able to reduce consuming time through comparison with DFS_MINE algorithm. In addition, our approach is applicable to location-based services such as tourist service, traffic service, and so on.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.569-577
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• 2014

The various services are required to user in time and space. It is important to provide suitable service to user according to user's circumstance. Therefore it is need to provide services to user through mining by latest information of user activity and service history. In this paper we propose a mining method to search association rule using service history based on spatiotemporal information and service ontology. In this method, we find the associative service pattern using level-cross tree on service ontology. The proposed method is to be a basic research to find the service pattern to provide high quality service to user according to season, location and age under the same context.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.119-121
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• 2008

Pattern matching is increasingly being employed in various researches as health care service, RFID-based system, facility management, and surveillance. Geosensor filter correlates a data stream to match specific patterns in distribution environments. In this paper, we present a geosensor query language to represent efficiently declarative geosensor query. Geosensor operators are proposed to use for fast query processing in terms of spatial and temporal area in distribution environments. We also propose a geosensor filter to match new query predicates into incoming stream predicates. Our filter can reduce the volume of transmission data and save power consumption of sensors. It can be utilized the stream data mining system to process in real-time various data as location, time, and geosensor information in distribution environments.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.3-5
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• 2007

Many continuous queries are important to be process efficiently in a data stream environment. It is applied a query index technique that takes linear performance irrespective of the number and width of intervals for processing many continuous queries. Previous researches are not able to support the dynamic insertion and deletion to arrange intervals for constructing an index previously. It shows that the insertion and search performance is slowed by the number and width of interval inserted. Many intervals have to be inserted and searched linearly in a data stream environment. Therefore, we propose Hashed Multiple Lists in order to process continuous queries linearly. Proposed technique shows fast linear search performance. It can be utilized the systems applying a sensor network, and preprocessing technique of spatiotemporal data mining.

• Spatial Information Research
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• v.20 no.2
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• pp.129-136
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• 2012

Recently international organizations such as ISO/TC211, OGC, INSPIRE (Infrastructure for Spatial Information in Europe) make an effort to share geospatial data using semantic web technologies. In addition, smart phone and social networking services enable community-based opportunities for participants to share issues of a social phenomenon based on geographic area, and many researchers try to find a method of extracting issues from that. However, serviceable spatial ontologies are still insufficient at application level, and studies of spatial information extraction from SNS were focused on user's location finding or geocoding by text mining. Therefore, a study of extracting spatial phenomenon from social media information and converting it into geosemantic knowledge is very usable. In this paper, we propose a framework for extracting keywords from micro-blog, one of the social media services, finding their relationships using data mining technique, and converting it into spatiotemopral knowledge. The result of this study could be used for implementing a related system as a procedure and ontology model for constructing geoseem antic issue. And from this, it is expected to improve the effectiveness of finding, publishing and analysing spatial issues.