• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.211-221
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• 2016

The human spatial mobility information is in high demand in various businesses; however, there are only few studies on human mobility because spatio-temporal data is insufficient and difficult to collect. Now with the spread of smartphones and the advent of social networking services, the spatio-temporal data began to occur on a large scale, and the data is available to the public. In this work, we compared the movement behavior of residents and tourists by using geo-tagged tweets which contain location information. We chose Seoul to be the target area for analysis. Various creative concepts and analytical methods are used: grid map concept, cells visited concept, reverse geocoding concept, average activity index, spatial mobility index, and determination of residents and visitors based on the number of days in residence. Conducting a series of analysis, we found significant differences of the movement behavior between local residents and tourists. We also discovered differences in visiting activity according to residential countries and used applications. We expect that findings of this research can provide useful information on tourist development and urban development.

• 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 the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2632-2637
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• 2010

Information in UbiComp Environment are transformed to knowledge by relationship in a spatio-temporal location, and then became intelligent contents with task procedures or application models. The entities in U-City has lots of relationships. It is important in U-City contents to provide intelligent and personalized response to meet the intention of users. We extend the spatial ontology model of SPIRIT to other domain. Domain ontologies are consist of type, relation, and instance ontologies. When the relationship model by shared concepts are not defined, we used the spatio-temporal events to find relationships. So we proposed the methods to recommend semantically related terms, not syntactically.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.549-557
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• 2012

Spatial phenomena such as environment pollution, disease and the risk of spreading information need a rapid initial response to perceive spread event. Moving data perceive spread event through real-time processing and analysis. To process and analysis the event, spatial-temporal complex event processing is used. Previous spatialtemporal complex event processing is possible basis spatial operator but insufficient apply to design spatialtemporal complex event processing to perceive spatial phenomena of high complexity. This study proposed hierarchical spatio-temporal CEP design which will efficiently manage the fast growing incoming sensor data. The implementation of the proposed design is evaluated with GPS location data of moving vehicles which are used as the incoming data stream for identifying spatial events. The spatial component of existing CEP software engine has been extended during the implementation phase to broaden the capabilities of processing spatio-temporal events.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.67-81
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• 2005

Recently, the interest and research on the development of new application services such as the Location Based Service and Telemetics providing the emergency service, neighbor information search, and route search according to the development of the Geographic Information System have been increasing. User's search in the spatio-temporal database which is used in the field of Location Based Service or Telemetics usually fixes the current time on the time axis and queries the spatial and aspatial attributes. Thus, if the range of query on the time axis is extensive, it is difficult to efficiently deal with the search operation. For solving this problem, the snapshot, a method to summarize the location data of moving objects, was introduced. However, if the range to store data is wide, more space for storing data is required. And, the snapshot is created even for unnecessary space that is not frequently used for search. Thus, non storage space and memory are generally used in the snapshot method. Therefore, in this paper, we suggests the Hash-based Spatio-Temporal Clustering Algorithm(H-STCA) that extends the two-dimensional spatial hash algorithm used for the spatial clustering in the past to the three-dimensional spatial hash algorithm for overcoming the disadvantages of the snapshot method. And, this paper also suggests the knowledge extraction algorithm to extract the knowledge for the path search of moving objects from the past location data based on the suggested H-STCA algorithm. Moreover, as the results of the performance evaluation, the snapshot clustering method using H-STCA, in the search time, storage structure construction time, optimal path search time, related to the huge amount of moving object data demonstrated the higher performance than the spatio-temporal index methods and the original snapshot method. Especially, for the snapshot clustering method using H-STCA, the more the number of moving objects was increased, the more the performance was improved, as compared to the existing spatio-temporal index methods and the original snapshot method.

• 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.29 no.5
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• pp.335-346
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• 2002

LBS(Location Based Services) provide the location-based information to its mobile users. The primary functionality of these services is to provide useful information to its users at a minimum cost of resources. The functionality can be implemented through data mining techniques. However, conventional data mining researches have not been considered spatial and temporal aspects of data simultaneously. Therefore, these techniques are inappropriate to apply on the objects of LBS, which change spatial attributes over time. In this paper, we propose a new data mining technique for identifying the temporal patterns from the series of the locations of moving objects that have both temporal and spatial dimension. We use a spatial operation of contains to generalize the location of moving point and apply time constraints between the locations of a moving object to make a valid moving sequence. Finally, the spatio-temporal technique proposed in this paper is very practical approach in not only providing more useful knowledge to LBS, but also improving the quality of the services.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.979-994
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• 2008

Moving objects are spatio-temporal data that change their location or shape continuously over time. Generally, if continuously moving objects are managed by a conventional database management system, the system cannot properly process the past and future location which is not stored in the database. Up to now, for the purpose of location tracking which is not stored, the linear interpolation to estimate the past location has been usually used. It is suitable for the moving objects on linear route, not curved route. In this paper, we propose a past location tracking algorithm for a moving object on curved routes, and also suggest a future location tracking algorithm using some past location information. We found that the proposed location tracking algorithm has higher accuracy than the linear interpolation function.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.79-93
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• 2009

For developing the location based service which is individualized and specialized according to the characteristic of the users, the spatio-temporal pattern mining for extracting the meaningful and useful patterns among the various patterns of the mobile object on the spatio-temporal area is needed. Thus, in this paper, as the practical application toward the development of the location based service in which it is able to apply to the real life through the pattern mining from the huge historical data of mobile object, we are proposed STOMP(using Frequency of sequence and Weight) that is the new mining method for extracting the patterns with spatial and temporal constraint based on the problems of mining the optimal moving pattern which are defined in STOMP(F)[25]. Proposed method is the pattern mining method compositively using weighted value(weights) (a distance, the time, a cost, and etc) for our previous research(STOMP(F)[25]) that it uses only the pattern frequent occurrence. As to, it is the method determining the moving pattern in which the pattern frequent occurrence is above special threshold and the weight is most a little bit required among moving patterns of the object as the optimal path. And also, it can search the optimal path more accurate and faster than existing methods($A^*$, Dijkstra algorithm) or with only using pattern frequent occurrence due to less accesses to nodes by using the heuristic moving history.