• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.527-547
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• 2009

Moving object management is widely used in traffic, logistic and data mining applications in ubiquitous environments. It is required to analyze spatio-temporal data and trajectories for moving object management. In this paper, we proposed a novel index structure for spatio-temporal aggregation of trajectory in a constrained network, named aCN-RB-tree. It manages aggregation values of trajectories using a constraint network-based index and it also supports direction of trajectory. An aCN-RB-tree consists of an aR-tree in its center and an extended B-tree. In this structure, an aR-tree is similar to a Min/Max R-tree, which stores the child nodes' max aggregation value in the parent node. Also, the proposed index structure is based on a constrained network structure such as a FNR-tree, so that it can decrease the dead space of index nodes. Each leaf node of an aR-tree has an extended B-tree which can store timestamp-based aggregation values. As it considers the direction of trajectory, the extended B-tree has a structure with direction. So this kind of aCN-RB-tree index can support efficient search for trajectory and traffic zone. The aCN-RB-tree can find a moving object trajectory in a given time interval efficiently. It can support traffic management systems and mining systems in ubiquitous environments.

• 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.

• 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 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.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.77-94
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• 2004

Recently, the needs of index structure which manages moving objects efficiently have been increased because of the rapid development of location-based techniques. Existing index structures frequently need updates because moving objects change continuatively their positions. That caused entire performance loss of the index structures. In this paper, we propose a new index structure called the TPKDB-tree that is a spatio-temporal index structure based on KDB-tree. Our technique optimizes update costs and reduces a search time for moving objects and reduces unnecessary updates by expressing moving objects as linear functions. Thus, the TPKDB-tree efficiently supports the searches of future positions of moving objects by considering the changes of moving objects included in the node as time-parameter. To maximize space utilization, we propose the new update and split methods. Finally, we perform various experiments to show that our approach outperforms others.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.463-475
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• 2006

In applications such as a traffic management system, analysis using a spatial hierarchy of a spatial data warehouse and a simple aggregation is required. Over the past few years, several studies have been made on solution using a spatial index. Many studies have focused on using extended R-tree. But, because it just provides either the current aggregation or the total aggregation, decision support of traffic policy required historical analysis can not be provided. This paper proposes hybrid index based on extended aR-tree for the spatio-temporal aggregation. The proposed method supports a spatial hierarchy and the current aggregation by the R-tree. The sorted hash table using the time structure of the extended aR-tree provides a temporal hierarchy and a historical aggregation. Therefore, the proposed method supports an efficient decision support with spatio-temporal analysis and is Possible currently traffic analysis and determination of a traffic policy with historical analysis.

• 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.

• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.85-98
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• 2010

The prevailing infrastructure of ubiquitous computing paradigm on the one hand making significant development for integrating technology in the daily life but on the other hand raising concerns for privacy and confidentiality. This research presents a new privacy-preserving spatio-temporal query processing technique, in which location based services (LBS) can be serviced without revealing specific locations of private users. Existing location cloaking techniques are based on a grid-based structures such as a Quad-tree and a multi-layered grid. Grid-based approaches can suffer a deterioration of the quality in query results since they are based on pre-defined size of grids which cannot be adapted for variations of data distributions. Instead of using a grid, we propose a location-cloaking algorithm which uses the R-tree, a widely adopted spatio-temporal index structure. The proposed algorithm uses the MBRs of leaf nodes as the cloaked locations of users, since each leaf node guarantees having not less than a certain number of objects. Experimental results show the superiority of the proposed method.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.451-463
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• 2003

Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on huge amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of a single R-tree variant for spatial dimension and numerous B-trees for temporal dimension. The multi~tree based frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management cost and low query efficiency. To overcome the limitations of such multi-tree based frameworks, we propose a new approach called Hilbert Cube(H-Cube), which employs fractals in order to impose a total-order on cells. In addition, the H-Cube takes advantage of the traditional Prefix-sum approach to improve Query efficiency significantly. The H-Cube partitions an embedding space into a set of cells which are clustered on disk by Hilbert ordering, and then composes a cube by arranging the grid cells in a chronological order. The H-Cube refines cells adaptively to handle regional data skew, which may change its locations over time. The H-Cube is an adaptive, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our approach focuses on indexing dynamic point objects in static spatial dimensions. Through the extensive performance studies, we observed that The H-Cube consumed at most 20% of the space required by multi-tree based frameworks, and achieved higher query performance compared with multi-tree structures.