• Journal of the Korea Society of Computer and Information
• /
• v.19 no.5
• /
• pp.71-78
• /
• 2014

The RFID-tag objects can be recognized by a distinct reader where it is installed. The RFID-tag objects are likely described as storages rather than the mobiles in the use of GPS. As RFID tags are large in number compared to moving objects, so the storing and retrieval costs are highly expensive. Here, two solutions for spatio-temporal model taking account of the feature in the tagged objects are proposed. First, the moving-tag objects are expressed by the terms "now" as well as "path location". Second, the size of storing index was noticeably reduced by not saving the tag information of palletizing products but mapping the tagged objects.

• Journal of Information Technology Applications and Management
• /
• v.12 no.1
• /
• pp.111-123
• /
• 2005

Many indexing methods were proposed so that process moving object efficiently. Among them, indexing methods like the 3D R-tree treat temporal and spatial domain as the same. Actually, however. both domain had better process separately because of difference in character and unit. Especially in this paper we deal with limited region such as indoor environment since spatial domain is limited but temporal domain is grown. In this paper we present a novel indexing structure, namely STS-tree(Separation of Time and Space tree). based on limited region. STS-tree is a hybrid tree structure which consists of R-tree and one-dimensional TB-tree. The R-tree component indexes static object and spatial information such as topography of the space. The TB-tree component indexes moving object and temporal information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.05a
• /
• pp.460-464
• /
• 2003

The objects which continuously change their locations in the real world are called moving objects. The works on the existing spatial indices have been proposed the retrieval methods. However, keeping track of the terminal location of moving objects is more important than the efficiency of the query processing in the moving object database. Therefore, many pure spatial indices are not applicable to the moving objects database which should maintain the object's current location as precise as possible. This parer proposes a method for reducing the construction time of indexing moving objects. We analyze the characteristics of the method to re-index all the objects after each time period and the method to update immediately the locations on reporting their locations. We also newly propose a selective immediate update method using the properties of moving objects in order to minimize the number of database updates.

• Journal of KIISE:Databases
• /
• v.30 no.3
• /
• pp.320-335
• /
• 2003

Location-Based Services(LBS) give rise to location-based queries of which results depend on the locations of moving objects. One of important applications of LBS is to examine tracks of continuously moving objects. Moving objects databases need to provide 3-dimensional indexing for efficiently processing range queries on the movement of continuously changing positions. An extension of the 2-dimensional R-tree to include time dimension shows low space utilization and poor search performance, because of high overlap of index nodes and their dead space. To solve these problems, we propose a new R-tree based indexing technique, namely TR-tree. To increase storage utilization, we assign more entries to the past node by using the unbalanced splitting policy. If two nodes are highly overlapped, these nodes are forcibly merged. It is the forced merging policy that reduces the dead space and the overlap of nodes. Since big line segments can also affect the overlap of index nodes to be increased, big line segments should be clipped by the clipping policy when splitting overfull nodes. The TR-tree outperforms the 3DR-tree and TB-tree in all experiments. Particularly, the storage utilization of the TR-tree is higher than the R-tree and R*-tree.

• The KIPS Transactions:PartB
• /
• v.12B no.1 s.97
• /
• pp.47-56
• /
• 2005

A moving object has a various features that its spatial location, shape, and size are changed as time goes. In addition, the moving object has both temporal feature and spatial feature. It is one of the highly interested feature information in video data. In this paper, we propose an efficient content-based multimedia information retrieval system, so tailed ECoMOT which enables user to retrieve video data by using a trajectory information of moving objects in video data. The ECoMOT includes several novel techniques to achieve content-based retrieval using moving objects' trajectories : (1) Muitiple trajectory modeling technique to model the multiple trajectories composed of several moving objects; (2) Multiple similar trajectory retrieval technique to retrieve more similar trajectories by measuring similarity between a given two trajectories composed of several moving objects; (3) Superimposed signature-based trajectory indexing technique to effectively search corresponding trajectories from a large trajectory databases; (4) convenient trajectory extraction, query generation, and retrieval interface based on graphic user interface

• Journal of KIISE:Databases
• /
• v.33 no.3
• /
• pp.283-298
• /
• 2006

The TPR-tree is the time-parameterized indexing scheme that supports the querying of the current and projected future positions of such moving objects by representing the locations of the objects with their coordinates and velocity vectors. If this index is, however, used in environments that directions and velocities of moving objects, such as vehicles, are very often changed, it increases the communication cost between the server and moving objects because moving objects report their position to the server frequently when the direction and the velocity exceed a threshold value. To preserve the communication cost regularly, there can be used a manner that moving objects report their position to the server periodically. However, the periodical position report also has a problem that lineal time functions of the TPR-tree do not guarantee the accuracy of the object's positions if moving objects change their direction and velocity between position reports. To solve this problem, we propose the query processing scheme and the data structure using road networks for predicting uncertainty positions of moving objects, which is reported to the server periodically. To reduce an uncertainty of the query region, the proposed scheme restricts moving directions of the object to directions of road network's segments. To remove an uncertainty of changing the velocity of objects, it puts a maximum speed of road network segments. Experimental results show that the proposed scheme improves the accuracy for predicting positions of moving objects than other schemes based on the TPR-tree.

• The Journal of the Korea Contents Association
• /
• v.4 no.4
• /
• pp.141-152
• /
• 2004

In this paper, we propose the index structure supporting the future position retrievals with efficiently updating continuous positions of moving objects in location based services. For reducing update costs of moving objects, our index structure directly accesses to the leaf node with moving objects using secondary index structure and performs bottom up update when node information is changed. Positions of moving objects are stored in primary index structure. In primary index structure, the split information similar to kd-tree is stored to internal node for increasing node's fanout. And the proposed index structure supports the future position retrievals using velocity of moving objects in the child node.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.6
• /
• pp.1276-1282
• /
• 2003

The works on the existing spatial indices have been proposed the retrieval methods. However, keeping track of the terminal location of moving objects is more important than the efficiency of the query processing in the moving object database. Therefore, many pure spatial indices are not applicable to the moving objects database which should maintain the object's current location as precise as possible. This paper proposes a method for reducing the construction time of indexing moving objects. We analyze the characteristics of the method to re-index all the objects after each time period and the method to update immediately the locations on reporting their locations. We also newly propose a selective immediate update method using the properties of moving objects in order to minimize the number of database updates.

• The Journal of the Korea Contents Association
• /
• v.7 no.7
• /
• pp.1-10
• /
• 2007

In this paper, we propose an indexing method for efficiently updating current positions of moving objects on road networks. The existing road network models increase update costs when objects move to adjacent road segments because their connectivity is not preserved. We propose an intersection based network model and a new index structure to solve this problem. The proposed intersection based network model preserves network connectivity through splitting road networks to contain intersection nodes always. The proposed index structure In our experiments, we show that our method is about 3 times faster than an existing index structure in terms of update costs.

• The KIPS Transactions:PartD
• /
• v.13D no.7 s.110
• /
• pp.879-888
• /
• 2006

Recently many researchers have focused on management of Historical trajectories of moving objects in Euclidean spaces due to numerous sizes of accumulated data over time. However, the movement of moving objects in real applications generally has some constraints, for example vehicles on roads can only travel along connected road networks. In this paper, we propose an indexing method for trajectories of moving objects on road networks in order to process the network-based spatiotemporal range query. Our method contains the connect information of road networks to use the network distance for query processing, deals with trajectories which are represented by road segments in road networks, and manages them using multiple R-trees assigned per each road segment. Furthermore, it has a structure to be able to share R-tree among several road segments in large road networks. Consequently, we show that our method takes about 30% less in node accesses for the network-based spatiotemporal range query processing than other methods based on the Euclidean distance by experiments.