Yong-Hun Park

An efficient data-centric storage method using time parameter for sensor networks

In wireless sensor networks, various schemes have been proposed to efficiently store and process sensed data. Among them, the data-centric storage (DCS) scheme is one of the most well-known. The DCS scheme distributes data regions and stores the data in the sensor that is responsible for the region. The DCS based scheme was proposed to reduce the communication cost for transmitting data and to efficiently process exact queries and range queries. Recently, a KDDCS scheme was proposed to overcome storage hot-spots by dynamically readjusting the distributed data regions to sensors based on the K-D tree. However, the existing DCS based schemes including KDDCS suffer from query hot-spots that are formed when query regions are not uniformly distributed. As a result, it reduces the lifetime of the sensor network.In this paper, we propose a new DCS based scheme, called Time-Parameterized Data-Centric Storage (TPDCS), that avoids the problems of storage hot-spots and query hot-spots. To decentralize the skewed data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the lifetime of sensor networks. It is shown through various experiments that our scheme outperforms the existing schemes.

An efficient method for processing reverse skyline queries

Many studies on reverse skyline query processing have been done for company oriented services. The existing methods about reverse skyline are based on dynamic skyline. There are no reverse skyline query processing algorithms based on metric spaces for Location-Based Services (LBS). In this paper, we propose a reverse skyline query processing scheme that applies for a general skyline and considers metric spaces. The proposed method processes reverse skyline queries in the metric spaces using the existing spatial indexing scheme and considers both monochromatic and bichromatic environments. In order to show the superiority of the proposed scheme, we compare it with the basic skyline query processing scheme through performance evaluation. As a result, the proposed method achieves about 5000 times more performance than the existing method.

An efficient mobile peer to peer architecture in wireless ad hoc network

With the rapid development of wireless communication technologies and mobile devices, the mobile peer to peer (MP2P) network has been emerged. Since the computation ability, bandwidth, storage and the dynamic network topology of mobile devices are limited, directly applying the existing wired P2P method to wireless ad hoc network will result in poor performance. A hierarchical MP2P architecture based on super peers has been proposed to alleviate the problem. However, the management cost of the mobile network topology is still high. In this paper, we propose a hierarchical MP2P architecture using clustering mobile peers. The proposed architecture considers the maximum connection time of connected peers, the hop count with the super peer, and the number of connected peers of the super peer. The connection time between the connected peers can be determined by the location, velocity and communication range of each mobile peer. Therefore, the network topology is relatively stable, the management cost of the network topology is decreased and the success rate of contents search is increased. Experiments have shown that our proposed scheme outperforms the existing schemes.

An efficient path nearest neighbor query processing scheme for location based services

A path nearest neighbor (PNN) query finds the closest objects from paths consisting of line segments. The existing method has redundant searches as a problem. In this paper, we propose PNN methods that avoid redundant searches in an index. In order to avoid redundant searches, the proposed methods find the closest objects from a path sequentially and determine whether the next closest object is found. To prove the superiority of the proposed methods, we evaluate their performance.

A New Spatial Index Structure for Efficient Query Processing in Location Based Services

The interests of Location Based Services (LBS) have been highly increased according to the development of location aware techniques and mobile devices. Various spatial-indexing methods based on R-tree structure have been proposed to process queries efficiently. However, many node accesses occur in tree based spatial index structures when queries are processed. In this paper, we propose a spatial index structure, called ISGrid that provides better efficient query processing than R-tree based index structures. ISGrid uses not a tree structure but a grid structure that provides direct accesses to data pages and uses Minimum Boundary Rectangle (MBR) as a leaf node. The grid structure is used to access the leaf node. In addition, each leaf nodes maintain the links to neighbor leaf nodes like B+-tree to avoid unnecessary parent node accesses to search a neighbor node. We show the superiority of ISGrid from performance evaluation.

An efficient data-centric storage scheme considering storage and query hot-spots in sensor networks

In wireless sensor networks, various schemes have been proposed to efficiently store and process sensed data. Among them, the Data-Centric Storage (DCS) scheme is one of the most well-known. The DCS scheme distributes data regions and stores the data in the sensor that is responsible for the region. In this paper, we propose a new DCS based scheme, called Time-Parameterized Data-Centric Storage (TPDCS), that avoids the problems of storage hot-spots and query hotspots. To decentralize the skewed data and queries, the data regions are assigned by a time dimension as well as data dimensions in our proposed scheme. Therefore, TPDCS extends the lifetime of sensor networks. It is shown through various experiments that our scheme outperforms the existing schemes.

An Index Structure for Efficient k-NN Query Processing in Location Based Services

Many studies on index structures for k-NN query processing have been done in Location Based Services(LBS). However, they are not suitable for the moving objects because their update costs are so expensive due to the frequent location updates of the moving objects. In this paper, we propose Incremental Search R-tree(ISR-tree) that processes k-NN queries efficiently in the environment with static objects and moving clients. In ISR-tree, the leaf-nodes maintain the entries pointing to their relevant neighbor nodes and the entries help to find the neighbor leaf-nodes without visiting internal nodes when k-NN queries are processed. We prove the superiority of ISR-tree through the comparison with previous works. As a result, since ISR-tree saves the costs to visit internal nodes, it significantly improves the performance over the existing index structure.

Design and Implementation of a Non-Shared Metadata Server Cluster for Large Distributed File Systems

Most of large-scale distributed file systems decouple metadata operation from reading or writing operation for a file. In other words, certain servers named metadata server (MDS) is responsible for maintaining the metadata information of file systems. But, mostly the existing file systems are used are strictive metadata management technique, because those systems mostly designed to focus a distributed management of data and an input/output performance rather than the metadata. In this paper, we present a new non-shared metadata management technique in order to provide flexible metadata throughput and scalability. First, we introduce a new metadata distribution technique called dictionary partitions (DP). Then, we introduce load distribution technique based DP. In addition, we demonstrate the superiority of our technique with shared metadata management technique.

k-Nearest neighbor query processing method based on distance relation pattern

The k-nearest neighbor (k-NN) query is one of the most important query types for location based services (LBS). Various methods have been proposed to efficiently process the k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. In this paper, we propose a new efficient method, called Pattern Based k-NN (PB-kNN) to process the k-NN query. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, PB-kNN significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.

Design and Implementation of a Cloud Data Management Interface(CDMI) System

Recently, cloud data management has been actively studied along with the development of a cloud computing technology that can process large amounts of data at a lower cost. However, the existing cloud computing platforms do not guarantee interoperability according to the construction environments of users because they do not provide technical and political openness. In other words, in order for users to receive the related services, they use services provided by only one vendor. In this paper, we design and implement a storage interface that supports the international standard CDMI in order to retrieve, update, and delete data in could environments. These interfaces provide the functionality required for developers who want to build and use the cloud storage without special restrictions. In addition, we verify the operability and usability of CDMI international standard through the implementation of the proposed system.