• Journal of KIISE:Databases
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• v.36 no.2
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• pp.99-111
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• 2009

In wireless sensor networks, various schemes have been proposed to store and process sensed data efficiently. A Data-Centric Storage(DCS) scheme assigns distributed data regions to sensors and stores sensed data to the sensor which is responsible for the data region overlapping the data. The DCS schemes have been proposed to reduce the communication cost for transmitting data and process exact queries and range queries efficiently. Recently, KDDCS that readjusts the distributed data regions dynamically to sensors based on K-D tree was proposed to overcome the storage hot-spots. However, the existing DCS schemes including KDDCS suffer from Query Hot-Spots that are formed if the query regions are not uniformly distributed. As a result, it causes reducing the life time of the sensor network. In this paper, we propose a new DCS scheme, called TPDCS(Time-Parameterized DCS), 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 life time of sensor networks. It is shown through various experiments that our scheme outperform the existing schemes.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.175-187
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• 2006

The data cube is an aggregation operator that computes group-bys for all possible combination of dimension attributes. %on the number of the dimension attributes is n, a data cube computes $2^n$ group-bys. Each group-by in a data cube is called a cuboid. Data cubes are often precomputed and stored as materialized views in data warehouses. These data cubes need to be updated when source relation change. The incremental maintenance of a data cube is to compute and propagate only its changes. To compute the change of a data cube of $2^n$ cuboids, previous works compute a delta cube that has the same number of cuboids as the original data cube. Thus, as the number of dimension attributes increases, the cost of computing a delta cube increases significantly. Each cuboid in a delta cube is called a delta cuboid. In this paper. we propose an incremental cube maintenance method that can maintain a data cube by using only $_nC_{{\lceil}n/2{\rceil}}$ delta cuboids. As a result, the cost of computing a delta cube is substantially reduced. Through various experiments, we show the performance advantages of our method over previous methods.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.365-380
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• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

• Journal of Korea Multimedia Society
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• v.3 no.3
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• pp.209-223
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• 2000

As the importance and utilization of multimedia data increases, it becomes necessary to represent and manage multimedia data within database systems. In this paper, we designed and implemented an image retrieval system which support efficient management and intelligent retrieval of image data using concept hierarchy and data mining techniques. We stored the image information intelligently in databases using concept hierarchy. To support intelligent retrievals and efficient web services, our system automatically extracts and stores the user information, the user's query information, and the feature data of images. The proposed system integrates user metadata and image metadata to support various retrieval methods on image data.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.369-377
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• 2007

Histogram is one of tools that efficiently summarize data, and it is widely used for selectivity estimation and approximate query answering. Existing histogram construction algorithms are applicable to point data represented by a set of values. As often as point data, we can meet interval data such as daily temperature and daily stock prices. In this paper, we thus propose the histogram construction algorithms for interval data by extending several methods used in existing histogram construction algorithms. Our experiment results, using synthetic data, show our algorithms outperform naive extension of existing algorithms.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1041-1048
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• 2004

Database logs contain various information on database operations, but they are used to recover database systems from failures generally. This paper proposes a log analysis tool that provides useful information for database tuning. This tool provides users with information on work-load organization, database schemas, and resources usages of queries. This paper describes the tool in views of its architecture, functions, implementation, and verification. The tool is verified by running the TPC-W benchmark, and representative analysis results are also presented.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1259-1265
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• 2004

In many applications, producing labeled data is costly and time consuming while an enormous amount of unlabeled data is available with little cost. Therefore, it is natural to ask whether we can take advantage of these unlabeled data in classification teaming. In machine learning literature, the co-training method has been widely used for this purpose. However, the current co-training method requires the entire features to be split into two independent sets. Therefore, in this paper, we improved the current co-training method in a number of ways, and proposed a new co-training method which do not need the feature split. Experimental results show that our proposed method can significantly improve the performance of the current co-training algorithm.

• Journal of KIISE:Information Networking
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• v.33 no.4
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• pp.324-332
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• 2006

A caching is a useful technique to alleviate performance degradation due to the inherent narrow bandwidth by reducing contention of broadcast requests. In this paper, we propose a caching strategy for pull-based data broadcast system which considers data popularity among clients. In addition, the proposed strategy also reflects recentness of data access based on data broadcast version. Then, we evaluate the performance of proposed strategy through a simulation approach. According to the results, the strategy considering both hit ratio and miss cost shows better performance than the traditional LRU. In addition, the strategy considering data popularity among clients shows better performance in some cases.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.521-531
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• 2003

Multi-level geographic data can be mainpulated by a window query such as a zoom operation. In order to handle multi-level geographic data efficiently, a spatial indexing method supporting a window query is needed. However, the conventional spatial indexing methods are not efficient to access multi-level geographic data quickly. To solve it, other a few spatial indexing methods for multi-level geographic data are known. However these methods do not support all types of multi-level geographic data. This paper presents a new efficient spatial indexing method, the MLR-tree for window query of multi-level geographic data. The MLR-tree offers both high search performance and no data redundancy. Experiments show them. Moreover, the MLR-tree supports all types of multi-level geographic data.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.1921-1929
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• 1997

This paper describes a direct transmission method of multimedia data stream between a multimedia data processor and a communication interface without using system memory. I propose the direct transfer method of multimedia data through the single data path, without additional data path between a multimedia data processor and a communication interface in multimedia platforms. The hardware architecture and functions for the direct transfer method is defined. Procedure to transfer multimedia data to and from the multimedia data processor is described by means of control flow chart. Comparing the proposed method with general methods, I show that the proposed method can decrease number of bus accesses and bus cycles.