• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.178-192
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• 2021

File system forensics typically focus on the contents or timestamps of a file, and it is common to work around file/directory centers. But to recover a deleted file on the disk or use a carving technique to find and connect partial missing content, the evidence must be analyzed using cluster-centered analysis. Forensics tools such as EnCase, TSK, and X-ways, provide a basic ability to get information about disk clusters, but these are not the core functions of the tools. Alternatively, Sysinternals' DiskView tool provides a more intuitive visualization function, which makes it easier to obtain information around disk clusters. In addition, most current tools are for Windows. There are very few forensic analysis tools for MacOS, and furthermore, cluster analysis tools are very rare. In this paper, we developed a tool named FACT (Forensic Analyzer based Cluster Information Tool) for analyzing the state of clusters in a HFS+ file system, for digital forensics. The FACT consists of three features, a Cluster based analysis, B-tree based analysis, and Directory based analysis. The Cluster based analysis is the main feature, and was basically developed for cluster analysis. The FACT tool's cluster visualization feature plays a central role. The FACT tool was programmed in two programming languages, C/C++ and Python. The core part for analyzing the HFS+ filesystem was programmed in C/C++ and the visualization part is implemented using the Python Tkinter library. The features in this study will evolve into key forensics tools for use in MacOS, and by providing additional GUI capabilities can be very important for cluster-centric forensics analysis.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.755-766
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• 2008

Recently, NAND flash memory is becoming into the spotlight as a next-generation storage device because of its small size, fast speed, low power consumption, and etc. compared to the hard disk. However, due to the distinct characteristics such as erase-before-write architecture, asymmetric operation speed and unit, disk-based systems and applications may result in severe performance degradation when directly implementing them on NAND flash memory. Especially when a B-tree is implemented on NAND flash memory, intensive overwrite operations may be caused by record inserting, deleting, and reorganizing. These may result in severe performance degradation. Although ${\mu}$-tree has been proposed in order to overcome this problem, it suffers from frequent node split and rapid increment of its height. In this paper, we propose Log-Structured B-Tree(LSB-Tree) where the corresponding log node to a leaf node is allocated for update operation and then the modified data in the log node is stored at only one write operation. LSB-tree reduces additional write operations by deferring the change of parent nodes. Also, it reduces the write operation by switching a log node to a new leaf node when inserting the data sequentially by the key order. Finally, we show that LSB-tree yields a better performance on NAND flash memory by comparing it to ${\mu}$-tree through various experiments.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.669-680
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• 2011

R-trees are widely used in various areas such as geographical information systems, CAD systems and spatial databases in order to efficiently index multi-dimensional data. As data sets used in these areas grow in size and complexity, however, range query operations on R-tree are needed to be further faster to meet the area-specific constraints. To address this problem, there have been various research efforts to develop strategies for acceleration query processing on R-tree by using the buffer mechanism or parallelizing the query processing on R-tree through multiple disks and processors. As a part of the strategies, approaches which parallelize query processing on R-tree through Graphics Processor Units(GPUs) have been explored. The use of GPUs may guarantee improved performances resulting from faster calculations and reduced disk accesses but may cause additional overhead costs caused by high memory access latencies and low data exchange rate between GPUs and the CPU. In this paper, to address the overhead problems and to adapt GPUs efficiently, we propose a novel approach which uses a GPU as a buffer to parallelize query processing on R-tree. The use of buffer algorithm can give improved performance by reducing the number of disk access and maximizing coalesced memory access resulting in minimizing GPU memory access latencies. Through the extensive performance studies, we observed that the proposed approach achieved up to 5 times higher query performance than the original CPU-based R-trees.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1003-1010
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• 2004

In this paper, we address efficient processing of subsequence matching in time-series databases. We first point out the performance problems occurring in the index searching of a prior method for subsequence matching. Then, we propose a new method that resolves these problems. Our method starts with viewing the index searching of subsequence matching from a new angle, thereby regarding it as a kind of a spatial-join called a window-join. For speeding up the window-join, our method builds an R＊-tree in main memory for f query sequence at starting of sub-sequence matching. Our method also includes a novel algorithm for joining effectively one R＊-tree in disk, which is for data sequences, and another R＊-tree in main memory, which is for a query sequence. This algorithm accesses each R＊-tree page built on data sequences exactly cure without incurring any index-level false alarms. Therefore, in terms of the number of disk accesses, the proposed algorithm proves to be optimal. Also, performance evaluation through extensive experiments shows the superiority of our method quantitatively.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.593-604
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• 2001

A database sharing system (DSS) refers to a system for high performance transaction processing. In the DSS, the processing nodes are coupled via a high speed network and share a common database at the disk level. Each node has a local memory and a separate copy of operating system. To reduce the number of disk accesses, the node caches data pages and index pages in its memory buffer. In general, B-tree index pages are accessed more often and thus cached at more processing nodes, than their corresponding data pages. There are also complicated operations in the B-tree such as Fetch, Fetch Next, Insertion and Deletion. Therefore, an efficient cache consistency scheme supporting high level concurrency is required. In this paper, we propose cache consistency schemes using identifiers of index pages and page_LSN of leaf page. The propose schemes can improve the system throughput by reducing the required message traffic between nodes and index re-traversal.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.13-24
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• 2005

Objects, which change their positions over time such as cars, are called moving objects. Trajectories of a moving object have large volumes because trajectories are accumulated. Efficient indexing techniques for searching these large volumes of trajectories are needed in the moving object databases. Especially the TB-tree which supports bundling trajectories is suitable for processing combined queries which have 2 steps: first step is selecting trajectories (range search), next is selecting the parts of each trajectory (trajectory search). But the TB-tree has unnecessary disk accesses cause of lack of spatial discrimination in range queries. In this paper, we propose and implement the splitting polity which can reduce dead spaces of non-leaf node in order to process range queries efficiently. The policy has better performance about range queries than the TB-tree as well as the advantages of the TB-tree, such as highly space utilization and efficient trajectory extraction. This paper shows that the newly proposed split policy has better performance in processing the range queries than that of the TB-tree by experimental evaluation.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.11-22
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• 2004

As random access memory chip gets cheaper, it becomes affordable to realize main memory-based database systems. Consequently, reducing cache misses emerges as the most important issue in current main memory databases, in which CPU speeds have been increasing at 60% per year, compared to the memory speeds at 10% per you. In this paper, we design and implement a main-memory database system for real-time mobile GIS. Our system is composed of 5 modules： the interface manager provides the interface for PDA users； the memory data manager controls spatial and non-spatial data in main-memory using virtual memory techniques； the query manager processes spatial and non-spatial query : the index manager manages the MR-tree index for spatial data and the T-tree index for non-spatial index : the GIS server interface provides the interface with disk-based GIS. The MR-tree proposed propagates node splits upward only if one of the internal nodes on the insertion path has empty space. Thus, the internal nodes of the MR-tree are almost 100% full. Our experimental study shows that the two-dimensional MR-tree performs search up to 2.4 times faster than the ordinary R-tree. To use virtual memory techniques, the memory data manager uses page tables for spatial data, non- spatial data, T-tree and MR-tree. And, it uses indirect addressing techniques for fast reloading from disk.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.597-610
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• 2011

In database application developments, data coexists in memory and disk spaces. To manipulate the memory data, the general programing languages are used and to manipulate the disk data, SQL is used. In particular, the procedural languages for the memory manipulation are difficult to create and manage than declarative languages such as SQL. Thus, this paper shows that a particular structure of memory data, tree structured, can be manipulated by SQL. Most of all, the model data of the user interfaces can be represented by a tree structure and thus, it can be processed by SQL except non set computations. The non set computations could be done by helper classes. The SQL memory data manipulation is more suited to the database application developments which have few complex computations.

• Journal of the Korean Wood Science and Technology
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• v.22 no.2
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• pp.61-70
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• 1994

This study was executed to find the applicability of press drying of tree disk by investigating the shrinkage and drying defect and to form appropriate model by comparing the actual moisture content(MC) and internal temperature in respect of drying time with calculated values based computer simulation to which was applied finite difference method. In press drying disk, heating period, constant drying rate period maintained plateau temperature at 100$^{\circ}C$ and falling drying rate period were significantly distinguished. Actual MC and internal temperature were analogous to those calculated at comparing points. Heat transfer model formed by Fourier's law using specific heat of moist wood and conduction coefficient considering fractional volume of each element of wood cell wall, bound water, free water and air showed applicability as basic data to developing heat expansion, shrinkage and drying stress during press drying. Also mass transfer model formed by Fick's diffusion law using water vapor diffusion coefficient showed applicability. Longitudinal shrinkage was developed by pressure of hot press and tangential shrinkage was restrained by hygrothermal recovery. The heart check, surface check and ring failure were occurred differently in species, but V-shaped crack didn't develop.