• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.909-912
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• 2008

대용량 문서에 대한 키워드 검색을 위해 역파일(inverted-file) 색인 기법이 널리 쓰이고 있다. 역파일 색인 기법을 구현함에 있어 고려되어야 할 점은 키워드 검색 처리 시에 디스크 사용을 최소로 할 수 있는 방법이다. 크기가 작은 역파일이라면 디스크 I/O 사용도 작고 필요시 역파일을 메모리에 적재하여 둠으로써 디스크 사용을 크게 줄일 수 있다. 하지만, 웹 검색이나 규모가 큰 도서관 시스템에서와 같이 색인 데이터 크기가 매우 큰 경우 역파일을 읽는 디스크 비용이 급격히 증가할 수 있다. 본 논문에서는 매우 큰 크기의 역파일을 사용하는 검색 환경에서 디스크 사용을 최소로 할 수 있는 역파일 구조를 제안한다. 제안된 구조는 질의 처리 과정을 고려해 계층 구조로 설계되며 실제 상용 시스템에 적용되어 안정성 및 성능을 입증했다.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.260-260
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• 2016

최근 고화질 및 대용량 영상의 등장으로 메모리 디바이스에 대한 연구가 활발하다. 메모리 디바이스의 oxide 층은 tunnel layer, trap layer와 blocking layer로 나누어지며, tunnel layer와 trap layer 사이 계면의 상태는 메모리 특성에 큰 영향을 준다. 한편, AlOx는 메모리 디바이스의 tunnel layer에 주로 적용되는 물질로서, AlOx를 형성하는 방법에는 진공공정을 이용하여 증착하는 방법과 알루미늄을 산화시켜 형성하는 방법이 있다. 그 중, 진공공정 방법인 RF 스퍼터를 이용하는 방법은 증착시 sputtering으로 인하여 표면에 손상을 주게 되어, 산화시켜 형성한 AlOx에 비해 막질이 좋지 않다는 단점이 있다. 따라서 본 연구에서는 우수한 막질의 메모리 디바이스를 제작하기 위하여 산화시켜 형성한 AlOx를 tunnel layer로 적용시킨 MOSCAP을 제작하여 메모리 특성을 평가하였다. 제작된 소자는 n-Si (1-20 ohm-cm) 기판을 사용하였다. Tunnel layer는 e-beam evaporator를 이용하여 Al을 5 nm 두께로 증착하고 퍼니스를 이용하여 O2 분위기에서 $300^{\circ}C$의 온도로 1시간 동안 산화시켜 AlOx을 형성하였으며, 비교군으로 RF 스퍼터를 이용하여 AlOx를 10 nm 두께로 증착한 소자를 같이 제작하였다. 순차적으로, trap layer와 blocking layer는 RF 스퍼터를 이용하여 각각 HfOx 30 nm와 SiOx 30 nm를 증착하였다. 마지막으로 전극 물질로는 Al을 e-beam evaporator를 이용하여 150 nm 두께로 증착하였다. 제작된 소자에서 메모리 측정을 한 결과, 같은 크기의 윈도우를 비교하였을 때 산화시킨 AlOx를 tunnel layer로 적용한 MOSCAP에서 더 적은 전압으로도 program 동작이 나타나는 것을 확인하였다. 또한 내구성을 확인하기 위해 program/erase를 103회 반복하여 endurance를 측정한 결과, 스퍼터로 증착한 AlOx를 적용한 MOSCAP에서는 24 %의 메모리 윈도우 감소가 일어난 반면에, 산화시킨 AlOx를 적용한 MOSCAP에서는 메모리 윈도우 감소가 5 % 미만으로 일어났다. 결과적으로 산화시킨 AlOx를 메모리소자의 tunnel layer로 적용한 MOSCAP에서 더 뛰어난 내구성을 나타냈으며, 추후 최적의 oxide 두께와 열처리 조건을 통해 더 뛰어난 메모리 특성을 가지는 메모리 디바이스 제작이 가능할 것으로 기대된다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.47-57
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• 2008

Recently, energy-efficient NAND Flash memory of large volume is favored as next-generation storage for sensor nodes. So far, most sensor node file systems are based on NOR flash and few file systems are applicable to large NAND flash memory. Although it is required to develop new file systems taking account of the features of NAND flash memory, it is difficult to develop them mainly due to the limit of SRAM memory on sensor nodes. Sensor nodes support SRAM of $4{\sim}10$ KBytes only. In this paper, we designed and implemented a novel file system to support data-centric applications. To do this, we added EEPROM of 1 KBytes to store persistent file description data efficiently and devised a simple wear-leveling method. This reduces the number of page updates, resulting in reduction in energy use and increase in lifetime of sensor nodes.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.77-82
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• 2014

SD memory cards are widely used in portable digital devices, and most of them exploit NAND flash memory as their storage, so that they have a feature of storing users' important data safely with low costs. In case of using NAND flash memory as storage, however, there is no method to store users' data if memory capacity is insufficient when transferring a large volume of data. This paper proposes a virtual SD memory card system. It used a SD memory card device driver to process data requested from a host by exploiting external storage rather than by exploiting flash memory as a memory core for storing data to the SD memory card. For experiment, it used the FPGA-based SD card slave controller IP on the SMC controller with a S3C2450 ARM CPU to test.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.313-315
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• 2013

Hand-held devices such as smart phones exploit flash memory based storages to store data for processing jobs. Since the flash memory, non-volatile memory, is able to store mass data, it is required to use the index for processing queries. However, the flash memory has the shortcomings that it does not support the overwrite operation and its write operation is very slow. In this paper, we build the fixed grid file, one of the multi-dimensional spatial index, on a flash memory and evaluate the performance test.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.755-758
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• 2007

플래시 메모리는 휴대폰, MP3 플레이어, 개인휴대정보단말기(PDA), 휴대용 멀티미디어 플레이어(PMP), 디지털 카메라 및 캠코더와 같은 이동성이 강한 소형기기에서 가장 많이 사용되는 저장 매체이다. 최근 대용량의 값싼 플래시 메모리가 등장하면서 랩톱이나 데스크톱과 같은 일반적인 컴퓨팅 환경을 지닌 기기들에서도 그 사용이 확대되고 있는 추세이다. 플래시 메모리가 보다 범용적인 저장 장치로 사용되기 위해서는 일반 컴퓨팅 환경에서의 복잡한 작업 부하에서도 우수한 성능을 제공할 수 있는 플래시 변환 계층(Flash Translation Layer)이 반드시 필요하다. 아쉽게도 현재까지 연구된 FTL 기법들은 소형기기의 단순한 작업 부하에 알맞도록 설계되어 있으며, 일반 컴퓨팅 환경과 같이 복잡한 작업 부하를 지닌 환경에서는 우수한 성능을 제공하지 못한다는 단점을 가지고 있다. 본 논문에서는 일반 컴퓨팅 환경의 복잡한 작업 부하에 대해서도 우수한 가비지 수집 성능을 제공하는 새로운 로그 블록 교체 기법을 제안하였다. 실험을 통한 평가 결과, 제안한 기법은 기존 기법 대비 평균 35% 정도의 가비지 수집 부하를 감소시키는 것으로 나타났다.

• Journal of KIISE
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• v.41 no.9
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• pp.674-685
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• 2014

The Knowledge service system needs to infer a new knowledge from indicated knowledge to provide its effective service. Most of the Knowledge service system is expressed in terms of ontology. The volume of knowledge information in a real world is getting massive, so effective technique for massive data of ontology is drawing attention. This paper is to provide the method to infer massive data-ontology to the extent of RDFS, based on cloud computing environment, and evaluate its capability. RDFS inference suggested in this paper is focused on both the method applying MapReduce based on RDFS meta table, and the method of single use of cloud computing memory without using MapReduce under distributed file computing environment. Therefore, this paper explains basically the inference system structure of each technique, the meta table set-up according to RDFS inference rule, and the algorithm of inference strategy. In order to evaluate suggested method in this paper, we perform experiment with LUBM set which is formal data to evaluate ontology inference and search speed. In case LUBM6000, the RDFS inference technique based on meta table had required 13.75 minutes(inferring 1,042 triples per second) to conduct total inference, whereas the method applying the cloud computing memory had needed 7.24 minutes(inferring 1,979 triples per second) showing its speed twice faster.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.117-126
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• 2008

Recent advances in medical imaging technologies have enabled the high-resolution data acquisition. Therefore visualization of such large data set on standard graphics hardware became a popular research theme. Among many visualization techniques, we focused on bricking method which divided the entire volume into smaller bricks and rendered them in order. Since it switches bet\W8n bricks on main memory and bricks on GPU memory on the fly, to achieve better performance, the number of these memory swapping conditions has to be minimized. And, because the original bricking algorithm was designed for regular volume data such as CT and MR, when applying the algorithm to ultrasound volume data which is based on the toroidal coordinate space, it revealed some performance degradation. In some areas near bricks' boundaries, an orthogonal viewing ray intersects the single brick twice, and it consequently makes a single brick memory to be uploaded onto GPU twice in a single frame. To avoid this redundancy, we divided the volume into bricks allowing overlapping between the bricks. In this paper, we suggest the formula to determine an appropriate size of these shared area between the bricks. Using our formula, we could minimize the memory bandwidth. and, at the same time, we could achieve better rendering performance.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.521-526
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• 2016

As on-line informal text data have massive in its volume and have unstructured characteristics in nature, there are limitations in applying traditional relational data model technologies for data storage and data analysis jobs. Moreover, using dynamically generating massive social data, social user's real-time reaction analysis tasks is hard to accomplish. In the paper, to capture easily the semantics of massive and informal on-line documents with unsupervised learning mechanism, we design and implement automatic topic extraction systems according to the mass of the words that consists a document. The input data set to the proposed system are generated first, using N-gram algorithm to build multiple words to capture the meaning of the sentences precisely, and Hadoop and Spark (In-memory distributed computing framework) are adopted to run topic model. In the experiment phases, TB level input data are processed for data preprocessing and proposed topic extraction steps are applied. We conclude that the proposed system shows good performance in extracting meaningful topics in time as the intermediate results come from main memories directly instead of an HDD reading.