Dong-Oh Kim

Aggregate Queries in Wireless Sensor Networks

Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation (BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a quadtree, and then processes aggregate queries in parallel for each cell region according to routing. It sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.

Spatial TinyDB: A Spatial Sensor Database System for the USN Environment

For the Ubiquitous Sensor Network (USN) environment, which generally uses spatial as well as aspatial sensor data, a sensor database system to manage these data is essential. For this reason, sensor database systems such as TinyDB and Cougar are being developed by researchers. However, as most of these systems do not support spatial data types and spatial operators for managing spatial sensor data, they are not suitable for the USN environment. Therefore, in this paper, we design and implement Spatial TinyDB which is a spatial sensor database system that extends TinyDB to support spatial data types and spatial operators for the efficient management of spatial sensor data. In particular, Spatial TinyDB provides memory management and filtering functions to reduce system overload caused by sensor data streams. Finally, we prove that Spatial TinyDB is superior by comparing its actual performance, in terms of execution time, accuracy, and memory usage, with that of TinyDB.

Adaptive metadata rebalance in exascale file system

This paper presents an effective method of metadata rebalance in exascale distributed file systems. Exponential data growth has led to the need for an adaptive and robust distributed file system whose typical architecture is composed of a large cluster of metadata servers and data servers. Though each metadata server can have an equally divided subset from the entire metadata set at first, there will eventually be a global imbalance in the placement of metadata among metadata servers, and this imbalance worsens over time. To ensure that disproportionate metadata placement will not have a negative effect on the intrinsic performance of a metadata server cluster, it is necessary to recover the balanced performance of the cluster periodically. However, this cannot be easily done because rebalancing seriously hampers the normal operation of a file system. This situation continues to get worse with both an ever-present heavy workload on the file system and frequent failures of server components at exascale. As one of the primary reasons for such a degraded performance, file system clients frequently fail to look up metadata from the metadata server cluster during the period of metadata rebalance; thus, metadata operations cannot proceed at their normal speed. We propose a metadata rebalance model that minimizes failures of metadata operations during the metadata rebalance period and validate the proposed model through a cost analysis. The analysis results demonstrate that our model supports the feasibility of online metadata rebalance without the normal operation obstruction and increases the chances of maintaining balance in a huge cluster of metadata servers.

High Performance Metadata Management Engine for Large-Scale Distributed File Systems

This paper presents a metadata management engine with considerable performance and scalability that can be used in a nationwide file storage service. While a metadata server cluster is needed to support a huge number of metadata requests in a large-scale file storage, in reality, there are few such clusters with a product level quality. We propose a metadata management architecture with several new features including the maximum performance potentialities using a single metadata server. Based on this architecture, we implemented a new file system that is now being used for many storage services on Internet.

Workload characteristics of DNA sequence analysis: from storage systems' perspective

The recent development of NGS (Next Generation Sequencing) methods has greatly increased the amount of genome data and created the need for high-performance computing and high-performance storage systems. The key issue in developing high-performance storage systems is building a storage system that is optimized for NGS analysis pipeline. In this paper, we implemented a tool to collect and analyze I/O workload in NGS analysis pipeline. Using this tool, we executed NGS analysis pipeline and analyzed the characteristics of I/Os collected in the experiment.

Genome Analysis Pipeline I/O Workload Analysis

As size of genomic data is increasing rapidly, the needs for high-performance computing system to process and store genomic data is also increasing. In this paper, we captured I/O trace of a system which analyzed 500 million sequence reads data in Genome analysis pipeline for 86 hours. The workload created 630 file with size of 1031.7 Gbyte and deleted 535 file with size of 91.4 GByte. What is interesting in this workload is that 80% of all accesses are from only two files among 654 files in the system. Size of read and write request in the workload was larger than 512 KByte and 1 Mbyte, respectively. Majority of read write operations show random and sequential patterns, respectively. Throughput and bandwidth observed in each processing phase was different from each other.

Audit Checking Items for the Efficient GIS Audit

The construction of the Geographical Information System(GIS) is continuously in progress, and with the diversification and complication of the system, the importance of its audit is further deepened. At this point in time, the information system audit has been obligatory as it has been stipulated in law, and the GIS audit has come to follow the criterion of the information system audit since it belongs to the information system audit category. However, it was found that there was a difference between the current information system audit standard and the former GIS audit standard, and the audit checking items for the efficient GIS audit is necessary. For this reason, this paper surveyed the characteristics of GIS in accordance with this necessity, analyzed the GIS audit checking items found in the audit performance guideline of the GIS audit standard, and studied the GIS development methodology through the related literature. In addition, this paper also elicited and proposed the audit checking items for the efficient GIS audit based on the findings of this paper. Finally, this paper confirmed the efficiency of the GIS audit checking items proposed here through the comparison of the actual cases of GIS audits.