• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.89-95
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• 2009

This paper proposes a tag indexing scheme for RFID tag using spatial index. The tag being used for the inventory management and the tag's location is determined by the position of readers. Therefore, the reader recognizes the tag, which is attached products and thereby their positions can be traced down. In this paper, we propose hTag-tree( Hybrid Tag index) which manages RFID tag attached products. hTag-tree is a new index, which is based on tag's attributes with fast searching, and this tag index manages RFID tags using reader's location. This tag index accesses rapidly to tags for insertion, deletion and updating in dynamic environment. This can minimize the number of node accesses in tag searching comparing to previous techniques. Also, by the extension of MER in present tag index, it is helpful to stop the lowering of capacity which can be caused by parent node approach. The proposed index experiment deals with the comparison of tag index. Fixed Interval R-tree, and present spatial index, R-tree comparison. As a result, the amount of searching time is significantly shortened through hTag-tree node access in data search. This shows that the use of proposed index improves the capacity of effective management of a large amount of RFID tag.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.47-54
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• 2005

정보 사회가 발전하고 정보의 활용이 늘어남에 따라 공간 데이타가 다양한 분야에서 활용되고 있다. 그리고, 공간 데이타가 널리 활용됨에 따라 ESRI와 같은 다양한 지리 정보 시스템(Geography Information System)이 발전하게 되었다. 그러나, 기존의 지리 정보 시스템은 다양한 분야에서의 활용을 위해 많은 기능을 제공함으로 인해 일반적으로 대용량 공간 데이타에 대한 검색이 비효율적이다. 특히, 네트워크 환경이 발전하고 컴퓨팅 파워가 증가함에 따라 점차 대용량의 지도 서비스를 제공하는 분야에서 기존의 지리 정보 시스템 적용 시공간 데이타 검색 속도가 저하되는 문제가 발생한다. 따라서, 본 논문에서는 안정적인 상용 DBMS인 Oracle을 기반으로 대용량 공간 데이터를 효율적으로 검색할 수 있는 Oracle 기반의 대용량 지도 서비스를 위한 XServer를 설계 및 개발하였다. XServer는 다양한 클라이언트의 질의를 효율적으로 처리하기 위한 질의 처리 관리자, 대용량의 공간 데이타를 빠르게 검색하기 위한 공간 인덱스 관리자 및 데이타 버퍼 관리자, 대용량의 공간 데이타를 안정적으로 저장하기 위해서 Shape 화일에서 추출한 공간 데이타를 Oracle에 저장 및 관리하기 위한 수입/수출 관리자와 DB 관리자로 구성되어있다. 마지막으로, 본 연구에서 개발한 Oracle 기반의 대용량 지도 서비스를 위한 XServer와 Oracle Spatial을 비교함으로써 기능을 검증하고 성능의 우수함을 입증하였다.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.487-496
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• 2009

This paper addresses an indexing scheme capable of efficiently processing range queries in a large-scale trajectory database. After discussing the drawbacks of previous indexing schemes, we propose a new scheme that divides the temporal dimension into multiple time intervals and then, by this interval, builds an index for the line segments. Additionally, a supplementary index is built for the line segments within each time interval. This scheme can make a dramatic improvement in the performance of insert and search operations using a main memory index, particularly for the time interval consisting of the segments taken by those objects which are currently moving or have just completed their movements, as contrast to the previous schemes that store the index totally on the disk. Each time interval index is built as follows: First, the extent of the spatial dimension is divided onto multiple spatial cells to which the line segments are assigned evenly. We use a 2D-tree to maintain information on those cells. Then, for each cell, an additional 3D $R^*$-tree is created on the spatio-temporal space (x, y, t). Such a multi-level indexing strategy can cure the shortcomings of the legacy schemes. Performance results obtained from intensive experiments show that our scheme enhances the performance of retrieve operations by 3$\sim$10 times, with much less storage space.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.302-304
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• 1999

최근 이미지 데이터에 대한 요구가 폭발적으로 증가됨에 따라 대용량 이미지 데이터에 대한 저장과 검색에 관한 연구가 활발히 진행되고 있다. 그러나 이미지 데이터는 기존의 텍스트 데이터에 비해 대용량이라는 특성과 비정형적인 특성을 가지고 있어 신속하고 효율적인 검색에 많은 어려움이 있다. 본 논문에서는 기존에 이미지 검색을 위해 제안된 인덱스 구조중 고차원 특성을 효율적으로 수용하고 저장공간의 이용률과 검색성능이 뛰어난 CIR-트리를 국내에서 개발된 상용 데이터베이스 시스템인 바다의 하부 저장구조인 MiDAS 기반에서 구현한다. CIR-트리 관리기를 갖는 MiDAS-III에서 K-NN 질의 및 범위 질의가 처리될 때 순차검색에 비해 약 60~99%정도의 검색성능이 향상되었다.

• The KIPS Transactions:PartA
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• v.15A no.6
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• pp.325-334
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• 2008

Flash memory is widely used as a storage medium for mobile devices such as cell phones, MP3 players, PDA's due to its tiny size, low power consumption and shock resistant characteristics. Additionally, some computer manufacturers try to replace hard-disk drives used in Laptops or personal computers with flash memory. More recently, there are some literatures on developing a flash memory-aware $B^+$-tree index for an efficient key-based search in the flash memory storage system. They focus on minimizing the number of "overwrites" resulting from inserting or deleting a sequence of key values to/from the $B^+$-tree. However, in addition to this factor, the size of a physical page allocated to a node can affect the maintenance cost of the $B^+$-tree. In this paper, with diverse experiments, we compare and analyze the costs of construction and search of the $B^+$-tree and the space requirement on flash memory as the node size increases. We also provide sorting-based or non-sorting-based algorithms to be used when inserting a key value into the node and suggest an header structure of the index node for searching a given key inside it efficiently.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.157-164
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• 2006

Since XML is emerging as the de facto standard for exchanging and representation of data on the web, the amount of XML data has rapidly increased. Thus, the need for effective store and retrieval of U data has arisen. Since the existing techniques such as XRel which is an XML storage and management technique using RDBMS simply record the existing all label paths, diverse classes of label path expressions could not be efficiently supported. In this paper, we present a technique which supports storage and retrieval for XML data using RDBMS efficiently compared with the existing approaches. Since the proposed technique keeps the XML path index on the relational database and replace label paths with path identifiers, diverse XML queries can be evaluated compared with existing approaches. Also, the proposed technique does not require the modification of the relational database engine and consumes the disk space less. Our experimental result demonstrates the better query performance compared with existing techniques.

• Journal of KIISE:Databases
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• v.31 no.5
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• pp.468-478
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• 2004

It is essential in various application areas of data mining and bioinformatics to effectively retrieve the occurrences of interesting patterns from sequence databases. For example, let's consider a network event management system that records the types and timestamp values of events occurred in a specific network component(ex. router). The typical query to find out the temporal casual relationships among the network events is as fellows: 'Find all occurrences of CiscoDCDLinkUp that are fellowed by MLMStatusUP that are subsequently followed by TCPConnectionClose, under the constraint that the interval between the first two events is not larger than 20 seconds, and the interval between the first and third events is not larger than 40 secondsTCPConnectionClose. This paper proposes an indexing method that enables to efficiently answer such a query. Unlike the previous methods that rely on inefficient sequential scan methods or data structures not easily supported by DBMSs, the proposed method uses a multi-dimensional spatial index, which is proven to be efficient both in storage and search, to find the answers quickly without false dismissals. Given a sliding window W, the input to a multi-dimensional spatial index is a n-dimensional vector whose i-th element is the interval between the first event of W and the first occurrence of the event type Ei in W. Here, n is the number of event types that can be occurred in the system of interest. The problem of‘dimensionality curse’may happen when n is large. Therefore, we use the dimension selection or event type grouping to avoid this problem. The experimental results reveal that our proposed technique can be a few orders of magnitude faster than the sequential scan and ISO-Depth index methods.hods.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.67-81
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• 2005

Recently, the interest and research on the development of new application services such as the Location Based Service and Telemetics providing the emergency service, neighbor information search, and route search according to the development of the Geographic Information System have been increasing. User's search in the spatio-temporal database which is used in the field of Location Based Service or Telemetics usually fixes the current time on the time axis and queries the spatial and aspatial attributes. Thus, if the range of query on the time axis is extensive, it is difficult to efficiently deal with the search operation. For solving this problem, the snapshot, a method to summarize the location data of moving objects, was introduced. However, if the range to store data is wide, more space for storing data is required. And, the snapshot is created even for unnecessary space that is not frequently used for search. Thus, non storage space and memory are generally used in the snapshot method. Therefore, in this paper, we suggests the Hash-based Spatio-Temporal Clustering Algorithm(H-STCA) that extends the two-dimensional spatial hash algorithm used for the spatial clustering in the past to the three-dimensional spatial hash algorithm for overcoming the disadvantages of the snapshot method. And, this paper also suggests the knowledge extraction algorithm to extract the knowledge for the path search of moving objects from the past location data based on the suggested H-STCA algorithm. Moreover, as the results of the performance evaluation, the snapshot clustering method using H-STCA, in the search time, storage structure construction time, optimal path search time, related to the huge amount of moving object data demonstrated the higher performance than the spatio-temporal index methods and the original snapshot method. Especially, for the snapshot clustering method using H-STCA, the more the number of moving objects was increased, the more the performance was improved, as compared to the existing spatio-temporal index methods and the original snapshot method.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.455-465
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• 2009

Suffix trees are widely used in similar sequence matching for DNA. They have several problems such as time consuming, large space usages of disks and memories and data skew, since DNA sequences are very large and do not fit in the main memory. Thus, in the paper, we present a space efficient indexing method called SENoM, allowing us to build trees without merging phases for the partitioned sub trees. The proposed method is constructed in two phases. In the first phase, we partition the suffixes of the input string based on a common variable-length prefix till the number of suffixes is smaller than a threshold. In the second phase, we construct a sub tree based on the disk using the suffix sets, and then write it to the disk. The proposed method, SENoM eliminates complex merging phases. We show experimentally that proposed method is effective as bellows. SENoM reduces the disk usage less than 35% and reduces the memory usage less than 20% compared with TRELLIS algorithm. SENoM is available to query efficiently using the prefix tree even when the length of query sequence is large.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.623-634
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• 2016

Most of existing frequent pattern mining methods address time efficiency and greatly rely on the primary memory. However, in the era of big data, the size of real-world databases to mined is exponentially increasing, and hence the primary memory is not sufficient enough to mine for frequent patterns from large real-world data sets. To solve this problem, there are some researches for frequent pattern mining method based on disk, but the processing time compared to the memory based methods took very time consuming. There are some researches to improve scalability of frequent pattern mining, but their processes are very time consuming compare to the memory based methods. In this paper, we present PPFP as a novel disk-based approach for mining frequent itemset from big data; and hence we reduced the main memory size bottleneck. PPFP algorithm is based on FP-growth method which is one of the most popular and efficient frequent pattern mining approaches. The mining with PPFP consists of two setps. (1) Constructing an IFP-tree: After construct FP-tree, we assign index number for each node in FP-tree with novel index numbering method, and then insert the indexed FP-tree (IFP-tree) into disk as IFP-table. (2) Mining frequent patterns with PPFP: Mine frequent patterns by expending patterns using stack based PUSH-POP method (PPFP method). Through this new approach, by using a very small amount of memory for recursive and time consuming operation in mining process, we improved the scalability and time efficiency of the frequent pattern mining. And the reported test results demonstrate them.