• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.11-12
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• 2019

IoT 및 SNS의 발달로 인해 관계를 표현하는 그래프 모델링 기법이 활용되고 있다. 실시간 스트림 그래프에서 유사한 모형의 그래프를 탐색하기 위한 근사 Top-k 서브 그래프 매칭에 대한 요구가 증가하고 있다. 본 논문에서는 그래프 스트림에서 간선의 유형 및 구조적 차이를 고려한 효율적인 근사 Top-k 서브 그래프 매칭 기법을 제안한다. 임계값 기반의 필터링과 스트림 환경에 맞는 연속 서브 그래프 매칭 구조를 제안함으로써 그래프 스트림에 적합한 질의 처리를 수행한다.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.42-53
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• 2020

With the development of social network services, graph structures have been utilized to represent relationships among objects in various applications. Recently, a demand of subgraph matching in real-time graph streams has been increased. Therefore, an efficient approximate Top-k subgraph matching scheme for low latency in real-time graph streams is required. In this paper, we propose an approximate Top-k subgraph matching scheme considering data reuse in graph stream environments. The proposed scheme utilizes the distributed stream processing platform, called Storm to handle a large amount of stream data. We also utilize an existing data reuse scheme to decrease stream processing costs. We propose a distance based summary indexing technique to generate Top-k subgraph matching results. The proposed summary indexing technique costs very low since it only stores distances among vertices that are selected in advance. Finally, we provide k subgraph matching results to users by performing an approximate Top-k matching on the summary indexing. In order to show the superiority of the proposed scheme, we conduct various performance evaluations in diverse real world datasets.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.35-46
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• 2022

Recently, with the development of network technologies, as IoT and social network service applications have been actively used, a lot of graph stream data is being generated. In this paper, we propose a graph compression scheme that considers the stream graph environment by applying graph mining to the existing compression technique, which has been focused on compression rate and runtime. In this paper, we proposed Incremental frequent pattern based compression technique for graph streams. Since the proposed scheme keeps only the latest reference patterns, it increases the storage utilization and improves the query processing time. In order to show the superiority of the proposed scheme, various performance evaluations are performed in terms of compression rate and processing time compared to the existing method. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.287-288
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• 2019

그래프에서 이상 패턴은 정상 그래프와 상이하게 다른 양상을 갖는 그래프를 의미한다. 이상 패턴을 판단하기 위해서는 정상데이터 정확한 정의가 요구된다. 본 논문에서는 스트림 그래프에서 실시간으로 이상 패턴을 감지하는 기법을 제안한다. 제안하는 기법은 정상 서브그래프의 패턴(정상 패턴)을 정의하고 정점 간 연결 관계를 고려한다.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.465-475
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• 2018

Recently, as the use of social media and IoT has increased, large graph streams has been generating and studies on real-time processing for them have been actively carrying out. In this paper we propose a incremental graph stream processing scheme that reuses previous result data when the graph changes continuously. We also propose a cost model to selectively perform incremental processing and static processing. The proposed cost model computes the predicted value of the detection cost and the processing cost of the recalculation area based on the actually processed history and performs the incremental processing when the incremental processing is more profit than the static processing. The proposed incremental processing increases the efficiency by processing only the part that changes when the graph update occurs. Also, by collecting only the previous result data of the changed part and performing the incremental processing, the disk I/O costs are reduced. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.25-34
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• 2007

Existing approaches that select an order for the join of three or more data streams have always used the simple heuristics. For their disadvantage - only one factor is considered and that is join selectivity or arrival rate, these methods lead to poor performance and inefficiency In some applications. The graph-based sliding window multi -join algorithm with optimal join sequence is proposed in this paper. In this method, sliding window join graph is set up primarily, in which a vertex represents a join operator and an edge indicates the join relationship among sliding windows, also the vertex weight and the edge weight represent the cost of join and the reciprocity of join operators respectively. Then the optimal join order can be found in the graph by using improved MVP algorithm. The final result can be produced by executing the join plan with the nested loop join procedure, The advantages of our algorithm are proved by the performance comparison with existing join algorithms.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.417-419
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• 2000

본 논문은 실시간 운영체제 기반의 ITV 시스템에서 MPEG-4 멀티미디어 장면을 실시간으로 랜더링하는 방법을 제시한다. MPEG-4 멀티미디어 스트림의 랜더링을 위한 장면 구성은 ITV 시스템에서 네트워크를 통해 전송되는 멀티미디어 스트림을 파싱 및 해석하여 장면그래프를 구성하고, 장면 랜더러가 이 장면 그래프를 이용하여 화면에 실시간으로 랜더링한다. MPEG-4 장면 랜더링 화면에서 사용자의 객체 교체나 객체 속성 변환 랜더링 이벤트가 발생하면, MPEG-4 장면 그래프를 실시간으로 갱신시켜 사용자 인터페이스에 랜더링한다.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.147-157
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• 2018

Recently, with the advancement of network technologies, and the activation of IoT and social network services, many graph stream data have been generated. As the relationship between objects in the graph streams changes dynamically, studies have been conducting to detect or analyze the change of the graph. In this paper, we propose a scheme to incrementally detect frequent patterns by using frequent patterns information detected in previous sliding windows. The proposed scheme calculates values that represent whether the frequent patterns detected in previous sliding windows will be frequent in how many future silding windows. By using the values, the proposed scheme reduces the overall amount of computation by performing only necessary calculations in the next sliding window. In addition, only the patterns that are connected between the patterns are recognized as one pattern, so that only the more significant patterns are detected. We conduct various performance evaluations in order to show the superiority of the proposed scheme. The proposed scheme is faster than existing similar scheme when the number of duplicated data is large.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.39-40
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• 2017

그래프 스트림 데이터에 대한 활용이 증가됨에 따라 빈발 패턴을 검출하는 연구가 활발하게 진행되고 있다. 본 논문에서는 슬라이딩 윈도우 내에 변경된 부분만을 계산하는 점진적인 빈발 패턴 검출 기법을 제안한다. 제안하는 기법은 윈도우에서 변경되는 부분만 계산함으로써 중복된 계산을 감소시킨다. 또한, 간선 관리 테이블을 이용해 관련이 없는 패턴들을 제거함으로써 의미 있는 빈발 패턴만을 검출한다.

• Journal of KIISE:Databases
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• v.27 no.2
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• pp.185-198
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• 2000

Recently the technological advance in the hardware dealing with multimedia data as well as the explosive increase of the volume of multimedia data bring about new interest in the use of multimedia presentations in many application domains. To use multimedia presentations efficiently, the integration of multimedia presentations into DBMS is necessary. This paper presents a multimedia presentatation query language based on contents and query processing techniques. Presently, multimedia presentation authoring tools denote a multimedia presentation using a presentation graph which is a DAG. A Node in the graph is a same type of media stream and edges denote a play-out order and a synchronization way among nodes. The contents of presentations graphs are the information of each stream, the sequential order of the information inside each stream and the play-out order among the streams. GCalculus/S is a calculus-based query language and can deal with the contents of a presentation graph and physical characteristics of multimedia data. It expresses the sequential order of information inside each stream and the play-out order of streams of a presentation graph using temporal operators Next, Connected and Until. O-Algebra, which is object algebra, is extended to process GCalculus/S queries.