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Efficient Dynamic Graph Processing Based on GPU Accelerated Scheduling and Operation Reduction
Sangho Song, Jihyeon Choi, Donghyeon Cha, Hyeonbyeong Lee, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2024.51.12.1125
Recent research has focused on utilizing GPUs to process large-scale dynamic graphs. However, processing dynamic graphs often leads to redundant data transmission and processing. This paper proposes an efficient scheme for processing large-scale dynamic graphs in memory-constrained GPU environments. The proposed scheme consists of dynamic scheduling and operation reduction methods. The dynamic scheduling method involves partitioning dynamic graph and maximizing GPU processing power by scheduling partitions based on active and potential active vertices. Also, snapshots are utilized to leverage the time-varying characteristics of the graph. The operation reduction method minimizes GPU computation and memory transfer costs by detecting redundant edge and vertex updates in dynamic graphs through snapshots. By avoiding redundant operations on the same edges or vertices, this method improves performance. Through various performance evaluations, the proposed scheme showed 280% and 108% performance improvements on average compared to a static graph processing scheme and a dynamic graph processing scheme, respectively.
An Efficient Continuous Subgraph Matching Technique for Graph Stream Processing in a Memory-constrained Environment
Somin Lee, Sanghyeuk Kim, Hyeonbyeong Lee, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2022.49.12.1154
Recently, with the proliferation of social network services, the size of graph data has been becoming increasingly vast and graph data are changed in real-time. Therefore, it is necessary to perform continuous query processing on real-time graph streams. Moreover, it is difficult keep the entire large graph data in the main memory since its size is constrained in real-world application environments. Consequently, continuous subgraph matching techniques are required by considering memory-constrained environments. In this paper, we propose a continuous subgraph matching technique for graph streams in a memory-constrained environment. The proposed technique consists of modules such as index manager, query processor, and cache manager for efficient continuous subgraph matching. We conduct performance evaluations to demonstrate the superiority of the proposed technique.
An Efficient Distributed In-memory High-dimensional Indexing Scheme for Content-based Image Retrieval in Spark Environments
Dojin Choi, Songhee Park, Yeondong Kim, Jiwon Wee, Hyeonbyeong Lee, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2020.47.1.95
Content-based image retrieval that searches an object in images has been utilizing for criminal activity monitoring and object tracking in video. In this paper, we propose a high-dimensional indexing scheme based on distributed in-memory for the content-based image retrieval. It provides similarity search by using massive feature vectors extracted from images or objects. In order to process a large amount of data, we utilized a big data platform called Spark. Moreover, we employed a master/slave model for efficient distributed query processing allocation. The master distributes data and queries. and the slaves index and process them. To solve k-NN query processing performance problems in the existing distributed high-dimension indexing schemes, we propose optimization methods for the k-NN query processing considering density and search costs. We conduct various performance evaluations to demonstrate the superiority of the proposed scheme.
Recommending Similar Users Through Interaction Analysis in Social IoT Environments
Yeondong Kim, Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2020.47.1.61
Recently, there has been extensive research on the social internet of things(Social IoT) that combines social networks and internet of things. Social IoT is integral for the connection between as well as for establishing relationships between users and objects for sharing information between objects or users. In this paper, we propose a method that recommends similar users by considering interaction between objects and users in the social IoT environments. The similar users can be found by analyzing the behavior of the users around the object. The proposed method improves the accuracy of similarity by calculating similarity in determining interests based on documents written by users in social networks. Finally, it recommends Top-N users as similar users based on the two similarity values. To show the superiority of the proposed method, we conducted various performance evaluations.
An Approximate k-Nearest Neighbor Query Processing Method Based on a Dynamic Partitioning Grid Index in Distributed Processing Environments
Dojin Choi, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
http://doi.org/10.5626/JOK.2018.45.8.838
As smart devices continue to grow in popularity, various location-based services are increasingly provided to users. Some location-based social applications that combine social services and location-based services have a large number of users. The demands of a k-nearest neighbors (k-NN) query, which finds the k closest locations from a user location, are increased in services such as these. In this paper, we propose an approximate k-NN query processing method for real time response requirements for a dynamic partition based grid index. The proposed approximate k-NN query processing method first retrieves the related cells by considering a user movement. Then, we optimize cell searches in the dynamic partitioning method and grid index for the improvement of the accuracy of the proposed approximate k-NN query. The proposed method is implemented in Storm to perform efficient distributed processing in stream environments. In order to show the superiority of this method, we conduct various performance evaluations.
Trust Evaluation Scheme of Web Data Based on Provenance in Social Semantic Web Environments
Sangwon Yoon, Kitae Choi, Jaeyeol Park, Jongtae Lim, Kyoungsoo Bok, Jaesoo Yoo
Recently, as the generation and sharing of web data have increased, the importance of a social semantic web that combines the semantic web and the social web has also been increasing. In this paper, we propose a trust evaluation scheme based on provenance by extending the PROV model in the social semantic web environment. The proposed scheme manages the provenance of web data and adds the necessary elements for trust evaluation in the PROV model of W3C. The extended PROV model supports data management and provenance tracing. The proposed trust evaluation scheme considers various parameters such as user trust, original data trust, and user evaluation. The evaluated trust is managed as provenance. When processing a query, the proposed scheme generates a result by considering the trust. Therefore, the proposed scheme can manage the provenance of web data and compute data trust correctly by using such various parameters. The evaluated trust becomes a criterion to determine whether the query result can be trusted or not. In order to show the validity of the proposed scheme, we verify its performance using SPARQL queries.
User Reputation Management Method Based on Analysis of User Activities on Social Media
Jinkyung Yun, Jiwon Jeong, Suji Lee, Jongtae Lim, Kyungsoo Bok, Jaesoo Yoo
Recently, social network services have changed by moving towards an open platform where, as well as simply allowing the building of relationships among users, various types of information can be generated and shared. Since existing user reputation management methods evaluate user reliability based on user profiles, explicit relations, and evaluation, they are not suitable for determining user reliability on social media due to few explicit evaluation. In this paper, we analyze social activities on social media and propose a new user reputation management method that considers implicit evaluation as well as explicit evaluation. The proposed method derives positive and negative implicit evaluation from social activities, and generates user reputation information by field in order to consider user expertise. It also considers the number of users that participate in evaluation in order to measure user influence. As a result, it generates the reputation information of users who have no explicit evaluation and creates user reputation information that is more suitable for social media.
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