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An Embedding Technique for Weighted Graphs using LSTM Autoencoders
http://doi.org/10.5626/JOK.2021.48.1.13
Graph embedding is the representation of graphs as vectors in a low-dimensional space. Recently, research on graph embedding using deep learning technology have been conducted. However, most research to date has focused mainly on the topology of nodes, and there are few studies on graph embedding for weighted graphs, which has an arbitrary weight on the edges between the nodes. Therefore, in this paper, we proposed a new graph embedding technique for weighted graphs. Given weighted graphs to be embedded, the proposed technique first extracts node-weight sequences that exist inside the graphs, and then encodes each node-weight sequence into a fixed-length vector using an LSTM (Long Short-Term Memory) autoencoder. Finally, for each graph, the proposed technique combines the encoding vectors of node-weight sequences extracted from the graph to generate one final embedding vector. The embedding vectors of the weighted graphs obtained by the proposed technique can be used for measuring the similarity between weighted graphs or classifying weighted graphs. Experiments on synthetic and real datasets consisting of groups of similar weighted graphs showed that the proposed technique provided more than 94% accuracy in finding similar weighted graphs.
An Efficient Method of Processing Spatio-Temporal Joins in IoT (Internet of Things) Environments
Ki Yong Lee, Minji Seo, Ryong Lee, Minwoo Park, Sang-Hwan Lee
http://doi.org/10.5626/JOK.2019.46.1.86
A spatio-temporal join is an operation that connects multiple sets of data with the same spatial and temporal values. Especially with the increasing spread of IoT (Internet of Things), the need for spatio-temporal joins is also increasing in order to retrieve data generated at the same time and location among the data generated by different things in the past. In this study, we propose an efficient method for processing spatio-temporal joins on IoT data. The proposed method divides the 3D spatio-temporal space into small subspaces and maintains the identifiers of things whose data are present in each subspace. When a spatio-temporal join between things is requested, the proposed method identifies the spaces in which the things’ data are close to each other. Then, it retrieves data contained in the identified spaces and performs the join only between them. Therefore, because only that data with the possibility of being joined are accessed, the execution cost is greatly reduced. The experimental results on a real IoT dataset show that the proposed method significantly reduces the execution time compared to the existing spatio-temporal methods.
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