Digital Library[ Search Result ]
A GRU-based Time-Series Forecasting Method using Patching
http://doi.org/10.5626/JOK.2024.51.7.663
Time series forecasting plays a crucial role in decision-making within various fields. Two recent approaches, namely, the patch time series Transformer (PatchTST) and the long-term time series foraging linear (LTSF-Linear) of the MLP structure have shown promising performance in this area. However, PatchTST requires significant time for both model training and inference, while LTSF-Linear has limited capacity due to its simplistic structure. To address these limitations, we propose a new approach called patch time series GRU (PatchTSG). By leveraging a Gated Recurrent Unit (GRU) on the patched data, PatchTSG reduces the training time and captures valuable information from the time series data. Compared to PatchTST, PatchTSG achieves an impressive reduction in learning time (up to 82%) and inference time (up to 46%).
A GCN-based Time-Series Data Anomaly Detection Method using Sensor-specific Time Lagged Cross Correlation
Kangwoo Lee, Yunyeong Kim, Sungwon Jung
http://doi.org/10.5626/JOK.2023.50.9.805
Anomaly detection of equipment through time series data is a very important because it can prevent further damage and contribute to productivity improvement. Although research studies on time series data anomaly detection are being actively conducted, but they have the following restrictions. First, unnecessary false alarms occur because correlations with other sensors are not considered. Second, although complete graph modeling and GAT have been applied to analyze the correlation of each sensor, this method requires a lot of time due to the increase in unnecessary operations. In this paper, we propose SC-GCNAD(Sensor-specific Correlation GCN Anomaly Detection) to address these problems. SC-GCNAD can analyze the exact correlation of each sensor by applying TLCC that reflects characteristics of time series data. It utilize GCN with excellent model expressiveness. As a result, SC-GCNAD can improve F1-Score by up to 6.37% and reduce analysis time by up to 95.31% compared to the baseline model.
Spatial LSM Tree for Indexing Blockchain-based Geospatial Point Data
Minjun Seo, Taehyeon Kwon, Sungwon Jung
http://doi.org/10.5626/JOK.2022.49.10.898
Blockchain technology is attracting attention for its high usability in various fields such as IoT and healthcare, and it is being used as an alternative to distributed databases. Despite their high usability for blockchain, the techniques for efficiently indexing blockchain-based geospatial data have not been studied much until now. Therefore, in this paper, we propose a spatial LSM tree indexing method that reduces the I/O cost when a block of geospatial point data is inserted into a blockchain by reflecting the write-intensive features of the blockchain. The proposed method linearizes geospatial data through Geohash on the blockchain where a large scale of real-time updates occur. It also minimizes the I/O cost when processing a range query and inserting data into the blockchain by taking the spatial proximity of the point data into account. Also, we propose a spatial filter to reduce unnecessary traversal of spatial LSM tree for processing geospatial point data range queries.
An Efficient Document Clustering Method using Space Transformation based on LDA and WMD
http://doi.org/10.5626/JOK.2021.48.9.1052
The existing TF-IDF-based document clustering methods do not properly exploit the contextual information of documents, i.e., co-occurence and word-order, and tend to degrade the performance due to the curse of dimensionality. To overcome these problems, the techniques such as a weighted average of word embedding vectors or Word Mover"s Distance (WMD) have been proposed. The performance of the techniques is good at document classification, but not a document clustering that needs to group documents. In this study, we define a document group as a topic document using LDA, the document group"s representative document, and solve the existing problem by calculating the WMD based on the topic document. However, since WMD requires a large amount of computation, we propose a space transformation method that shows a good performance while reducing the computation cost by mapping each document to a low-dimensional space in which each axis means WMD value from each topic document.
An Efficient MapReduce-based Skyline Query Processing Method with Two-level Grid Blocks
http://doi.org/10.5626/JOK.2017.44.6.613
Skyline queries are used extensively to solve various problems, such as in decision making, because they find data that meet a variety of user criteria. Recent research has focused on skyline queries by using the MapReduce framework for large database processing, mainly in terms of applying existing index structures to MapReduce. In a skyline, data closer to the origin dominate more area. However, the existing index structure does not reflect such characteristics of the skyline. In this paper, we propose a grid-block structure that groups grid cells to match the characteristics of a skyline, and a two-level grid-block structure that can be used even when there are no data close to the origin. We also propose an efficient skyline-query algorithm that uses the two-level grid-block structure.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr