Byoungwook Kim's research while affiliated with Gangneung-Wonju National University and other places
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Publications (21)
Classification of representative spatiotemporal documents from text data and extraction of representative spatiotemporal information are necessary studies for searching for specific events and accidents, and real-time time and place recognition for events. However, this task is challenging due to the absence of data and difficulties in recognizing...
As the use of social media and online news media increases, the amount of text data online is rapidly increasing. As the use of spatio-temporal information increases, research on extracting spatio-temporal information from text data and detecting events is being actively conducted. However, the document also contains insignificant and trivial spati...
As the scale of online news and social media expands, attempts to analyze the latest social issues and consumer trends are increasing. Research on detecting spatio-temporal event sentences in text data is being actively conducted. However, a document contains important spatio-temporal events necessary for event analysis, as well as non-critical eve...
As location information of numerous Internet of Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Existing DBS...
With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is bei...
With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association ru...
In data mining, there are several clustering algorithms that utilize a spatial attribute to group spatial objects on geometric space. However, a spatial object can have a non-spatial attribute as well as spatial attribute, but there are not many clustering algorithms that utilize both the spatial attribute and the non-spatial attribute yet. Jaccard...
Graph clustering is a technique for grouping vertices having similar characteristics into the same cluster. It is widely used to analyze graph data and identify its characteristics. Recently, a large-capacity large-scale graph data is being generated in a variety of applications such as a social network service, a world wide web, and a telephone ne...
With the growth of artificial intelligence technology, the importance of recommender systems that recommend personalized content has increased. The general form of the recommender system usually analyzes the users' log information to provide them with contents that they are interested in. However, to enable users to receive more suitable and person...
Given a data sequence, sequential pattern mining, which finds frequent sequence patterns among them, is an important data mining problem. However, in the existing sequential pattern mining, only the purchase order of the items is considered, and the position where the item is purchased is not considered. In this paper, we developed a sequential pat...
To improve the quality of spatial information in a location‐based services (LBS), crowdsourced LBS (cLBS) applications that receive additional information such as the visit time of static spatial objects from users have appeared. In this paper, we propose a new type of nearest neighbor (NN) query called the k‐nearest reliable neighbor (kNRN) query,...
The authors would like to make the following corrections to this paper [...]
Data mining plays a critical role in sustainable decision-making. Although the k-prototypes algorithm is one of the best-known algorithms for clustering both numeric and categorical data, clustering a large number of spatial objects with mixed numeric and categorical attributes is still inefficient due to complexity. In this paper, we propose an ef...
– In the recent year, with the emergence of various smart devices, the data is explosively increasing in the social Internet of Things (IoT) such as human healthcare. These data mainly involve information about user behaviors collected from various heterogeneous wireless sensor and social networks. Therefore, it is vital to analyze the data to find...
Data mining plays a critical role in the sustainable decision making. The k-prototypes algorithm is one of the best-known algorithm for clustering both numeric and categorical data. Despite this, however, clustering a large number of spatial object with mixed numeric and categorical attributes is still inefficient due to its high time complexity. I...
The adaptive mobile resource offloading (AMRO) proposed in this paper is a load balancing scheme for processing large-scale jobs using mobile resources without a cloud server. AMRO is applied in a mobile cloud computing environment based on collaborative architecture. A load balancing scheme with efficient job division and optimized job allocation...
The setting of standards is a critical process in educational evaluation, but it is time-consuming and expensive because it is generally conducted by an education experts group. The purpose of this paper is to find a suitable cluster validity index that considers the futures of item response data for setting cut-off scores. In this study, nine repr...
Recently, with the development of embedded system hardware technology, there is a need to support various kinds of operating system (OS) operation in embedded systems. In mobile processors, ARM started to provide the virtualization extension support technology which was intended for processors in PC processors. Virtualization technology has the adv...
The k-means is one of the most popular and widely used clustering algorithm, however, it is limited to only numeric data. The k-prototypes algorithm is one of the famous algorithms for dealing with both numeric and categorical data. However, there have been no studies to accelerate k-prototypes algorithm. In this paper, we propose a new fast k-prot...
The item response data is the
nm
-dimensional data based on the responses made by
m
examinees to the questionnaire consisting of
n
items. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective co...
Citations
... According to the regulation of the roulette wheel selection, the higher the chance of individuals with high adaptation to enter the next generation [32] . The selection probability of (13) where means the fitness of a certain individual, k is the coefficient, n means the number of individuals, and means the probability of selection. ...
... In our previous study, we developed a CNN-based representative spatio-temporal document classifier [5]. The study used training data consisting only of temporal and spatial attributes, and presented only CNN as a deep learning model. ...
... Jang, Yang, Park, and Kim et al. [5] proposed the regionbased frequent pattern growth (RFP-Growth) algorithm to find association rules in dense regions. The study demonstrated that RFP-Growth discovered new association rules that the original FP-Growth could not find. ...
... The reviews that are gathered from social media regarding tourism provide a very huge amount of information for the extraction of preferences. Furthermore, all the comments that are semantically preprocessed as well as sentimentally analyzed are preprocessed for detecting tourist preferences [31], [32]. Similar to this, the features of these areas of interest are extracted using all the aggregated reviews. ...
... On 34 investigated the MapReduce framework to¯nd all frequent patterns in sequence databases in parallel by using Pre¯xSpan algorithm on location-based data. In their work, the location-based Pre¯xSpan algorithm and its parallel version, MapReduce Location-based Pre¯x-Span algorithm, were de¯ned. ...
... Jo et al. [184,185] proposed the Quadrant based Minimum Bounding Rectangle (QbMBR)-tree structure for processing large scale spatial data in HBase systems for an efficient processing, to reduce storage space and false positives in spatial query processing. The structure proposed in this work, partitions the space recursively into quadrants and for each quadrant, an MBR is created to provide secondary indexes that are stored in the HBase table. ...
... With k initial arbitrarily centroid set, the k-means algorithm finds the locally optimal solutions by gradually minimizing the clustering error calculated according to numerical attributes. While the technique has been applied in several disciplines, (Thiprungsri and Vasarhelyi (2011); Sfyridis and Agnolucci (2020); Jang et al. (2019)), there is less related work in life insurance. Devale and Kulkarni (2012) suggests the use of k-means to identify population segments to increase customer base. ...
... It employs 3D modeling technology to present geospatial phenomena in a realistic manner, capturing both planar updates and vertical relationships between spatial objects. Additionally, 3D GIS offers unique functionalities for spatial browsing and analysis 24 . The core component of 3D GIS is the 3D spatial database, which requires careful consideration of factors such as aesthetics, shape, vegetation, building characteristics, and water resources during data design. ...
... In many previous studies, one important assumption for team formation is that the capabilities of the team members are known in advance [4][5][6]. In practice, the agents may be unaware of the types or capabilities of their potential partners [7], and they compete with another team about which we know nothing. ...
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... A new architecture is proposed and called Adaptive Mobile Resource Offloading (AMRO) [6]. This architecture did not use a cloud server to execute the tasks from the mobile devices rather the tasks are executed on other mobile devices. ...
