• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.101-108
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• 2019

Workflow management systems (WfMSs) automate and manage workflows, which are implementations of organizational processes operated in process-centric organizations. In this paper, wepropose an algorithm to discover temporal work transference networks from workflow execution logs. The temporal work transference network is a special type of enterprise social networks that consists of workflow performers, and relationships among them that are formed by work transferences between performers who are responsible in performing precedent and succeeding activities in a workflow process. In terms of analysis, the temporal work transference network is an analytical property that has significant value to be analyzed to discover organizational knowledge for human resource management and related decision-making steps for process-centric organizations. Also, the beginning point of implementinga human-centered workflow intelligence framework dealing with work transference networks is to develop an algorithm for discovering temporal work transference cases on workflow execution logs. To this end, we first formalize a concept of temporal work transference network, and next, we present a discovery algorithm which is for the construction of temporal work transference network from workflow execution logs. Then, as a verification of the proposed algorithm, we apply the algorithm to an XES-formatted log dataset that was released by the process mining research group and finally summarize the discovery result.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3037-3054
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• 2019

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.

• Journal of the Korean Society of Visualization
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• v.21 no.2
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• pp.14-23
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• 2023

The pressure difference across stenotic blood vessels is a commonly used clinical metric for diagnosing many cardiovascular diseases. At present, most clinical pressure measurements rely solely on invasive catheterization. In this study, we propose a novel method for non-invasive pressure estimation using the incompressible Navier-Stokes equations and a 3D multi-path integration approach. We verify spatio-temporal convergence on an in-silico dataset of a cylindrical straight pipe phantom with steady and pulsatile flow fields. We then evaluate the proposed method on an in vitro dataset of reconstructed control, pre-operative, and post-operative carotid artery cases acquired from 4D flow MRI. The performance of our method is compared to existing approaches based on the pressure Poisson equation and work-energy relative pressure. The results demonstrate the proposed method's high accuracy, robustness to spatio-temporal subsampling, and reduced sensitivity to noise, highlighting its great potential for non-invasive pressure estimation.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2378-2383
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• 2007

Direct Numerical Simulation was carried out to predict mass transfer in turbulent flow around a rotating stepped cylinder. This investigation is a follow-up study of Nesic et al. [Corrosion, Vol. 56, No. 10, pp. 1005 - 1014] The original motivation of this work stemmed from the efforts to design a simple device which can generate flows of high turbulence intensity at low cost for corrosion researchers. Two cases were considered; Sc=1 and 10 both at Re=335. Here, Sc and Re stand for Schmidt number and Reynolds number, respectively, based on the step height and the surface speed of the cylinder upstream the step. Main focus was placed on the correlation between turbulent fluctuation and concentration field. The spatio-temporal evolution of concentration field is discussed. The numerical results are qualitatively compared with those of the experiment conducted with the same flow configuration.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.9
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• pp.799-806
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• 2007

Direct Numerical Simulation was carried out to predict mass transfer in turbulent flow around a rotating stepped cylinder. This investigation is a follow-up study of DNS of turbulent flow in Nesic et al. [Corrosion, Vol. 56, No. 10, pp. 1005 - 1014] The original motivation of this work stemmed from the efforts to design a simple device which can generate flows of high turbulence intensity at low cost for corrosion researchers. Two cases were considered; Sc=1 and 10 both at Re=335. Here, Sc and Re stand for Schmidt number and Reynolds number, respectively, based on the step height and the surface speed of the cylinder upstream of the step. Main focus was placed on the correlation between turbulence and mass transfer. The spatio-temporal evolution of concentration field is discussed. The numerical results are qualitatively compared with those of the experiment conducted with a similar flow configuration.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.15-23
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• 2022

Participants in real-time online sessions, be it (business) meetings, virtual school lessons, or social live streams, all engage in cyber social interactions. Unlike parasocial interactions, cyber social interactions are characterized by reciprocity and temporal proximity. In contrast to social interactions, they lack spatial proximity and bodily contact. This is a fairly new concept in information science that rose from technological advances and unprecedented circumstances (e.g., the rise of digital economy and knowledge workers being able to work remotely or, more recently, global lockdowns and contact restrictions). As a result, the past ways of working and socializing were transformed by making them, in some cases predominantly, virtual. Regarding the example of social live streaming we exhibit the importance of cyber social interactions for information behavior research. This conceptual article is a plea for information science to engage more in human-human online relations and interactions.

• Smart Media Journal
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• v.4 no.3
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• pp.31-37
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• 2015

Recently, computer graphics has made possible a close representation of the due diligence. But, prefer the feel of a 2D computer graphics. 2D computer graphics and compositing of 3D computer graphics have already been produced for a long time. But, the virtual 3D computer graphics application in 2D image or live-action have been used to effect expression of a 2D computer graphics. Recently, 2D computer graphics content is being expanded through the cartoons. In some cases increase the efficiency by making a 3D computer graphics on the camera angle or temporal and spatial part. Through practical work and want to present the proper direction.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.85-92
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• 2019

In this paper, we extract the quantitative relation data of activities from the workflow event log file recorded in the XES standard format and connect them to rediscover the workflow process model. Extract the workflow process patterns and proportions with the rediscovered model. There are four types of control-flow elements that should be used to extract workflow process patterns and portions with log files: linear (sequential) routing, disjunctive (selective) routing, conjunctive (parallel) routing, and iterative routing patterns. In this paper, we focus on four of the factors, disjunctive routing, and conjunctive path. A framework implemented by the authors' research group extracts and arranges the activity data from the log and converts the iteration of duplicate relationships into a quantitative value. Also, for accurate analysis, a parallel process is recorded in the log file based on execution time, and algorithms for finding and eliminating information distortion are designed and implemented. With these refined data, we rediscover the workflow process model following the relationship between the activities. This series of experiments are conducted using the Large Bank Transaction Process Model provided by 4TU and visualizes the experiment process and results.