Browsing by Author "Wu, I-Chin"

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An exploratory study of incorporating task-stage identification into a long-term document support process
(2006)
Wu, I-Chin
;
Chang, Pei-Cheng
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
With the increasing amount of information (and knowledge) in organizational memories and the phenomenal growth of the World Wide Web, knowledge workers are increasingly suffering from information overload Because of the growing complexity of knowledge-intensive tasks, a profiling technique is needed to deliver task-relevant documents to workers. In this study, we propose a task-stage identification technique that provides effective document support throughout the execution of a task. A pattern similarity analysis method based on the correlation value is used to identify a worker\'s task-stage. The results are incorporated into a profile adaptation process to generate the worker\'s current task profile. We conducted a pilot study in a research institute. The results confirm the effectiveness of the proposed document support process in a knowledge-intensive working environment.
Collaborative relevance assessment for task-based knowledge support
(2008-01-01)
Liu, Duen-Ren
;
Wu, I-Chin
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
The operations and management activities of enterprises are mainly task-based and knowledge intensive. Accordingly, an important issue in deploying knowledge management systems is the provision of task-relevant information (codified knowledge) to meet the information needs of knowledge workers during the execution of a task. Codified knowledge extracted from previously executed tasks can provide valuable knowledge about conducting the task-at-hand (current task), and is a valuable information source for constructing a task profile that models a worker's task needs, i.e., information needs for the current task. In this paper, we propose a novel task-relevance assessment approach that evaluates the relevance of previous tasks in order to construct a task profile for the current task. The approach helps knowledge workers assess the relevance of previous tasks through linguistic evaluation and the collaboration of knowledge workers. In addition, applying relevance assessment to a large number of tasks may create an excessive burden for workers. Thus, we propose a novel two-phase relevance assessment method to help workers conduct relevance assessment effectively. Furthermore, a modified relevance feedback technique, which is integrated with the task-relevance assessment method, is employed to derive the task profile for the task-at-hand. Consequently, task-based knowledge support can be enabled to provide knowledge workers with task-relevant information based on task profiles. Empirical experiments demonstrate that the proposed approach models workers' task-needs effectively and helps provide task-relevant knowledge. (C) 2007 Elsevier B.V. All rights reserved.
Hepatocellular carcinoma-associated single-nucleotide variants and deletions identified by the use of genome-wide high-throughput analysis of hepatitis B virus
(2017-10-01)
Liu, Wen-Chun
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Wu, I-Chin
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Lee, Yen-Chien
;
Lin, Chih-Peng
;
Cheng, Ji-Hong
This study investigated hepatitis B virus (HBV) single-nucleotide variants (SNVs) and deletion mutations linked with hepatocellular carcinoma (HCC). Ninety-three HCC patients and 108 non-HCC patients were enrolled for HBV genome-wide next-generation sequencing (NGS) analysis. A systematic literature review and a meta-analysis were performed to validate NGS-defined HCC-associated SNVs and deletions. The experimental results identified 60 NGS-defined HCC-associated SNVs, including 41 novel SNVs, and their pathogenic frequencies. Each SNV was specific for either genotype B (n = 24) or genotype C (n = 34), except for nt53C, which was present in both genotypes. The pathogenic frequencies of these HCC-associated SNVs showed a distinct U-shaped distribution pattern. According to the meta-analysis and literature review, 167 HBV variants from 109 publications were categorized into four levels (A-D) of supporting evidence that they are associated with HCC. The proportion of NGS-defined HCC-associated SNVs among these HBV variants declined significantly from 75% of 12 HCC-associated variants by meta-analysis (Level A) to 0% of 10 HCC-unassociated variants by meta-analysis (Level D) (P < 0.0001). PreS deletions were significantly associated with HCC, in terms of deletion index, for both genotypes B (P = 0.030) and C (P = 0.049). For genotype C, preS deletions involving a specific fragment (nt2977-3013) were significantly associated with HCC (HCC versus non-HCC, 6/34 versus 0/32, P = 0.025). Meta-analysis of preS deletions showed significant association with HCC (summary odds ratio 3.0; 95% confidence interval 2.3-3.9). Transfection of Huh7 cells showed that all of the five novel NGS-defined HCC-associated SNVs in the small surface region influenced hepatocarcinogenesis pathways, including endoplasmic reticulum-stress and DNA repair systems, as shown by microarray, real-time polymerase chain reaction and western blot analysis. Their carcinogenic mechanisms are worthy of further research. Copyright (c) 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
Learning Dynamic Information Needs: A Collaborative Topic Variation Inspection Approach
(2009-12-01)
Wu, I-Chin
;
Liu, Duen-Ren
;
Chang, Pei-Cheng
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
For projects in knowledge-intensive domains, it is crucially important that knowledge management systems are able to track and infer workers' up-to-date information needs so that task-relevant information can be delivered in a timely manner. To put a worker's dynamic information needs into perspective, we propose a topic variation inspection model to facilitate the application of an implicit relevance feedback (IRF) algorithm and collaborative filtering in user modeling. The model analyzes variations in a worker's task-needs for a topic (i.e., personal topic needs) over time, monitors changes in the topics of collaborative actors, and then adjusts the worker's profile accordingly. We conducted a number of experiments to evaluate the efficacy of the model in terms of precision, recall, and F-measure. The results suggest that the proposed collaborative topic variation inspection approach can substantially improve the performance of a basic profiling method adapted from the classical RF algorithm. It can also improve the accuracy of other methods when a worker's information needs are vague or evolving, i.e., when there is a high degree of variation in the worker's topic-needs. Our findings have implications for the design of an effective collaborative information filtering and retrieval model, which is crucial for reusing an organization's knowledge assets effectively.
Measuring the variation in task-needs for knowledge delivery: A profiling via collaboration technique
(2007)
Liu, Duen-Ren
;
Wu, I-Chin
;
Chang, Pei-Cheng
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Effective knowledge. management (KM) in a knowledge-intensive working environment requires an understanding of workers' information needs for tasks, (task-needs), so that they can be provided with appropriate codified knowledge (textual documents) when performing long-term tasks. This work proposes a novel profiling technique based on implicit relevance feedback and collaborative filtering techniques that model workers' task-needs. The proposed profiling method analyses variations in workers' task-needs for topics (i.e., topic needs) in a topic taxonomy over time. Variations in the topic needs of similar workers' are used to predict variations in a target worker's topic needs and adjust his/her task profile accordingly. Experiment results suggest that considering variations In the topic needs of similar workers' during the profile adaptation process Is effective in improving the precision of document retrieval.
Mining and supporting task-stage knowledge: A hierarchical clustering technique
(2006)
Liu, Duen-Ren
;
Wu, I-Chin
;
Chen, Wei-Hsiao
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In task-based business environments, organizations usually conduct knowledge-intensive tasks to achieve organizational goals; thus, knowledge management systems (KMSs) need to provide relevant information to fulfill the information needs of knowledge workers. Since knowledge workers usually accomplish a task in stages, their task-needs may be different at various stages of the task's execution. Thus, an important issue is how to extract knowledge from historical tasks and further support task-relevant knowledge according to the workers' task-needs at different task-stages. This work proposes a task-stage mining technique for discovering task-stage needs from historical (previously executed) tasks. The proposed method uses information retrieval techniques and a modified hierarchical agglomerative clustering algorithm to identify task-stage needs by analyzing codified knowledge (documents) accessed or generated during the task's performance. Task-stage profiles are generated to model workers' task-stage needs and used to deliver task-relevant knowledge at various task-stages. Finally, we conduct empirical evaluations to demonstrate that the proposed method provides a basis for effective knowledge support.
Toward incorporating a task-stage identification technique into the long-term document support process
(2008-09-01)
Wu, I-Chin
;
Liu, Duen-Ren
;
Chang, Pei-Cheng
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Effective knowledge management in a knowledge-intensive environment can place heavy demands on the information filtering (IF) strategies used to model workers' long-term task-needs. Because of the growing complexity of knowledge-intensive work tasks, a profiling technique is needed to deliver task-relevant documents to workers. In this study, we propose an IF technique with task-stage identification that provides effective codification-based support throughout the execution of a task. Task-needs pattern similarity analysis based on a correlation value is used to identify a worker's task-stage (the pre-focus, focus formulation, or post-focus task-stage). The identified task-stage is then incorporated into a profile adaptation process to generate the worker's current task profile. The results of a pilot study conducted in a research institute confirm that there is a low or negative correlation between search sessions and transactions in the pre-focus task-stage, whereas there is at least a moderate correlation between search sessions/transactions in the post-focus stage. Compared with the traditional IF technique, the proposed IF technique with task-stage identification achieves, on average, a 19.49%, improvement in task-relevant document support. The results confirm the effectiveness of the proposed method for knowledge-intensive work tasks. (c) 2007 Elsevier Ltd. All rights reserved.