Relation-Centric Task Identification for Policy-Based Process Mining

Many organizations use business policies to govern their business processes. For complex business processes, this results in huge amount of policy documents. Given the large volume of policies, manually analyzing policy documents to discover process information imposes excessive cognitive load. In order to provide a solution to this problem, we have proposed previously a novel approach named Policy-based Process Mining (PBPM) to automatically extracting process models from policy documents using information extraction techniques. In this paper, we report our recent findings in an important PBPM step called task identification. Our investigation indicates that task identification from policy documents is quite challenging because it is not a typical information extraction problem. The novelty of our approach is to formalize task identification as a problem of extracting relations among three process components, i.e., resource, action, and data while using sequence kernel techniques. Our initial experiment produced very promising results

User data discovery and aggregation: the CS-UDD algorithm

In the social web, people use social systems for sharing content and opinions, for communicating with friends, for tagging, etc. People usually have different accounts and different profiles on all of these systems. Several tools for user data aggregation and people search have been developed and protocols and standards for data portability have been defined. This paper presents an approach and an algorithm, named Cross-System User Data Discovery (CS-UDD), to retrieve and aggregate user data distributed on social websites. It is designed to crawl websites, retrieve profiles that may belong to the searched user, correlate them, aggregate the discovered data and return them to the searcher which may, for example, be an adaptive system. The user attributes retrieved, namely attribute-value pairs, are associated with a certainty factor that expresses the confidence that they are true for the searched user. To test the algorithm, we ran it on two popular social networks, MySpace and Flickr. The evaluation has demonstrated the ability of the CS-UDD algorithm to discover unknown user attributes and has revealed high precision of the discovered attributes

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive

根付き外平面点彩色グラフに対する列挙アルゴリズム

京都大学0048新制・課程博士博士(情報学)甲第15534号情博第392号新制||情||72(附属図書館)28012京都大学大学院情報学研究科数理工学専攻(主査)教授 永持 仁, 教授 福嶋 雅夫, 教授 太田 快人学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

Faculty Use of Active Learning in Postgraduate Nephrology Education: A Mixed-Methods Study

Background: Active learning is an effective instructional tool in medical education. However, its integration by nephrology faculty remains limited despite residents' declining interest in nephrology. Study Design: A sequential explanatory mixedmethods study design was used to explore nephrology faculty understanding of difficult teaching topics and active learning integration using the theory of planned behavior as theoretical framework. Setting & Participants: Nephrology faculty at 6 residency sites in Singapore were recruited. Methodology: A 28-item questionnaire was administered to conveniently sampled faculty followed by 1-to-1 semi-structured interviews of a purposively sampled subset. Analytical Approach: Quantitative data were analyzed using descriptive and regression statistics. Qualitative data were analyzed using thematic analysis in line with the theory of planned behavior constructs (attitude, subjective norm, perceived behavioral control, intention, and behavior). Results: 49 of 82 invited faculty responded, with 49% and 42% perceiving self-directed learning and interactive lectures, respectively, as active learning formats. Fluid, electrolyte, and acid-base disturbances; transplantation immunology; glomerulonephritis; and hemodialysis adequacy were cited as difficult topics by 75%, 63%, 45%, and 31% of responders, respectively. Only 55% reported integrating active learning formats when teaching difficult topics. Faculty in leadership roles and teaching difficult topics more regularly were more likely to adopt active learning formats. Multivariable logistic regression analysis showed that faculty attitude strongly and significantly predicted active learning intention. Thematic analysis identified 4 themes: active learning competence, barriers and challenges, environmental influence, and self-identity. Self-identity, defined as values developed from past behavior and experience, emerged as an important contributor to active learning adoption outside the theory of planned behavior framework. Limitations: Sampling, context, and measurement biases may affect study reliability and generalizability. Conclusions: Nephrology faculty lack active learning competence and face cognitive challenges when teaching difficult topics. Faculty teaching experience significantly influenced active learning adoption. Our findings build on the theoretical understanding of faculty instructional innovation adoption and can inform nephrology faculty development initiatives

Complex Problem Solving: Identity Matching Based on Social Contextual Information *

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