Selected work

Representative Papers

Rethinking Accuracy: A Weighted Error-Based Metric for Data Quality

Jul 24, 2026

This work addresses the lack of a universal, tunable, and multi-scenario-compatible metric for data quality assessment, which hinders effective comparison of diverse data cleaning pipelines. To overcome this limitation, the authors propose TOMME—a general-purpose data quality measurement framework based on weighted errors—that extends traditional accuracy into a configurable, composite score. By producing a single quantitative metric, TOMME enables flexible adjustment of error weights according to specific use cases, thereby supporting both automated processing and optimization requirements. Experimental results demonstrate that TOMME exhibits strong adaptability, practicality, and comparability across a variety of scenarios, offering an efficient and unified solution for data quality evaluation and decision-making.

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Extending GouDa: Generation of Universal Datasets with (and without) Errors for Data Quality Benchmarking

Jul 22, 2026

This work addresses the challenge that existing synthetic data generation methods struggle to simultaneously ensure realism, support multiple data formats, enable controlled error injection, and produce corresponding error-free ground-truth data. To this end, we propose GouDa, a novel data generator that unifies support for both relational and non-relational data formats within an extensible framework. GouDa leverages customizable attribute distributions and structured error models to flexibly generate high-fidelity synthetic datasets. Its key innovation lies in the synchronized production of data instances containing controllable errors alongside their error-free counterparts, significantly enhancing the utility of synthetic data in edge-case scenarios and providing robust support for data quality evaluation and machine learning tasks.

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LUDB++: Enabling LUDB for the Analysis of Shaped Feedforward FIFO Networks using Network Calculus

May 09, 2026

Existing LUDB methods cannot analyze delay in feed-forward FIFO networks incorporating traffic shapers. This work addresses this limitation by integrating traffic shaping into the LUDB framework for the first time within Network Calculus, proposing an enhanced method termed LUDB++. The new approach supports modeling of shapers both at network endpoints and internal nodes, significantly improving the tightness of delay bounds while retaining computational efficiency. Experimental evaluation across 130 linear and tree topologies demonstrates that LUDB++ consistently yields tighter delay upper bounds than the original LUDB, and outperforms the current state-of-the-art ELP method in most scenarios, achieving a maximum improvement of 9.13%.

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What induces plane structures in complete graph drawings?

Mar 05, 2026

This study investigates the conditions under which drawing all edges of a complete graph in the plane with curves necessarily yields a large set of pairwise non-crossing curves—that is, a planar substructure—under specific crossing rules. By integrating combinatorial geometry and graph-theoretic techniques with crossing analysis and constructive proofs, the work provides the first systematic characterization of two mild crossing constraints that inevitably force the emergence of numerous disjoint curves. Moreover, it constructs an explicit drawing scheme in which every pair of curves intersects, while simultaneously satisfying tight upper and lower bounds on the total number of crossings. These results uncover a profound connection between local crossing rules and global planar structure, offering new structural insights and constructive tools for graph drawing theory.

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Generative AI Usage of University Students: Navigating Between Education and Business

Feb 18, 2026

This study addresses the lack of systematic understanding regarding how university students navigating dual academic and professional identities employ generative artificial intelligence (GenAI) in the intersecting contexts of education and work. Drawing on grounded theory, the research constructs the first integrated theoretical model of GenAI use through semi-structured in-depth interviews with 11 distance-learning students. The analysis identifies three core causal conditions and four intervening factors that shape distinct usage strategies. Findings reveal that while GenAI enhances both learning and work efficiency, it simultaneously introduces critical challenges concerning reliability, academic integrity, and ethical risks. These insights offer valuable theoretical and practical implications for human-AI collaboration across boundary-spanning scenarios.

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Recent publications

Latest Papers

Rethinking Accuracy: A Weighted Error-Based Metric for Data Quality

Jul 24, 2026

This work addresses the lack of a universal, tunable, and multi-scenario-compatible metric for data quality assessment, which hinders effective comparison of diverse data cleaning pipelines. To overcome this limitation, the authors propose TOMME—a general-purpose data quality measurement framework based on weighted errors—that extends traditional accuracy into a configurable, composite score. By producing a single quantitative metric, TOMME enables flexible adjustment of error weights according to specific use cases, thereby supporting both automated processing and optimization requirements. Experimental results demonstrate that TOMME exhibits strong adaptability, practicality, and comparability across a variety of scenarios, offering an efficient and unified solution for data quality evaluation and decision-making.

0 citationsRead paper

Extending GouDa: Generation of Universal Datasets with (and without) Errors for Data Quality Benchmarking

Jul 22, 2026

This work addresses the challenge that existing synthetic data generation methods struggle to simultaneously ensure realism, support multiple data formats, enable controlled error injection, and produce corresponding error-free ground-truth data. To this end, we propose GouDa, a novel data generator that unifies support for both relational and non-relational data formats within an extensible framework. GouDa leverages customizable attribute distributions and structured error models to flexibly generate high-fidelity synthetic datasets. Its key innovation lies in the synchronized production of data instances containing controllable errors alongside their error-free counterparts, significantly enhancing the utility of synthetic data in edge-case scenarios and providing robust support for data quality evaluation and machine learning tasks.

0 citationsRead paper

LUDB++: Enabling LUDB for the Analysis of Shaped Feedforward FIFO Networks using Network Calculus

May 09, 2026

Existing LUDB methods cannot analyze delay in feed-forward FIFO networks incorporating traffic shapers. This work addresses this limitation by integrating traffic shaping into the LUDB framework for the first time within Network Calculus, proposing an enhanced method termed LUDB++. The new approach supports modeling of shapers both at network endpoints and internal nodes, significantly improving the tightness of delay bounds while retaining computational efficiency. Experimental evaluation across 130 linear and tree topologies demonstrates that LUDB++ consistently yields tighter delay upper bounds than the original LUDB, and outperforms the current state-of-the-art ELP method in most scenarios, achieving a maximum improvement of 9.13%.

0 citationsRead paper

What induces plane structures in complete graph drawings?

Mar 05, 2026

This study investigates the conditions under which drawing all edges of a complete graph in the plane with curves necessarily yields a large set of pairwise non-crossing curves—that is, a planar substructure—under specific crossing rules. By integrating combinatorial geometry and graph-theoretic techniques with crossing analysis and constructive proofs, the work provides the first systematic characterization of two mild crossing constraints that inevitably force the emergence of numerous disjoint curves. Moreover, it constructs an explicit drawing scheme in which every pair of curves intersects, while simultaneously satisfying tight upper and lower bounds on the total number of crossings. These results uncover a profound connection between local crossing rules and global planar structure, offering new structural insights and constructive tools for graph drawing theory.

0 citationsRead paper

Generative AI Usage of University Students: Navigating Between Education and Business

Feb 18, 2026

This study addresses the lack of systematic understanding regarding how university students navigating dual academic and professional identities employ generative artificial intelligence (GenAI) in the intersecting contexts of education and work. Drawing on grounded theory, the research constructs the first integrated theoretical model of GenAI use through semi-structured in-depth interviews with 11 distance-learning students. The analysis identifies three core causal conditions and four intervening factors that shape distinct usage strategies. Findings reveal that while GenAI enhances both learning and work efficiency, it simultaneously introduces critical challenges concerning reliability, academic integrity, and ethical risks. These insights offer valuable theoretical and practical implications for human-AI collaboration across boundary-spanning scenarios.

0 citationsRead paper