Titlebook: Big Data Analytics and Knowledge Discovery; 25th International C Robert Wrembel,Johann Gamper,Ismail Khalil Conference proceedings 2023 The

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Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Recordndividual or group level. We conduct quantitative experiments and sensitivity studies on the real-world clinical PBC dataset. The results demonstrate that the proposed fairness notations and debiasing algorithm are capable of guaranteeing fairness in the presence of accurate prediction.

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DAT@Z21: A Comprehensive Multimodal Dataset for Rumor Classification in MicroblogsWe conduct exploratory analyses to understand our dataset’s characteristics and analyze useful patterns. We also experiment various state-of-the-art rumor classification methods to illustrate DAT@Z21’s usefulness, especially its visual components. Eventually, DAT@Z21 is available online at ..

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0302-9743 took place in Penang, Malaysia, during August 29-30, 2023. ..The 18 full papers presented together with 19 short papers were carefully reviewed and selected from a total of 83 submissions..They were organized in topical sections as follows: Data quality; advanced analytics and pattern discovery; ma

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https://doi.org/10.1007/978-1-4842-5013-6ns on type definitions to transform technical errors into type errors. We show how to use this framework to prevent errors related to schema or model transformations in analytical workflows. We provide an open-source implementation in Scala which can be used to detect errors at compile time.

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Using Ontologies as Context for Data Warehouse Quality Assessmentality in Data Warehouse considering data context. In addition to presenting our general approach, in this paper we propose two particular data quality rules for accuracy dimension, using OWL domain ontologies as context. With our approach, we obtain data quality metrics that can be adapted to any application domain.

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Preventing Technical Errors in Data Lake Analyses with Type Theoryns on type definitions to transform technical errors into type errors. We show how to use this framework to prevent errors related to schema or model transformations in analytical workflows. We provide an open-source implementation in Scala which can be used to detect errors at compile time.