Titlebook: Big Data Analytics and Knowledge Discovery; 24th International C Robert Wrembel,Johann Gamper,Ismail Khalil Conference proceedings 2022 The

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Text-Based Causal Inference on Irony and Sarcasm Detectionthe statistical correlation between features which may lead to bias. Therefore, to build robust systems, causality should be considered while estimating the given task’s data generating process. In this study, we explore text-based causal inference on the irony and sarcasm detection problem. Additio

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A Fast NDFA-Based Approach to Approximate Pattern-Matching for Plagiarism Detection in Blockchain-Drt significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). After testing in real-world scenarios, we conclude that our approach is suitable and adequate to perform appro

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On Decisive Skyline Queriese in . specified criteria. However, objects whose values are good in only a subset of the given criteria are also included in the skyline set, even though they may take arbitrarily bad values in the remaining criteria. To alleviate this shortcoming, we study the decisive subspaces that express the s

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Safeness: Suffix Arrays Driven Materialized View Selection Framework for Large-Scale Workloadss have been propounded, and some are deployed in commercial DBMSs. The central role of MVs in optimizing workloads has recently prodded researchers to revisit the problems associated with them (selection, maintenance, adaptation) by proposing AI techniques to tackle those issues. When deeply analyzi

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A Process Warehouse for Process Variants Analysisn a given domain, e.g., order-to-cash or procure-to-pay process in reseller or procurement domain. These adjustments produce some variations between these process models that mainly should be identical but may differ slightly. Existing approaches related to data warehouse solutions suffer from adequ

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Pathology Data Prioritisation: A Study Using Multi-variate Time Serieson is the ever increasing quantity of pathology data that clinicians are expected to consider. The fundamental idea, given a previously unseen pathology result and the associated pathology history, is to use a deep learning model to predict future pathology results and then use the prediction to cla