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

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發(fā)表于 2025-3-21 18:55:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Big Data Analytics and Knowledge Discovery
期刊簡(jiǎn)稱24th International C
影響因子2023Robert Wrembel,Johann Gamper,Ismail Khalil
視頻videohttp://file.papertrans.cn/186/185603/185603.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Big Data Analytics and Knowledge Discovery; 24th International C Robert Wrembel,Johann Gamper,Ismail Khalil Conference proceedings 2022 The
影響因子This volume LNCS 13428 constitutes the papers of the 24 th International Conference on Big Data Analytics and Knowledge Discovery, held in August 2022 in Vienna, Austria.. The 12 full papers presented together with 12 short papers in this volume were carefully reviewed and selected from a total of 57 submissions.. The papers reflect a wide range of topics in the field of data integration, data warehousing, data analytics, and recently big data analytics, in a broad sense. The main objectives of this event are to explore, disseminate, and exchange knowledge in these fields..
Pindex Conference proceedings 2022
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Forschungen aus Staat und Rechtation of QJO, thanks to suffix arrays initially introduced for string processing, enabling efficient algorithms for data compression, repeat finding, etc. Firstly, we show the flexibility of suffix arrays in coding analytical queries, capturing shareable subexpressions, and incorporating QJO. Second
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https://doi.org/10.1007/978-3-662-25528-5 processes as a generic data model to capture and consolidate process variants into a reference process model; (ii) a process warehouse model to perform typical online analytical processing operations on different variation parts thus providing support to decision-making through KPIs; The framework
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https://doi.org/10.1007/978-3-7091-0597-9tance scores include a notion of redundancy awareness making them a tool to achieve redundancy-free feature selection. We show that the deriving features’ selection outperforms competing methods in lowering the redundancy rate while maximizing the information contained in the data. We also introduce
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Chaoyang Zhang,Jing Huang,Rupeng Buposition way and multi-output regression (MOR), respectively. To the best of our knowledge, such two algorithms are the first proposed embedded-type OSFS technique for multi-label streaming features so far. Our extensive experiments conducted on six benchmark data sets demonstrate that our two propo
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Chaoyang Zhang,Jing Huang,Rupeng Bu set of relevant features with no protected features and with the least possible redundancy under prediction quality constraint. This constraint consists of a trade-off between fairness and prediction performance. Our experiments on well-known biased datasets from the literature demonstrated that ou
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Safeness: Suffix Arrays Driven Materialized View Selection Framework for?Large-Scale Workloadsation of QJO, thanks to suffix arrays initially introduced for string processing, enabling efficient algorithms for data compression, repeat finding, etc. Firstly, we show the flexibility of suffix arrays in coding analytical queries, capturing shareable subexpressions, and incorporating QJO. Second
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Unsupervised Features Ranking via?Coalitional Game Theory for?Categorical Datatance scores include a notion of redundancy awareness making them a tool to achieve redundancy-free feature selection. We show that the deriving features’ selection outperforms competing methods in lowering the redundancy rate while maximizing the information contained in the data. We also introduce
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