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Titlebook: Enterprise Information Systems; 22nd International C Joaquim Filipe,Michał Śmiałek,Slimane Hammoudi Conference proceedings 2021 Springer Na

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书目名称Enterprise Information Systems
副标题22nd International C
编辑Joaquim Filipe,Michał Śmiałek,Slimane Hammoudi
视频video
丛书名称Lecture Notes in Business Information Processing
图书封面Titlebook: Enterprise Information Systems; 22nd International C Joaquim Filipe,Michał Śmiałek,Slimane Hammoudi Conference proceedings 2021 Springer Na
描述This book constitutes extended, revised and selected papers from the 22nd International Conference on Enterprise Information Systems, ICEIS 2020, held online during May 5-7, 2020..The 41 papers presented in this volume were carefully reviewed and selected for inclusion in this book from a total of 255 submissions. They were organized in topical sections as follows: database and information systems integration; artificial intelligence and decision support systems; information systems analysis and specification; software agents and internet computing; human-computer interaction; and enterprise architecture..
出版日期Conference proceedings 2021
关键词big data; data science; enterprise architecture; information integration; information systems; software d
版次1
doihttps://doi.org/10.1007/978-3-030-75418-1
isbn_softcover978-3-030-75417-4
isbn_ebook978-3-030-75418-1Series ISSN 1865-1348 Series E-ISSN 1865-1356
issn_series 1865-1348
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

书目名称Enterprise Information Systems影响因子(影响力)




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书目名称Enterprise Information Systems网络公开度




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书目名称Enterprise Information Systems读者反馈学科排名




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A Machine Learning Based Framework for Enterprise Document Classification classification rules behind the labelling process are informal and tend to change that complicates the labelling even more. We propose a machine learning based document classification framework (Framework) that allows for continuous retraining of the classification bots, for easy analysis of the bo
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Improving Corporate Support by Predicting Customer e-Mail Response Time: Experimental Evaluation anda reply at acceptable levels..By utilizing learning models based on the Random Forest Algorithm, the extent to which it is possible to predict e-Mail time-to-respond is investigated. This is investigated both for customers, but also for customer support agents. The former focusing on how long until
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An Investigation of Problem Instance Difficulty for Case-Based Reasoning and Heuristic Searcheady existing HSIs. This reuse should be automated, and we (meta-)modeled the HSI domain for .. These models together with additionally defined transformation rules that lead from a model of one specific HSI to another one facilitate automatic adaptations of HSI instances in these models and, hence,
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