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標題: Titlebook: Business Intelligence; Second European Summ Marie-Aude Aufaure,Esteban Zimányi Textbook 2013 Springer-Verlag GmbH Germany, part of Springer [打印本頁]

作者: purulent    時間: 2025-3-21 16:36
書目名稱Business Intelligence影響因子(影響力)




書目名稱Business Intelligence影響因子(影響力)學科排名




書目名稱Business Intelligence網(wǎng)絡(luò)公開度




書目名稱Business Intelligence網(wǎng)絡(luò)公開度學科排名




書目名稱Business Intelligence被引頻次




書目名稱Business Intelligence被引頻次學科排名




書目名稱Business Intelligence年度引用




書目名稱Business Intelligence年度引用學科排名




書目名稱Business Intelligence讀者反饋




書目名稱Business Intelligence讀者反饋學科排名





作者: giggle    時間: 2025-3-21 23:59

作者: 語源學    時間: 2025-3-22 03:57

作者: 情感脆弱    時間: 2025-3-22 06:57
Textbook 2013only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., machine learning, logic networks, graph mining, business semantics, large-scale data management and analysis, and multicriteria and collaborative
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作者: 他去就結(jié)束    時間: 2025-3-23 00:01
978-3-642-36317-7Springer-Verlag GmbH Germany, part of Springer Nature 2013
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作者: 一大群    時間: 2025-3-23 14:53
https://doi.org/10.1007/978-1-4612-2286-6tandard data models and system implementations (OLAP) for multidimensional databases are sometimes not able to capture the complexities of advanced real-world application domains. This lecture will focus on how to manage such complex multidimensional data, including complex dimension hierarchies, co
作者: Facet-Joints    時間: 2025-3-23 20:42

作者: Palpate    時間: 2025-3-24 00:09
Sandra Jeppesen,Paola Sartorettoormal way and examined for their significance in the broader context of statistical relational learning approaches in general and Bayesian logic networks in particular. A sandbox example is discussed in order to explain in detail the meanings of input theories with weighted clauses for a MLN. Then,
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作者: Intersect    時間: 2025-3-25 00:35
Managing Complex Multidimensional Data,mplex measures, and integration of multidimensional data with complex external data. We will look at how complex multidimensional data emerge in complex application domains such as medical data, location-based services, music data, web data, and text data, and present solutions for these domains that support multidimensional business intelligence.
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作者: Arboreal    時間: 2025-3-25 15:02
An Introduction to Multicriteria Decision Aid: The PROMETHEE and GAIA Methods,ain in detail the PROMETHEE and GAIA methods. Finally, an illustrative example will be analyzed with the D-Sight software. This will highlight the added value of using interactive and visual tools in complex decision processes.
作者: 外露    時間: 2025-3-25 17:19
Yuliya Yurchuk,Liudmila Voronovanherent parallelism as well as a different programming model compared to the classical von Neumann CPUs. Such hardware architectures offer the processing capability to distribute the workload among the CPU and other processors, and enable systems to process bigger workloads.
作者: 有機體    時間: 2025-3-25 23:30
Big Data Analytics on Modern Hardware Architectures: A Technology Survey,nherent parallelism as well as a different programming model compared to the classical von Neumann CPUs. Such hardware architectures offer the processing capability to distribute the workload among the CPU and other processors, and enable systems to process bigger workloads.
作者: FILTH    時間: 2025-3-26 03:01
https://doi.org/10.1007/978-1-4612-2286-6mplex measures, and integration of multidimensional data with complex external data. We will look at how complex multidimensional data emerge in complex application domains such as medical data, location-based services, music data, web data, and text data, and present solutions for these domains that support multidimensional business intelligence.
作者: hemophilia    時間: 2025-3-26 05:42
Hybrid Optical and Wireless Access,ng business processes using current information technologies and standards, such as Business Process Modeling Notation (BPMN) and Business Process Execution Language (BPEL). We also address the novel, yet growing, topic of Business Process Mining, and point out to open research challenges in the area.
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作者: 疲勞    時間: 2025-3-26 23:13
Sandra Jeppesen,Paola Sartorettoness processes. We will present the state of the art of ontology learning approaches from textual data and web environment and their integration in enterprise systems to perform personalized and incremental knowledge harvesting.
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作者: FLIP    時間: 2025-3-27 15:45
Knowledge Discovery from Constrained Relational Data: A Tutorial on Markov Logic Networks,ormal way and examined for their significance in the broader context of statistical relational learning approaches in general and Bayesian logic networks in particular. A sandbox example is discussed in order to explain in detail the meanings of input theories with weighted clauses for a MLN. Then,
作者: 浮夸    時間: 2025-3-27 17:46
Big Data Analytics on Modern Hardware Architectures: A Technology Survey, to handle these workloads is to distribute the processing utilizing massive parallel analysis systems or to use big machines able to handle the workload. We discuss massively parallel analysis systems and their programming models. Furthermore, we discuss the application of modern hardware architect
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作者: 有幫助    時間: 2025-3-28 05:27

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