Titlebook: Learning from Data Streams in Evolving Environments; Methods and Applicat Moamar Sayed-Mouchaweh Book 2019 Springer International Publishin

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Processing Evolving Social Networks for Change Detection Based on Centrality Measures,ther as time flies. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. Such evolving behavior leads to changes in the network topology that can be investigated under different perspectives.

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Process Mining for Analyzing Customer Relationship Management Systems: A Case Study,building models that can detect patterns and behaviors. In the meanwhile, organizational perspective is being considered in Process Mining by taking advantage of the ability to extract social networks that represent different kinds of relations between resources performing the process. The case stud

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On Social Network-Based Algorithms for Data Stream Clustering,rs for many decades. This task becomes even more problematic when data is presented as a potentially unbounded sequence, the so-called data streams. Albeit most of the research on data stream mining focuses on supervised learning, the assumption that labels are available for learning is unverifiable

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Isah Abdullahi Lawaltal data on various metals over broad ranges of strain rates, from quasi-static to 10./s and greater, and temperatures from 77 to 1,300K and greater. In this model, the role of the strain gradient is embedded in the nature of the dislocations, their density and distribution, and the manner by which

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978-3-030-07862-1Springer International Publishing AG, part of Springer Nature 2019