The Knowledge Stable Analysis Pattern

2014 ◽  
pp. 136-161
Keyword(s):  
Analysis Pattern

scholarly journals On the Integrated Descriptions of Metaphorical Sememes of Verbs

2021 ◽  
Vol 5 (1) ◽  
pp. 62-72
Author(s):  
Yuhai Peng
Keyword(s):  
Semantic Interpretation ◽  
Semantic Features ◽  
Semantic Change ◽  
Cognitive Metaphor ◽  
Analysis Pattern ◽  
Verbal Metaphor ◽  
Syntactic Semantics ◽  
Micro Level ◽  
Analysis System ◽  
Formal Feature

Cognitive metaphor is an important way and mechanism of semantic derivation of verbs, the resulting large number of verbal metaphorical sememes has different expressions in their semantic features, formal syntax, communicative structure and even modality and pragmatics. This paper thoroughly and carefully discusses the problem of semantic change of verbal metaphor in terms of the integrated description method of the Moscow Semantic School. Thus, we will create a unique and innovative framework and theoretical model of generalized formal feature analysis, we will also try to introduce formal semantic description and micro-level semantic interpretation into the analysis system of semantic change of the verbal metaphor. Furthermore, we will describe in detail and characterize a number of changes caused by metaphorical semantic change of Russian verbs from the perspective of multi-dimensional integration. This study breaks down the barriers between semantics, cognition, pragmatics and grammar and the traditional analysis pattern of semantic derivation which contributes to significant broadening of the theoretical semantic vision and deepening of the research of cognitive semantics problems of verbal metaphor. It also helps explore innovative analytical methods and strategies for the study of Russian lexical and syntactic semantics.

A Structured Method for Security Requirements Elicitation concerning the Cloud Computing Domain

2015 ◽  
pp. 875-896
Author(s):  
Kristian Beckers ◽  
Isabelle Côté ◽  
Ludger Goeke ◽  
Selim Güler ◽  
Maritta Heisel
Keyword(s):  
Cloud Computing ◽  
System Analysis ◽  
Business Case ◽  
Security Requirements ◽  
Cloud System ◽  
Security Requirement ◽  
Security Measures ◽  
Analysis Pattern ◽  
It Resources ◽  
Requirements Patterns

Cloud computing systems offer an attractive alternative to traditional IT-systems, because of economic benefits that arise from the cloud's scalable and flexible IT-resources. The benefits are of particular interest for SME's. The reason is that using Cloud Resources allows an SME to focus on its core business rather than on IT-resources. However, numerous concerns about the security of cloud computing services exist. Potential cloud customers have to be confident that the cloud services they acquire are secure for them to use. Therefore, they have to have a clear set of security requirements covering their security needs. Eliciting these requirements is a difficult task, because of the amount of stakeholders and technical components to consider in a cloud environment. Therefore, the authors propose a structured, pattern-based method supporting eliciting security requirements and selecting security measures. The method guides potential cloud customers to model the application of their business case in a cloud computing context using a pattern-based approach. Thus, a potential cloud customer can instantiate our so-called Cloud System Analysis Pattern. Then, the information of the instantiated pattern can be used to fill-out our textual security requirements patterns and individual defined security requirement patterns, as well. The presented method is tool-supported. Our tool supports the instantiation of the cloud system analysis pattern and automatically transfers the information from the instance to the security requirements patterns. In addition, they have validation conditions that check e.g., if a security requirement refers to at least one element in the cloud. The authors illustrate their method using an online-banking system as running example.

scholarly journals A Data Protection Framework for Learning Analytics

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Andrew Nicholas Cormack
Keyword(s):  
Data Protection ◽  
Learning Analytics ◽  
Personal Data ◽  
Informed Choice ◽  
Ethical Framework ◽  
Early Interventions ◽  
Sensitive Data ◽  
Analysis Pattern ◽  
Open Questions ◽  
Automated Processing

Most studies on the use of digital student data adopt an ethical framework derived from human-studies research, based on the informed consent of the experimental subject. However consent gives universities little guidance on the use of learning analytics as a routine part of educational provision: which purposes are legitimate and which analyses involve an unacceptable risk of harm. Obtaining consent when students join a course will not give them meaningful control over their personal data three or more years later. Relying on consent may exclude those most likely to benefit from early interventions. This paper proposes an alternative framework based on European Data Protection law. Separating the processes of analysis (pattern-finding) and intervention (pattern-matching) gives students and staff continuing protection from inadvertent harm during data analysis; students have a fully informed choice whether or not to accept individual interventions; organisations obtain clear guidance: how to conduct analysis, which analyses should not proceed, and when and how interventions should be offered. The framework provides formal support for practices that are already being adopted and helps with several open questions in learning analytics, including its application to small groups and alumni, automated processing and privacy-sensitive data.

scholarly journals A quantitative analysis pattern for regional risk conduction

2019 ◽  
Vol 1419 ◽  
pp. 012032
Author(s):  
Jie Su ◽  
Xinyue Zhang ◽  
Ling Zhou ◽  
Yue Yin
Keyword(s):  
Quantitative Analysis ◽  
Analysis Pattern ◽  
Regional Risk
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