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Modelling Foundations and Applications

Overview of attention for book
Cover of 'Modelling Foundations and Applications'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Efficient Model Synchronization with View Triple Graph Grammars
  3. Altmetric Badge
    Chapter 2 Level-Agnostic Designation of Model Elements
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    Chapter 3 Towards Scalable Querying of Large-Scale Models
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    Chapter 4 OCLR : A More Expressive, Pattern-Based Temporal Extension of OCL
  6. Altmetric Badge
    Chapter 5 Interpretation of Linguistic Architecture
  7. Altmetric Badge
    Chapter 6 Alloy4SPV : A Formal Framework for Software Process Verification
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    Chapter 7 Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability
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    Chapter 8 Identifying and Visualising Commonality and Variability in Model Variants
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    Chapter 9 Modular DSLs for Flexible Analysis: An e-Motions Reimplementation of Palladio
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    Chapter 10 Language-Independent Traceability with Lässig
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    Chapter 11 A Family-Based Framework for i-DSML Adaptation
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    Chapter 12 Normalizing Heterogeneous Service Description Models with Generated QVT Transformations
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    Chapter 13 Towards the Systematic Construction of Domain-Specific Transformation Languages
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    Chapter 14 A MOF-Based Framework for Defining Metrics to Measure the Quality of Models
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    Chapter 15 Neo4EMF, A Scalable Persistence Layer for EMF Models
  17. Altmetric Badge
    Chapter 16 Towards an Infrastructure for Domain-Specific Languages in a Multi-domain Cloud Platform
  18. Altmetric Badge
    Chapter 17 Experiences with Business Process Model and Notation for Modeling Integration Patterns
Attention for Chapter 13: Towards the Systematic Construction of Domain-Specific Transformation Languages
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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Chapter title
Towards the Systematic Construction of Domain-Specific Transformation Languages
Chapter number 13
Book title
Modelling Foundations and Applications
Published in
Lecture notes in computer science, July 2014
DOI 10.1007/978-3-319-09195-2_13
Book ISBNs
978-3-31-909194-5, 978-3-31-909195-2
Authors

Jesús Sánchez Cuadrado, Esther Guerra, Juan de Lara

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 36%
Student > Bachelor 3 21%
Professor > Associate Professor 2 14%
Researcher 2 14%
Student > Master 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 11 79%
Unknown 3 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2014.
All research outputs
#14,655,561
of 22,758,963 outputs
Outputs from Lecture notes in computer science
#4,476
of 8,126 outputs
Outputs of similar age
#123,985
of 228,570 outputs
Outputs of similar age from Lecture notes in computer science
#96
of 210 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 228,570 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.