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Objects, Components, Models and Patterns

Overview of attention for book
Cover of 'Objects, Components, Models and Patterns'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 On Realizing a Framework for Self-tuning Mappings
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    Chapter 2 Programming Models for Concurrency and Real-Time
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    Chapter 3 CIF: A Framework for Managing Integrity in Aspect-Oriented Composition
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    Chapter 4 A Diagrammatic Formalisation of MOF-Based Modelling Languages
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    Chapter 5 Designing Design Constraints in the UML Using Join Point Designation Diagrams
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    Chapter 6 Stream-Based Dynamic Compilation for Object-Oriented Languages
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    Chapter 7 Algebraic Semantics of OCL-Constrained Metamodel Specifications
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    Chapter 8 Specifying and Composing Concerns Expressed in Domain-Specific Modeling Languages
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    Chapter 9 Early Crosscutting Metrics as Predictors of Software Instability
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    Chapter 10 Extensibility in Model-Based Business Process Engines
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    Chapter 11 Guaranteeing Syntactic Correctness for All Product Line Variants: A Language-Independent Approach
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    Chapter 12 A Sound and Complete Program Logic for Eiffel
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    Chapter 13 A Coding Framework for Functional Adaptation of Coarse-Grained Components in Extensible EJB Servers
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    Chapter 14 A Leasing Model to Deal with Partial Failures in Mobile Ad Hoc Networks
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    Chapter 15 Reusing and Composing Tests with Traits
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    Chapter 16 Flow-Centric, Back-in-Time Debugging
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    Chapter 17 A Classification Framework for Pointcut Languages in Runtime Monitoring
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    Chapter 18 Fast Simulation Techniques for Design Space Exploration
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    Chapter 19 PyGirl: Generating Whole-System VMs from High-Level Prototypes Using PyPy
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    Chapter 20 Using Grammarware Languages to Define Operational Semantics of Modelled Languages
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    Chapter 21 Automatic Generation of Integrated Formal Models Corresponding to UML System Models
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

2 tweeters
1 patent
1 Facebook page

Readers on

19 Mendeley
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Objects, Components, Models and Patterns
Published by
ADS, June 2009
DOI 10.1007/978-3-642-02571-6
978-3-64-202571-6, 978-3-64-202570-9

Oriol, Manuel, Meyer, Bertrand

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 5%
Germany 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 42%
Student > Bachelor 3 16%
Researcher 2 11%
Student > Ph. D. Student 2 11%
Professor 1 5%
Other 3 16%
Readers by discipline Count As %
Computer Science 15 79%
Business, Management and Accounting 2 11%
Social Sciences 1 5%
Engineering 1 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 04 March 2016.
All research outputs
of 13,462,296 outputs
Outputs from ADS
of 26,212 outputs
Outputs of similar age
of 264,266 outputs
Outputs of similar age from ADS
of 266 outputs
Altmetric has tracked 13,462,296 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 26,212 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 79% of its peers.
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 264,266 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 266 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.