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Formal Methods and Stochastic Models for Performance Evaluation

Overview of attention for book
Cover of 'Formal Methods and Stochastic Models for Performance Evaluation'

Table of Contents

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    Book Overview
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    Chapter 1 A Precedence PEPA Model for Performance and Reliability Analysis
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    Chapter 2 A Function-Equivalent Components Based Simplification Technique for PEPA Models
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    Chapter 3 Functional Performance Specification with Stochastic Probes
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    Chapter 4 Embedding Real Time in Stochastic Process Algebras
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    Chapter 5 Precise Regression Benchmarking with Random Effects: Improving Mono Benchmark Results
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    Chapter 6 Working Set Characterization of Applications with an Efficient LRU Algorithm
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    Chapter 7 Model Checking for a Class of Performance Properties of Fluid Stochastic Models
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    Chapter 8 Explicit Inverse Characterizations of Acyclic MAPs of Second Order
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    Chapter 9 Implementation Relations for Stochastic Finite State Machines
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    Chapter 10 On the Convergence Rate of Quasi Lumpable Markov Chains
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    Chapter 11 Applying the UML Class Diagram in the Performance Analysis
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    Chapter 12 Dependability Evaluation of Web Service-Based Processes
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    Chapter 13 Improving the Performance of IEEE 802.11e with an Advanced Scheduling Heuristic
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    Chapter 14 Worst Case Analysis of Batch Arrivals with the Increasing Convex Ordering
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    Chapter 15 The Impact of Buffer Finiteness on the Loss Rate in a Priority Queueing System
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    Chapter 16 Experimental Analysis of the Correlation of HTTP GET Invocations
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Mentioned by

wikipedia
1 Wikipedia page

Citations

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2 Dimensions

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1 Mendeley
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Title
Formal Methods and Stochastic Models for Performance Evaluation
Published by
Lecture notes in computer science, January 2006
DOI 10.1007/11777830
ISBNs
978-3-54-035362-1, 978-3-54-035365-2
Authors

András Horváth, Miklós Telek

Editors

Horváth, András, Telek, Miklós

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 100%
Readers by discipline Count As %
Computer Science 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 19 February 2013.
All research outputs
#7,459,393
of 22,805,349 outputs
Outputs from Lecture notes in computer science
#2,487
of 8,126 outputs
Outputs of similar age
#40,101
of 154,443 outputs
Outputs of similar age from Lecture notes in computer science
#47
of 146 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% 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 has gotten more attention than average, scoring higher than 55% 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 154,443 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.