EDBT 2026 Demo / reviewers in the wild / expert
Erling Vagn Sørensen
dblp:20/208
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 1993
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1Software engineering, systems software and programming languages · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
dependability modeling |
0.0 | 1 | 1993 | From CSP Models to Markov Models · IEEE Trans. Software Eng. 1993 |
Performance modeling and evaluation
markov models |
0.0 | 1 | 1993 | From CSP Models to Markov Models · IEEE Trans. Software Eng. 1993 |
Methods — techniques the papers use, named apart from their topics
finite automaton transformation · 0.0communicating sequential processes · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1993 | Composition and Refinement of Probabilistic Real-Time Systems
Jens Nordahl, Erling Vagn Sørensen |
SAFECOMP | 3 |
| 1993 | From CSP Models to Markov ModelsabstractIt is shown how a probabilistic dependability model of a safety-critical system can be derived from a trace-based functional model of the system. The functional model is a communicating sequential process (CSP) that includes command, failure, and repair events. The dependability model is a time homogeneous Markov process with transitions determined by these events. The method applies to deterministic systems that can be described in terms of a finite number of states and in which all event occurrences are stochastic with exponential time distribution. The derivation is carried out in two steps. An algorithmic determination is made of a finite automaton from the specification of the CSP process. The automaton is transformed into a Markov process. The Markov model for this system is used to determine the waiting time to terminal failure. The theory is applied to a larger and more realistic example: a gas burner system operating in the on-off mode. For this system, the waiting time to terminal failure is calculated, and the number of failures per year in a large population of identical, independently operated systems is estimated.> Erling Vagn Sørensen, Jens Nordahl, Niels Herman Hansen |
IEEE Trans. Software Eng. | 1 |