2018
DOI: 10.1016/j.jss.2018.05.011
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A likelihood-free Bayesian derivation method for service variants

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Cited by 1 publication
(8 citation statements)
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“…In our algorithm, this search process is captured by function SemanticMatch(p, R) (line 5) which either returns a business entity that semantically matches p or an empty element (null) when no match can be found. Next, if a matching entity e is retrieved, the mapping between e and the corresponding parameter p is recorded (line 11), and all the parameters p nested in p are mapped to the attributes of e (lines [12][13][14]. Mapping a parameter to an attribute is captured by function ConvertToAttr(p ) (line 13).…”
Section: Definition 5 (Business Entity Model) a Business Entitymentioning
confidence: 99%
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“…In our algorithm, this search process is captured by function SemanticMatch(p, R) (line 5) which either returns a business entity that semantically matches p or an empty element (null) when no match can be found. Next, if a matching entity e is retrieved, the mapping between e and the corresponding parameter p is recorded (line 11), and all the parameters p nested in p are mapped to the attributes of e (lines [12][13][14]. Mapping a parameter to an attribute is captured by function ConvertToAttr(p ) (line 13).…”
Section: Definition 5 (Business Entity Model) a Business Entitymentioning
confidence: 99%
“…The technique exploits close proximity of parameters in each operation to determine the most likely next parameter to find for a subset based on a previous parameter probabilistic tree search. We herein give a short introduction to this service variant analysis technique proposed in our previous publication [14], where readers interested in the technique can find more relevant details. Figure 4 depicts an overview of our service variant analysis technique (using an abstract example).…”
Section: E Service Variants Analysismentioning
confidence: 99%
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