| Title |
A catalog of automated analysis methods for enterprise models
|
|---|---|
| Published in |
SpringerPlus, April 2016
|
| DOI | 10.1186/s40064-016-2032-9 |
| Pubmed ID | |
| Authors |
Hector Florez, Mario Sánchez, Jorge Villalobos |
| Abstract |
Enterprise models are created for documenting and communicating the structure and state of Business and Information Technologies elements of an enterprise. After models are completed, they are mainly used to support analysis. Model analysis is an activity typically based on human skills and due to the size and complexity of the models, this process can be complicated and omissions or miscalculations are very likely. This situation has fostered the research of automated analysis methods, for supporting analysts in enterprise analysis processes. By reviewing the literature, we found several analysis methods; nevertheless, they are based on specific situations and different metamodels; then, some analysis methods might not be applicable to all enterprise models. This paper presents the work of compilation (literature review), classification, structuring, and characterization of automated analysis methods for enterprise models, expressing them in a standardized modeling language. In addition, we have implemented the analysis methods in our modeling tool. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 2% |
| France | 1 | 2% |
| Unknown | 41 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 26% |
| Student > Ph. D. Student | 7 | 16% |
| Student > Bachelor | 6 | 14% |
| Student > Doctoral Student | 3 | 7% |
| Professor | 3 | 7% |
| Other | 5 | 12% |
| Unknown | 8 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 49% |
| Engineering | 5 | 12% |
| Business, Management and Accounting | 3 | 7% |
| Unspecified | 1 | 2% |
| Environmental Science | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 9 | 21% |
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 19 April 2016.
All research outputs
#14,257,527
of 22,865,319 outputs
Outputs from SpringerPlus
#773
of 1,850 outputs
Outputs of similar age
#160,411
of 300,372 outputs
Outputs of similar age from SpringerPlus
#81
of 189 outputs
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We're also able to compare this research output to 189 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 52% of its contemporaries.