Mostra el registre d'ítem simple
Dealing with goal models complexity using topological metrics and algorithms
dc.contributor.author | Méndez Tapia, Emma Lucía |
dc.contributor.author | López Cuesta, Lidia |
dc.contributor.author | Ayala Martínez, Claudia Patricia |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació |
dc.date.accessioned | 2018-07-26T06:53:44Z |
dc.date.available | 2018-07-26T06:53:44Z |
dc.date.issued | 2017 |
dc.identifier.citation | Méndez, L., López, L., Ayala, C.P. Dealing with goal models complexity using topological metrics and algorithms. A: International i* Workshop. "iStar 2017: Proceedings of the 10th International i* Workshop co-located with the 29th International Conference on Advanced Information Systems Engineering (CAiSE 2017): Essen, Germany, June 12-13, 2017". CEUR-WS.org, 2017, p. 43-48. |
dc.identifier.isbn | 1613-0073 (ISSN) |
dc.identifier.uri | http://hdl.handle.net/2117/119981 |
dc.description.abstract | The inherent complexity of business goal-models is a challenge for organizations that has to analyze and maintaining them. Several approaches are developed to reduce the complexity into manageable limits, either by providing support to the modularization or designing metrics to monitor the complexity levels. These approaches are designed to identify an unusual complexity comparing it among models. In the present work, we expose two approaches based on structural characteristics of goal-model, which do not require these comparisons. The first one ranksthe importance of goalsto identify a manageable set of them that can be considered as a priority; the second one modularizes the model to reduce the effort to understand, analyze and maintain the model. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | CEUR-WS.org |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
dc.subject.lcsh | Business forecasting--Mathematical models--Computer programs |
dc.subject.other | : i* Framework |
dc.subject.other | iStar |
dc.subject.other | Complexity |
dc.subject.other | Metrics |
dc.subject.other | PageRank |
dc.subject.other | Clustering |
dc.title | Dealing with goal models complexity using topological metrics and algorithms |
dc.type | Conference report |
dc.subject.lemac | Previsió dels negocis -- Models matemàtics |
dc.contributor.group | Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://ceur-ws.org/Vol-1829/iStar17_paper_3.pdf |
dc.rights.access | Open Access |
local.identifier.drac | 23243109 |
dc.description.version | Postprint (published version) |
local.citation.author | Méndez, L.; López, L.; Ayala, C.P. |
local.citation.contributor | International i* Workshop |
local.citation.publicationName | iStar 2017: Proceedings of the 10th International i* Workshop co-located with the 29th International Conference on Advanced Information Systems Engineering (CAiSE 2017): Essen, Germany, June 12-13, 2017 |
local.citation.startingPage | 43 |
local.citation.endingPage | 48 |