AI meets Business Rules and Business Process Management

dc.accessRightsAnonymous
dc.audienceSonstige
dc.contributor.authorHinkelmann, Knut
dc.contributor.authorWache, Holger
dc.date.accessioned2015-10-05T15:42:13Z
dc.date.available2015-10-05T15:42:13Z
dc.date.issued2008
dc.description.abstractBusiness rules and business process management are growing research and application areas for semantic technologies. While both areas make use of model driven knowledge representations?often in conjunction with application-oriented modeling tools?the potential of knowledge representations with precise semantics has only recently been recognized.Generally, the areas of ?business rules,? ?semantic technologies,? and ?business process management? are addressed by different communities at present. Standards are promoted by different organizations like W3C, OMG, and WfMC. Current research and practice, however, begins to identify and explore the benefits for combining methodologies from these different areas.Business rules, for example, strive to meet the increasing requirements of transparency and compliance, making sure that all stakeholders comply with all rules and regulations at any place and any time. Defining a commonly agreed vocabulary is a prerequisite for rule definitions.
dc.eventAAAI Spring Symposium Reports
dc.identifier.urihttp://hdl.handle.net/11654/9560
dc.language.isoenen
dc.relation.ispartofAAAI Spring Symposium Reports
dc.subjectBusiness Software
dc.subjectICT
dc.subject.ddc330 - Wirtschaft
dc.subject.ddc005 - Computer Programmierung, Programme und Daten
dc.titleAI meets Business Rules and Business Process Management
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereunbekannt
fhnw.ReviewTypeNo peer review
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.pagination
fhnw.publicationStateVeröffentlicht
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relation.isAuthorOfPublication9a5348f4-47b3-437d-a1f9-7cf66011e883
relation.isAuthorOfPublication.latestForDiscovery6898bec4-c71c-491e-b5f8-2b1cba9cfa00
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