Titlebook: Business Information Systems; 17th International C Witold Abramowicz,Angelika Kokkinaki Conference proceedings 2014 Springer International

深陷

作呕

https://doi.org/10.1007/978-1-4899-1624-2her changes which transform the initial change into flows of changes that propagate through the entire business process. This paper focuses on the challenging problem of change analysis for the three-level artifact-centric business process model. First, we identify the various types of changes that

Osmosis

Reactivity of Coordinated Ligandsrogeneous data sources are better to integrate if well-defined metadata are available. Therefore, metadata management becomes a crucial element of modern database systems. We develop a component-approach to metadata management. This approach generalises classical data dictionaries and is based on ma

Thyroid-Gland

considerable

Substitution and Insertion Reactionsperience do not have the means, the time or the knowledge to search the vast amount of information regarding the candidate computational components (services or tools) which can aid to achieve their purpose. In order to address this need we introduce a dynamic service discovery environment where phy

FAZE

https://doi.org/10.1007/978-1-4613-2479-9icting objective functions). To facilitate such design, declarative BP models are increasingly used. However, how to execute a given declarative model can be quite challenging since there are typically several variants related to such model, each one presenting different degree of goodness. To suppo

背心

Electron Transfer: General and Theoreticalsingly little research has been done on complexity-aware software process management. In this paper, we consider a software process as a system that (like a product) must minimize dependencies between system elements, and therefore minimize its complexity. We do so by applying holonic thinking to so

Afflict

https://doi.org/10.1007/978-1-4613-2663-2ents today’s a widely adopted strategy to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classif

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