Los tres gauchos orientales, y : el matrero Luciano Santos. Cantalicio Quirós y Miterio Castro en un baile del Club Uruguay. Otras poesías sultas /

Mode of access: Internet.

Idioma nacional de los Argentinos, por el Dr. Luciano Abeille ... con una introducción por Louis Duvau.

"Principales fuentes lingüísticas y filológicas": p. [vii]-ix.

Luciano español : dialogos morales, vtiles por sus documentos /

Mode of access: Internet.

El sueño de Luciano Samosatense : que es la Vida de Luciano y La tabla de Cebes ... : en griego y español /

Mode of access: Internet.

Scriptores Græci; sive Selecta ex scriptoribus Græcis, : viz. Herodoto, Thucydide, Xenophonte, Platone, Isocrate, Luciano. : Cum versione Latina. In usum Regiæ Scholæ Etonensis.

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Parodiarum Graecarum exempla ex Aristophane, Plutarcho et Luciano excerpta.

Mode of access: Internet.

De Luciano Menippeo ...

Vita.

Jürg Aeppli / Luciano Gasser / Eveline Gutzwiller / Annette Tettenborn: Empirisches wissenschaftliches Arbeiten. Ein Studienbuch für die Bildungswissenschaften 2. Bad Heilbrunn: Klinkhardt 2011 [Rezension]

Rezension von: Jürg Aeppli / Luciano Gasser / Eveline Gutzwiller / Annette Tettenborn: Empirisches wissenschaftliches Arbeiten. Ein Studienbuch für die Bildungswissenschaften 2. Bad Heilbrunn: Klinkhardt 2011 (389 S.; ISBN 978-3-7815-1812-4)

Managing process model collections with AProMoRe

As organizations reach higher levels of Business Process Management maturity, they tend to collect numerous business process models. Such models may be linked with each other or mutually overlap, supersede one another and evolve over time. Moreover, they may be represented at different abstraction levels depending on the target audience and modeling purpose, and may be available in multiple languages (e.g. due to company mergers). Thus, it is common that organizations struggle with keeping track of their process models. This demonstration introduces AProMoRe (Advanced Process Model Repository) which aims to facilitate the management of (large) process model collections.

Clone Detection in Repositories of Business Process Models

Business process model repositories capture precious knowledge about an organization or a business domain. In many cases, these repositories contain hundreds or even thousands of models and they represent several man-years of effort. Over time, process model repositories tend to accumulate duplicate fragments, as new process models are created by copying and merging fragments from other models. This calls for methods to detect duplicate fragments in process models that can be refactored as separate subprocesses in order to increase readability and maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

APROMORE : an advanced process model repository

Business process models are becoming available in large numbers due to their popular use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: How can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionalities and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.

Clone detection in repositories of business process models

As organizations reach to higher levels of business process management maturity, they often find themselves maintaining repositories of hundreds or even thousands of process models, representing valuable knowledge about their operations. Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. ex- act clones, can be identified and factored out as shared subprocesses, the repository’s maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.