Flaws in the Flow: The Weakness of Unstructured Business Process Modeling Languages Dealing with Data

Combi, Carlo; Gambini, Mauro

doi:10.1007/978-3-642-05148-7_6

Cited by 42 publications

(15 citation statements)

References 17 publications

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“…A few contributions to data-centric workflows can be cited. The work in [35] clarifies the interactions between control flow and data flow features in business process models, showing that ignoring data flow features can limit the flexibility of business process modeling languages. The work in [36] deals with workflow verification.…”

Section: Related Workmentioning

confidence: 99%

Extending BCDM to Cope with Proposals and Evaluations of Updates

Anselma

Bottrighi

Montani

et al. 2013

IEEE Trans. Knowl. Data Eng.

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Abstract-The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]). In cases in which data/knowledge quality and reliability are crucial, proposals of update/insertion/deletion need to be evaluated by experts. To the best of our knowledge, no theoretical framework has been devised to model the semantics of update proposal/evaluation in the relational context. Since time is an intrinsic part of most domains (as well as of the proposal/evaluation process itself), semantic approaches to temporal relational databases (specifically, Bitemporal Conceptual Data Model (henceforth, BCDM) [2]) are the starting point of our approach. In this paper, we propose BCDM PV , a semantic temporal relational model that extends BCDM to deal with multiple update/insertion/deletion proposals and with acceptances/rejections of proposals themselves. We propose a theoretical framework, defining the new data structures, manipulation operations and temporal relational algebra and proving some basic properties, namely that BCDM PV is a consistent extension of BCDM and that it is reducible to BCDM. These properties ensure consistency with most relational temporal database frameworks, facilitating implementations.

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Section: Related Workmentioning

confidence: 99%

Extending BCDM to Cope with Proposals and Evaluations of Updates

Anselma

Bottrighi

Montani

et al. 2013

IEEE Trans. Knowl. Data Eng.

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“…How understandable a model 1.1. CONTRIBUTIONS is depends not only on the size of the model, but also on features such as level of concurrency and modularity of the model [18,64]. Second, unless all corrections are strict, there is a need to verify that the resulting process model satisfies the original goal.…”

Section: Automated Process Evolution Approachmentioning

confidence: 99%

Corrective Evolution of Adaptable Process Models

Baresi

Marconi

Pistore

et al. 2013

Enterprise, Business-Process and Information Systems Modeling

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“…Further, process management systems with block-structured process modeling language like AristaFlow BPM Suite [33] and CAKE2 [34] emerged, which have been applied to a variety of processes from different domains. When compared to unstructured process models, block-structured ones are easier understandable and have lower error probability [35,36,37]. If a process model is not block-structured, in many cases we can transform it into a block-structured one [31,36,12,29].…”

Section: Backgroundsmentioning

confidence: 99%

Mining business process variants: Challenges, scenarios, algorithms

Reichert

Wombacher

2011

Data & Knowledge Engineering

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During the last decade a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. Respective adaptations result in a large number of process model variants that were derived from the same process model, but slightly differ in structure. Generally, such model variants are expensive to configure and maintain. This paper introduces two different scenarios for learning from process model adaptations and for discovering a reference model out of which the variants can be configured with minimum efforts. The first scenario presumes a reference process model and a collection of related process model variants. The goal is to evolve the reference process model such that it structurally fits better to the given variant models. The second scenario comprises a collection of process variants, while the original reference model is unknown; i.e., the goal is to "merge" these variants into a reference process model. We suggest two algorithms that are applicable in both scenarios, but which have their pros and cons. We systematically compare the two algorithms and contrast them with conventional process mining techniques. Our comparison results indicate good performance of both algorithms. Further they confirm that specific techniques are needed for learning from past process adaptations. Finally, we present a case study in the automotive industry in which we applied our algorithms.

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Flaws in the Flow: The Weakness of Unstructured Business Process Modeling Languages Dealing with Data

Cited by 42 publications

References 17 publications

Extending BCDM to Cope with Proposals and Evaluations of Updates

Extending BCDM to Cope with Proposals and Evaluations of Updates

Corrective Evolution of Adaptable Process Models

Mining business process variants: Challenges, scenarios, algorithms

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