[서평]「Software Engineering : A Practitioner's Approach, 4^(th) Edition」

Business Process Management (BPM) includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. It can be considered as an extension of classical Workflow Management (WFM) systems and approaches. Although the practical relevance of BPM is undisputed, a clear definition of BPM and related acronyms such as BAM, BPA, and STP are missing. Moreover, a clear scientific foundation is missing. In this paper, we try to demystify the acronyms in this domain, describe the state-of-the-art technology, and argue that BPM could benefit from formal methods/languages (cf. Petri nets, process algebras, etc.).

Business Process Management: A Survey

https://hal.archives-ouvertes.fr/hal-01982217/document

Interoperability assessment: A systematic literature review

Impact analysis and change propagation in service-oriented enterprises

In recent years, due to technological advancements, the concept of Industry 4.0 (I4.0) is gaining popularity, while presenting several technical challenges being tackled by both the industrial and academic research communities. Semantic Web including Knowledge Graphs is a promising technology that can play a significant role in realizing I4.0 implementations. This paper surveys the use of the Semantic Web and Knowledge Graphs for I4.0 from different perspectives such as managing information related to equipment maintenance, resource optimization, and the provision of on-time and on-demand production and services. Moreover, to solve the challenges of limited depth and expressiveness in the current ontologies, we have proposed an enhanced reference generalized ontological model (RGOM) based on Reference Architecture Model for I4.0 (RAMI 4.0). RGOM can facilitate a range of I4.0 concepts including improved asset monitoring, production enhancement, reconfiguration of resources, process optimizations, product orders and deliveries, and the life cycle of products. Our proposed RGOM can be used to generate a knowledge graph capable of providing answers in response to any real-time query.

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Semantic Web and Knowledge Graphs for Industry 4.0

The emergence of BPMN as a standard notation to express the business processes is based on its simplicity of notations and its exhaustive expressiveness. Nevertheless the lack of formal semantics in the BPMN can cause syntactic and structural errors. The former requires less effort to be checked, while the later usually requires attention to prove some properties, like deadlock-freedom and livelock-freedom. In this paper, we address the issue of detecting the structural errors with an approach based on model checking. It verifies the soundness of business process model and helps the business modelers to avoid the deadlocks, livelocks, and multiple terminations errors.

/pdf/using-model-checking-to-control-the-structural-errors-in-8zsm3dbur6.pdf

Using model checking to control the structural errors in BPMN models

AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data

Change management is a critical task to control the side effects of a modification during the business process evolution. The evolution of business processes is an essential activity for the companies to better fulfill the requirements of their customers and different stakeholders. In this respect, the enterprises should adopt an effective mechanism in order to achieve the flexible business process models. It is important to identify and highlight the ripple effects of a change for minimizing their impact on other parts or entities of the system and associated services. This paper proposes a dependency-centric approach for change impact analysis. We attempt to demonstrate the change impact propagation in business process models by detecting and analyzing the interdependencies among all parts of business processes along with associated services. It can support the maintenance and evolution of business process models. The major objective is to help the modelers and business experts to assess the associated risk of intended changes and estimate the effort required for their accomplishments.

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Analyzing the ripple effects of change in business process models

Abstract Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such “industrial big data” can relay its usefulness to reach further utilitarian applications. In this context, Machine Learning (ML) is among the major predictive modeling approaches that can enable manufacturing researchers and practitioners to improve the product quality and achieve resource efficiency by exploiting large amounts of data (which is collected during manufacturing process). However, disposing ML algorithms is a challenging task for manufacturing industrial actors due to the prior specification of one or more algorithms hyperparameters (HPs) and their values. Moreover, manufacturing industrial actors often lack the technical expertise to apply advanced analytics. Consequently, it necessitates frequent consultations with data scientists; but such collaborations tends to cost the delays, which can generate the risks such as human-resource bottlenecks. As the complexity of these tasks increases, so does the demand for support solutions. In response, the field of automated ML (AutoML) is a data mining-based formalism that aims to reduce human effort and speedup the development cycle through automation. In this regard, existing approaches include evolutionary algorithms, Bayesian optimization, and reinforcement learning. These approaches mainly focus on providing the user assistance by automating the partial or entire data analysis process, but they provide very limited details concerning their impact on the analysis. The major goal of these conventional approaches has been generally focused on the performance factors, while the other important and even crucial aspects such as computational complexity are rather omitted. Therefore, in this paper, we present a novel meta-learning based approach to automate ML predictive models built over the industrial big data. The approach is leveraged with development of, AMLBID, an Automated ML tool for Big Industrial Data analyses. It attempts to support the manufacturing engineers and researchers who presumably have meager skills to carry out the advanced analytics. The empirical results show that AMLBID surpasses the state-of-the-art approaches and could retrieve the usefulness of large manufacturing data to prosper the research in manufacturing domain and improve the use of predictive models instead of precluding their outcomes. 

/pdf/using-meta-learning-for-automated-algorithms-selection-and-3edhntmw.pdf

Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data

Logistics processes involve complex physical flows and integration of different elements. It is widely observed that the uncontrolled processes can decline the state of logistics. The optimization of logistic processes can support the desired growth and consistent continuity of logistics. In this paper, we present a software framework for logistic processes optimization. It primarily defines logistic ontologies and then optimize them. It intends to assist the design of a computational knowledge-base tool for better util-isation of the logistic resources. The defined ontologies share the knowledge domain of logistics and optimization. The knowledge-base incorporates the standard optimization techniques, along with the definition of ontologies to resolve logistic problems. It can help to better understand the inherent complexities of logistic problems.

https://hal.archives-ouvertes.fr/hal-01894059/document

Adeel Ahmad

Papers

Using model checking to control the structural errors in BPMN models

AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data

Analyzing the ripple effects of change in business process models

Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data

Logisitics optimization using ontologies