A self-learning approach for validation of runtime adaptation in service-oriented systems

Abstract: Self-service Business Intelligence (SSBI) is an emerging topic for many companies. Casual users should be enabled to independently build their own analyses and reports. This accelerates and simplifies the decision-making processes. Although recent studies began to discuss parts of a self-service environment, none of these present a comprehensive architecture. Following a design science research approach, this study proposes a new self-service oriented BI architecture in order to address this gap. Starting from an in-depth literature review, an initial model was developed and improved by qualitative data analysis from interviews with 18 BI and IT specialists form companies across different industries. The proposed architecture model demonstrates the interaction between introduced self-service elements with each other and with traditional BI components. For example, we look at the integration of collaboration rooms and a self-learning knowledge database that aims to be a source for a report recommender. Keywords: Business Intelligence, Big Data, Architecture, Self-Service, Analytics

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A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

Processes ◽

10.3390/pr7100739 ◽

2019 ◽

Vol 7 (10) ◽

pp. 739 ◽

Cited By ~ 2

Author(s):

Seung-Jun Shin ◽

Young-Min Kim ◽

Prita Meilanitasari

Keyword(s):

Machine Tool ◽

Process Planning ◽

Predictive Modeling ◽

Manufacturing Systems ◽

Minimum Energy ◽

Hybrid Learning ◽

Prototype System ◽

Learning Approach ◽

Machining Processes ◽

Self Learning

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.

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