Shunshan Piao, Jeongmin Park and Eunseok Lee
This paper seeks to develop an approach to problem localization and an algorithm to address the issue of determining the dependencies among system metrics for automated system…
Abstract
Purpose
This paper seeks to develop an approach to problem localization and an algorithm to address the issue of determining the dependencies among system metrics for automated system management in ubiquitous computing systems.
Design/methodology/approach
This paper proposes an approach to problem localization for learning the knowledge of dynamic environment using probabilistic dependency analysis to automatically determine problems. This approach is based on Bayesian learning to describe a system as a hierarchical dependency network, determining root causes of problems via inductive and deductive inferences on the network. An algorithm of preprocessing is performed to create ordering parameters that have close relationships with problems.
Findings
The findings show that using ordering parameters as input of network learning, it reduces learning time and maintains accuracy in diverse domains especially in the case of including large number of parameters, hence improving efficiency and accuracy of problem localization.
Practical implications
An evaluation of the work is presented through performance measurements. Various comparisons and evaluations prove that the proposed approach is effective on problem localization and it can achieve significant cost savings.
Originality/value
This study contributes to research into the application of probabilistic dependency analysis in localizing the root cause of problems and predicting potential problems at run time after probabilities propagation throughout a network, particularly in relation to fault management in self‐managing systems.
Details
Keywords
China is currently developing and promoting an industrial cluster policy at the government level. By enacting the ‘Opinion on promoting industrial cluster development’, China is…
Abstract
China is currently developing and promoting an industrial cluster policy at the government level. By enacting the ‘Opinion on promoting industrial cluster development’, China is supporting the development of industrial clusters. Building an industrial cluster is done by using a single factor but requires many additional factors like regional characteristics, competitiveness factors are also diversified. To evaluate the competitiveness of the Chinese automobile industry cluster, a competitiveness element index should be developed and a competitiveness evaluation method is needed to evaluate the importance of each element. To accomplish this objective, this research applied the analytic hierarchy process (AHP) and focused on the importance of the competitiveness elements.
This research investigated the character is tics regarding cases of clusters and also analyzed the competitiveness of the Changchun automobile cluster located in northeastern China. The purpose of this research is to help Korean enterprises who enter China in the hopes that Korea will emerge as a top automobile production country.