New hybrid multi-criteria decision-making DEMATELMAIRCA model: sustainable selection of a location for the development of multimodal logistics centre

The paper describes the application of a new multi-criteria decision-making (MCDM) model, MultiAtributive Ideal-Real Comparative Analysis (MAIRCA), used to select a location for the development of a multimodal logistics centre by the Danube River. The MAIRCA method is based on the comparison of theoretical and empirical alternative ratings. Relying on theoretical and empirical ratings the gap (distance) between the empirical and ideal alternative is defined. To determine the weight coefficients of the criteria, the DEMATEL method was applied. In this paper, through a sensitivity analysis, the results of MAIRCA and other MCDM methods – MOORA, TOPSIS, ELECTRE, COPRAS and PROMETHEE – were compared. The analysis showed that a smaller or bigger instability in alternative rankings appears in MOORA, TOPSIS, ELECTRE and COPRAS. On the other hand, the analysis showed that MAIRCA and PROMETHEE offer consistent solutions and have a stable and wellstructured analytical framework for ranking the alternatives. By presenting a new method MCDM expands the theoretical framework of expertise in the field of MCDM. This enables the analysis of practical problems with new methodology and creates a basis for further theoretical and practical upgrade

Exploiting structural similarity of log files in fault diagnosis for Web service composition

AbstractWith increasing deployment of Web services, the research on the dependability and availability of Web service composition becomes more and more active. Since unexpected faults of Web service composition may occur in different levels at runtime, log analysis as a typical data-driven approach for fault diagnosis is more applicable and scalable in various architectures. Considering the trend that more and more service logs are represented using XML or JSON format which has good flexibility and interoperability, fault classification problem of semi-structured logs is considered as a challenging issue in this area. However, most existing approaches focus on the log content analysis but ignore the structural information and lead to poor performance. To improve the accuracy of fault classification, we exploit structural similarity of log files and propose a similarity based Bayesian learning approach for semi-structured logs in this paper. Our solution estimates degrees of similarity among structural elements from heterogeneous log data, constructs combined Bayesian network (CBN), uses similarity based learning algorithm to compute probabilities in CBN, and classifies test log data into most probable fault categories based on the generated CBN. Experimental results show that our approach outperforms other learning approaches on structural log datasets