Assessement of Enterprise Interoperability Maturity Level through Generative and Recognition Models

In a globalized and networked society, enterprise interoperability is a key factor of success for enterprises in their effort to maximize their own added values and to exploit the market opportunities. The sustainable enterprise interoperability is a continuous challenge of the networked collaborative environment. By making business decisions, managers have to take into account the maturity level of their own enterprise and of others’ with whom they get involved into businesses. Maturity level of enterprise interoperability has been defined by the Framework for Enterprise Interoperability (FEI), standardized by CEN EN ISO 11354. In this paper, we propose a novel approach to assess maturity levels of enterprise interoperability (MLEI) through latent factor analysis (LFA) and generative and recognition models applied to the categories and features defined by FEI. Given an enterprise interoperability maturity matrix we have trained a stochastic neural network, namely Restricted Bolzmann Machine (RBM) to learn the MLEI. Our research seeks to answer the following questions: whether the maturity level assessed by evaluators correlate with the maturity levels recognized by RBM trained in a supervised learning representation, and how to model recognition matrix of MLEI by using maturity level correlations between observed performances (inputs) and latent or hidden factors that influence the correct assessment. We considered a maturity level correlation matrix representing the enterprise features as defined in FEI in addition to a set of latent factors, representing the type of maturity level of each individual enterprise. Our proposal is based on a generative and a recognition model using deterministic non-linear functions in a Bayesian setting. The model has been tested on artificial data by training a RBM. Experiments on artificial data sets of enterprises proved that our proposal is a reliable approach that can be further developed into a methodology and extended for the design of adaptive learning agents. In the perspective of the Future Internet, such agents may successfully assist human evaluators in the tedious and time consuming process of the assessment of MLEI in real settings.