GRAI-ICE Model Driven Interoperability Architecture for Developing Interoperable ESA

International audienceThis paper presents GRAI-ICE Model Driven Interoperability Architecture (MDI) which is developed based on MDA (Model Driven Architecture) of OMG and some initial works performed in INTEROP NoE. This MDI architecture aims at supporting the development of changeable on-demand and interoperable ESA (Enterprise Software Application). The architecture defined five modelling levels, i.e., Top CIM, Bottom CIM, Object oriented PIM, Pattern oriented PSM, and Component and configuration oriented CODE. This paper presents in detail core concepts and rational of each modeling level. An application example in nuclear equipment industry is outlined

Multitask Oriented Virtual Resource Integration and Optimal Scheduling in Cloud Manufacturing

To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks

MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer interaction systems. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese). Upon MultiSpider, we further identify the lexical and structural challenges of text-to-SQL (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.Comment: AAAI2023 Main Conference. Code: https://github.com/microsoft/ContextualS

Feature Space Based Business Model Quality Evaluation

It is inevitable that there are more or less diversities between business models created by different modelers, thus it is necessary to evaluate and compare them quantitatively to help decision makers discover whose models are pressing much closer to customer requirements. In this paper, a new approach for business model quality evaluation is presented. In order to deal with business models described by varied modeling languages, a unified and extended feature modeling technique is adopted. Quality of a user created model is then measured from two views “completeness” and “soundness” by assessing the distance between the user model and the standard model with the help of feature space as the tools. An example is briefly shown along with each concept and algorithm for illustration. Benefits and deficiencies of our method are briefly concluded for future works