Modelling electronic service systems using UML

This paper presents a profile for modelling systems of electronic services using UML. Electronic services encapsulate business services, an organisational unit focused on delivering benefit to a consumer, to enhance communication, coordination and information management. Our profile is based on a formal, workflow-oriented description of electronic services that is abstracted from particular implementation technologies. Resulting models provide the basis for a formal analysis to verify behavioural properties of services. The models can also relate services to management components, including workflow managers and Electronic Service Management Systems (ESMSs), a novel concept drawn from experience of HP Service Composer and DySCo (Dynamic Service Composer), providing the starting point for integration and implementation tasks. Their UML basis and platform-independent nature is consistent with a Model-Driven Architecture (MDA) development strategy, appropriate to the challenge of developing electronic service systems using heterogeneous technology, and incorporating legacy systems

Merging Application Models in a MDA Based Runtime Environment for Enterprise Information Systems

The issue of merging source code based applications is very problematic, particularly when involving code from disparate sources, due to the typical unsuitability of available source code for software merging. The relatively recent field of model driven architecture is primely involved in the definition and development of the source model structures for model based applications and in developing transformations from the abstract models to various executable formats. The authors are also involved in these MDA activities in the development of their G2 prototype system targeted at the specific domain of enterprise information system style applications. They have reviewed various methods for merging application models within this domain and describe the fundamentals of three application model integration methods from their G2 system; standard element referencing, virtual data element mapping and element envelopment that can be used to provide practical and simple application model merging at both the design time and runtime of a model based production system, to produce a working integrated merged application

The Future of Modeling in Material Handling Systems

Today, when we talk about “modeling” in the context of material handling systems, invariably we are referring to a mathematical or computational model for analyzing some aspect of the system, such as its throughput rate, response time, cost of ownership, required storage capacity, etc. Creating these kinds of models requires considerable knowledge in at least two domains the material handling system domain, and the analysis methodology domain—and considerable skill in the “art of modeling” in order to express the former in the terms of the latter. The results can be somewhat ad hoc—e.g., two different modelers are likely to create two somewhat different simulation models of exactly the same material handling system. In the past, the situation in software development was very similar, with individual programming experts idiosyncratically driving software development. Over the past twenty years, however, computer scientists and software engineers have created a radically different approach to the process of software “modeling” called Model Driven Architecture, or MDA, that is used to create software for standard applications. The thesis of this paper is that MDA can be adapted to the kind of modeling done to support design and operational decision making in material handling systems. The paper describes MDA technologies in the context of material handling system modeling, and explains how adapting this approach to our context will transform the way we do research and the way material handling systems are analyzed and designed in practice

A new MDA-SOA based framework for intercloud interoperability

Cloud computing has been one of the most important topics in Information Technology which aims to assure scalable and reliable on-demand services over the Internet. The expansion of the application scope of cloud services would require cooperation between clouds from different providers that have heterogeneous functionalities. This collaboration between different cloud vendors can provide better Quality of Services (QoS) at the lower price. However, current cloud systems have been developed without concerns of seamless cloud interconnection, and actually they do not support intercloud interoperability to enable collaboration between cloud service providers. Hence, the PhD work is motivated to address interoperability issue between cloud providers as a challenging research objective. This thesis proposes a new framework which supports inter-cloud interoperability in a heterogeneous computing resource cloud environment with the goal of dispatching the workload to the most effective clouds available at runtime. Analysing different methodologies that have been applied to resolve various problem scenarios related to interoperability lead us to exploit Model Driven Architecture (MDA) and Service Oriented Architecture (SOA) methods as appropriate approaches for our inter-cloud framework. Moreover, since distributing the operations in a cloud-based environment is a nondeterministic polynomial time (NP-complete) problem, a Genetic Algorithm (GA) based job scheduler proposed as a part of interoperability framework, offering workload migration with the best performance at the least cost. A new Agent Based Simulation (ABS) approach is proposed to model the inter-cloud environment with three types of agents: Cloud Subscriber agent, Cloud Provider agent, and Job agent. The ABS model is proposed to evaluate the proposed framework.Fundação para a Ciência e a Tecnologia (FCT) - (Referencia da bolsa: SFRH SFRH / BD / 33965 / 2009) and EC 7th Framework Programme under grant agreement n° FITMAN 604674 (http://www.fitman-fi.eu

E-MDAV: A Framework for Developing Data-Intensive Web Applications

The ever-increasing adoption of innovative technologies, such as big data and cloud computing, provides significant opportunities for organizations operating in the IT domain, but also introduces considerable challenges. Such innovations call for development processes that better align with stakeholders needs and expectations. In this respect, this paper introduces a development framework based on the OMG's Model Driven Architecture (MDA) that aims to support the development lifecycle of data-intensive web applications. The proposed framework, named E-MDAV (Extended MDA-VIEW), defines a methodology that exploits a chain of model transformations to effectively cope with both forward- and reverse-engineering aspects. In addition, E-MDAV includes the specification of a reference architecture for driving the implementation of a tool that supports the various professional roles involved in the development and maintenance of data-intensive web applications. In order to evaluate the effectiveness of the proposed E-MDAV framework, a tool prototype has been developed. E-MDAV has then been applied to two different application scenarios and the obtained results have been compared with historical data related to the implementation of similar development projects, in order to measure and discuss the benefits of the proposed approach

Towards semi-automatic generation of training scenarios in industrial automated systems

International audienceIn the context of E-Learning, remote hands-on training has become an insisting need as in traditional learning, especially in scientific and technical disciplines. Electronic Laboratories (ELabs) have been growing for the last few years. But till now, exchanging learning scenarios of ELabs is still difficult as existing scenarios (elaborated by means of standard ELearning authoring tools) are restricted to specific apparatuses, which prevents instructors from reusing or exchanging scenarios. Actually, LIESP team started in 2002 a research aiming to provide a framework which helps towards exchanging ELab learning scenarios when they fit to similar apparatuses (same functions, maybe not the same hardware). Meanwhile, LIMOS team focused on a design process to automate PLC code generation to help to design and generate programs for industrial discrete systems. This paper presents a project of merging these works to help ELab designers to design and integrate apparatuses into ELab frameworks when these apparatuses are discrete systems

Engineering Enterprise Software Systems with Interactive UML Models and Aspect-Oriented Middleware

Large scale enterprise software systems are inherently complex and hard to maintain. To deal with this complexity, current mainstream software engineering practices aim at raising the level of abstraction to visual models described in OMG’s UML modeling language. Current UML tools, however, produce static design diagrams for documentation which quickly become out-of-sync with the software, and thus obsolete. To address this issue, current model-driven software development approaches aim at software automation using generators that translate models into code. However, these solutions don’t have a good answer for dealing with legacy source code and the evolution of existing enterprise software systems. This research investigates an alternative solution by making the process of modeling more interactive with a simulator and integrating simulation with the live software system. Such an approach supports model-driven development at a higher-level of abstraction with models without sacrificing the need to drop into a lower-level with code. Additionally, simulation also supports better evolution since the impact of a change to a particular area of existing software can be better understood using simulated “what-if” scenarios. This project proposes such a solution by developing a web-based UML simulator for modeling use cases and sequence diagrams and integrating the simulator with existing applications using aspect-oriented middleware technology

Modeling 4.0: Conceptual Modeling in a Digital Era

Digitization provides entirely new affordances for our economies and societies. This leads to previously unseen design opportunities and complexities as systems and their boundaries are re-defined, creating a demand for appropriate methods to support design that caters to these new demands. Conceptual modeling is an established means for this, but it needs to be advanced to adequately depict the requirements of digitization. However, unlike the actual deployment of digital technologies in various industries, the domain of conceptual modeling itself has not yet undergone a comprehensive renewal in light of digitization. Therefore, inspired by the notion of Industry 4.0, an overarching concept for digital manufacturing, in this commentary paper, we propose Modeling 4.0 as the notion for conceptual modeling mechanisms in a digital environment. In total, 12 mechanisms of conceptual modeling are distinguished, providing ample guidance for academics and professionals interested in ensuring that modeling techniques and methods continue to fit contemporary and emerging requirements

A Model Based Framework for IoT-Aware Business Process Management

IoT-aware Business Processes (BPs) that exchange data with Internet of Things (IoT) devices, briefly referred to as IoT-aware BPs, are gaining momentum in the BPM field. Introducing IoT technologies from the early stages of the BP development process requires dealing with the complexity and heterogeneity of such technologies at design and analysis time. This paper analyzes widely used IoT frameworks and ontologies to introduce a BPMN extension that improves the expressiveness of relevant BP modeling notations and allows an appropriate representation of IoT devices from both an architectural and a behavioral perspective. In the BP management field, the use of simulation-based approaches is recognized as an effective technology for analyzing BPs. Simulation models need to be parameterized according to relevant properties of the process under study. Unfortunately, such parameters may change during the process operational life, thus making the simulation model invalid with respect to the actual process behavior. To ease the analysis of IoT-aware BPs, this paper introduces a model-driven method for the automated development of digital twins of actual business processes. The proposed method also exploits data retrieved by IoT sensors to automatically reconfigure the simulation model, to make the digital twin continuously coherent and compliant with its actual counterpart

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018