42,440 research outputs found

    ON THE CHALLENGE OF A SEMI-AUTOMATIC TRANSFORMATION PROCESS IN MODEL DRIVEN ENTERPRISE INFORMATION SYSTEMS

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    Recently, Model Driven Engineering (MDE) approaches have been proposed for supporting the development, maintenance and evolution of software systems. Model driven architecture (MDA) from OMG (Object Management Group), “Software Factories” from Microsoft and the Eclipse Modelling Framework (EMF) from IBM are among the most representative MDE approaches. Nowadays, it is well recognized that model transformations are at the heart of these approaches and represent as a consequence one of the most important operations in MDE. However, despite the multitude of model transformation languages proposals emerging from university and industry, these transformations are often created manually. In this paper we present in the first part our previous works towards automation of the transformation process in the context of MDA. It consists on an extended architecture which introduces mapping and matching as first class entities in the transformation process, represented by models and metamodels. Our architecture is enforced by a methodology which details the different steps leading to a semi-automatic transformation process. In the second part, we propose the illustration of the architecture and methodology to the main case of transforming a PIM into PSM

    Business Level Service-Oriented Enterprise Application Integration

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    In this paper we propose a new approach for service-oriented enterprise application integration (EAI). Unlike current EAI solutions, which mainly focus on technological aspects, our approach allows business domain experts to get more involved in the integration process. First, we provide a technique for modeling application services at a sufficiently high level of abstraction for business experts to work with. Next, these business experts can model the orchestration as well as the information mappings that are required to achieve their integration goals. Our mediation framework then takes over and realizes the integration solution by transforming these models to existing service orchestration technology

    A unified view of data-intensive flows in business intelligence systems : a survey

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    Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

    Realising the open virtual commissioning of modular automation systems

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    To address the challenges in the automotive industry posed by the need to rapidly manufacture more product variants, and the resultant need for more adaptable production systems, radical changes are now required in the way in which such systems are developed and implemented. In this context, two enabling approaches for achieving more agile manufacturing, namely modular automation systems and virtual commissioning, are briefly reviewed in this contribution. Ongoing research conducted at Loughborough University which aims to provide a modular approach to automation systems design coupled with a virtual engineering toolset for the (re)configuration of such manufacturing automation systems is reported. The problems faced in the virtual commissioning of modular automation systems are outlined. AutomationML - an emerging neutral data format which has potential to address integration problems is discussed. The paper proposes and illustrates a collaborative framework in which AutomationML is adopted for the data exchange and data representation of related models to enable efficient open virtual prototype construction and virtual commissioning of modular automation systems. A case study is provided to show how to create the data model based on AutomationML for describing a modular automation system

    Challenges for Model-Driven Development of Strategically Aligned Information Systems

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    [EN] Model-Driven Development (MDD) has been proposed as an alternative to the traditional development of information systems, given its ability to integrate different stakeholders into the information system engineering process. Currently, longtime researched MDD methods and modern no-code and low-code platforms support the generation of the working code of the information system and services. However, in today's continuously changing environment, organisations need to align the information systems and services with the business structure, strategy, and processes they support. This article shows the design challenges for integrating business strategy information into a model-driven development method. We applied a set of mechanism experiments on an MDD method composed of three modelling frameworks with demonstrated semantic consistency, that covers the organisational, business process, and information system levels to identify information loss and transformation coverage issues that prevent the generation of information systems and services that are strategically aligned. The challenges were discussed with experts, confirming the relevance of avoiding the overlapping between the strategic and business process concepts, providing organisational-level constructs to express strategic ends and means, and considering the organisational structure in the modular design of business process and information systems and services.This work was supported in part by the Spanish State Research Agency and the Generalitat Valenciana under Project MICIN/AEI/10.13039/501100011033, Project GV/2021/072, and Project INNEST/2021/57 by Agencia Valenciana de Innovacion (AVI); in part by the European Regional Development Fund (ERDF), the European Union Next Generation, and Plan de Recuperacion, Transformacion y Resiliencia (PRTR); and in part by the National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Becas Chile under Grant 2020-72210494.Noel-Lopez, R.; Panach, JI.; Pastor López, O. (2022). Challenges for Model-Driven Development of Strategically Aligned Information Systems. IEEE Access. 10:38237-38253. https://doi.org/10.1109/ACCESS.2022.316222538237382531

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

    Web Data Extraction, Applications and Techniques: A Survey

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    Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

    Data integration through service-based mediation for web-enabled information systems

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    The Web and its underlying platform technologies have often been used to integrate existing software and information systems. Traditional techniques for data representation and transformations between documents are not sufficient to support a flexible and maintainable data integration solution that meets the requirements of modern complex Web-enabled software and information systems. The difficulty arises from the high degree of complexity of data structures, for example in business and technology applications, and from the constant change of data and its representation. In the Web context, where the Web platform is used to integrate different organisations or software systems, additionally the problem of heterogeneity arises. We introduce a specific data integration solution for Web applications such as Web-enabled information systems. Our contribution is an integration technology framework for Web-enabled information systems comprising, firstly, a data integration technique based on the declarative specification of transformation rules and the construction of connectors that handle the integration and, secondly, a mediator architecture based on information services and the constructed connectors to handle the integration process

    Business Capability Mining - Opportunities and Challenges

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    Business capability models are widely used in enterprise architecture management to generate an abstract overview of an organization’s business activities to reach its business objectives. The creation and maintenance of these models are associated with a huge manual workload. Research provides insights into opportunities for automated modeling of enterprise architecture models. However, most models address the application and technology layer and leave the business layer largely unexplored. Particularly, no research has been conducted on the automated generation of business capability models. This research paper uses 19 semi-structured expert interviews to identify possible automated modeling opportunities of business capabilities and related challenges and to jointly develop a business capability mining approach. This research benefit both, practice and research, by describing a situation-based business capability mining approach and identifying appropriate implementation scenarios
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