Model-driven data-intensive Enterprise Information Systems

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Ontology-based model-driven patterns for notification-oriented data-intensive enterprise information systems

International audienceIn the fourth industrial revolution, the current Enterprise Information Systems (EIS) are facing a set of new challenges raised by the applications of Cyber-Physical Systems (CPS) and Internet of Things (IoT). In this scenario, a data-intensive EIS involves networks of physical objects with sensing, data collection, transmission and actuation capabilities, and vast endpoints in the cloud, thereby offering large amounts of data. Such systems can be considered as a multidisciplinary complex system with strong interrelations between the involved components. In order to cope with the big heterogeneousness of those physical objects and their intrinsic information, the authors propose a notification-based approach derived from the so-called Notification Oriented Paradigm (NOP), a new rule and event driven approach for software and hardware specification and execution. However, the heterogeneity of those information and their interpretation relatively to an evolving context impose the definition of model-driven patterns based on some formal knowledge modelled by a set of skill-based ontologies. Thus, the paper focuses on the open issue related to the formalisation of such ontology-based patterns for their verification, ensuring the coherence of the whole set of data in each contextual engineering domain involved in the EIS

Coordinating negotiations in data-intensive collaborative working environments using an agent-based model-driven platform

This paper tackles the interoperability problems of enterprise information systems by presenting a distributive model-driven platform for parallel coordination of multiple negotiations in data-intensive collaborative working environments. The proposed model was validated and verified by an industrial application scenario within the European research project H2020 C2NET (Cloud Collaborative Manufacturing Networks). This real scenario developed data-intensive collaborative and cloud-enabled tools that allow the optimisation of the supply network of manufacturing SMEs, proposing a negotiation solution based on a model-driven interoperable decentralised architecture.info:eu-repo/semantics/acceptedVersio

Designing Traceability into Big Data Systems

Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems need to be designed from the outset to support usage of such Items across the spectrum of business use rather than from any specific application view. The design philosophy advocated in this paper is to drive the design process using a so-called description-driven approach which enriches models with meta-data and description and focuses the design process on Item re-use, thereby promoting traceability. Details are given of the description-driven design of big data systems at CERN, in health informatics and in business process management. Evidence is presented that the approach leads to design simplicity and consequent ease of management thanks to loose typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore July 2015. arXiv admin note: text overlap with arXiv:1402.5764, arXiv:1402.575

Designing Reusable Systems that Can Handle Change - Description-Driven Systems : Revisiting Object-Oriented Principles

In the age of the Cloud and so-called Big Data systems must be increasingly flexible, reconfigurable and adaptable to change in addition to being developed rapidly. As a consequence, designing systems to cater for evolution is becoming critical to their success. To be able to cope with change, systems must have the capability of reuse and the ability to adapt as and when necessary to changes in requirements. Allowing systems to be self-describing is one way to facilitate this. To address the issues of reuse in designing evolvable systems, this paper proposes a so-called description-driven approach to systems design. This approach enables new versions of data structures and processes to be created alongside the old, thereby providing a history of changes to the underlying data models and enabling the capture of provenance data. The efficacy of the description-driven approach is exemplified by the CRISTAL project. CRISTAL is based on description-driven design principles; it uses versions of stored descriptions to define various versions of data which can be stored in diverse forms. This paper discusses the need for capturing holistic system description when modelling large-scale distributed systems.Comment: 8 pages, 1 figure and 1 table. Accepted by the 9th Int Conf on the Evaluation of Novel Approaches to Software Engineering (ENASE'14). Lisbon, Portugal. April 201

Knowledge Management for Public Administrations: Technical Realizations of an Enterprise Attention Management System

The improvement of governments’ efficiency has gained great importance and validity especially in the current times of economic downturn. E-Government constitutes the most contemporary techno-managerial proposition in the track of possible interventions. The paper addresses, more specifically, empowerments necessitated by Public Administration (PA) organizations. Anchored on the needs of three real-life cases, the paper describes the conception and the realization of an IT artefact together with its methodological appeals aiming at improving information access and delivery and thus PAs’ decision making capacity. Our proposition constitutes a novel approach for managing users’ attention in knowledge intensive organizations which goes beyond informing a user about changes in relevant information towards proactively supporting the user to react on changes. The approach is based on an expressive attention model, which is realized by combining ECA (Event-Condition-Action) rules with ontologies. The technical realizations described in the paper constitute the underlying infrastructure of an Enterprise Attention Management System

Business Models and their Implications for Skills

The dominant political-economic narrative of our time is that, under conditions of global competition with low-wage economies able to undercut even efficient western firms, the only viable and sustainable route to competitiveness is to trade on high value-added goods and services and that these in turn require enhanced skills and knowledge. This kind of analysis finds echo and sustenance in the management literature concerning 'knowledge'. Drawing upon a series of case studies this monograph reveals a more varied and complex pattern of possibilities

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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