15,301 research outputs found

    An ontology-based collaborative business service selection: contributing to automatic building of collaborative business process

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    International audienceWith worldwide inter-enterprise collaboration and interoperability background, automatic collaborative business process deduction is a crucial researching subject. We have designed a methodology of deducing collaborative process by only collecting collaborative objectives and partners’ business services. The two key problems are (i) selecting corresponding business services for a set of collaborative objectives and (ii) ordering business services with serializations and parallelization. This paper aims to present a solution of business service selection and the following business process extraction. In order to solve the problem, we have defined a collaborative ontology, which contains numerous instances of business services and processes from the MIT process handbook. The collaborative ontology contains essential concepts in collaborative situations and process-deducing rules and algorithms. We provide a brief illustration of implementation within a SaaS toolkit called Mediator Modeling 2ool

    Value-driven partner search for <i>Energy from Waste</i> projects

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    Energy from Waste (EfW) projects require complex value chains to operate effectively. To identify business partners, plant operators need to network with organisations whose strategic objectives are aligned with their own. Supplier organisations need to work out where they fit in the value chain. Our aim is to support people in identifying potential business partners, based on their organisation’s interpretation of value. Value for an organisation should reflect its strategy and may be interpreted using key priorities and KPIs (key performance indicators). KPIs may comprise any or all of knowledge, operational, economic, social and convenience indicators. This paper presents an ontology for modelling and prioritising connections within the business environment, and in the process provides means for defining value and mapping these to corresponding KPIs. The ontology is used to guide the design of a visual representation of the environment to aid partner search

    Autonomic Management of Networked Small-Medium Factories

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    The Chapter reports the achievements of a research project that is developing a software platform with a suite of autonomic services enabling every company in the network to move from a situation where it wastes valuable resources in struggling with its customers and suppliers, towards a rational business environment where communication becomes faster, and operation and collaboration more efficient. The ultimate objective of the project is to set-up, develop, experiment and promote the adoption of a new collaboration practice within networked factories taking advantage of the autonomic model applied to a suite of support software services

    Automatic detection of accommodation steps as an indicator of knowledge maturing

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    Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

    Framework for collaborative knowledge management in organizations

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    Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications. Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic

    Collaborative Knowledge Framework for Mediation Information System Engineering

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    With the worldwide interenterprise collaboration and interoperability background, automatic collaborative business process deduction is crucial and imperative researching subject. A methodology of deducing collaborative process is designed by collecting collaborative knowledge. Due to the complexity of deduction methodology, a collaborative knowledge framework is defined to organize abstract and concrete collaborative information. The collaborative knowledge framework contains three dimensions: elements, levels, and life cycle. To better define the framework, the relations in each dimension are explained in detail. They are (i) relations among elements, which organize the gathering orders and methods of different collaborative elements, (ii) relations among life cycle, which present modeling processes and agility management, and (iii) relations among levels, which define relationships among different levels of collaborative processes: strategy, operation, and support. This paper aims to explain the collaborative knowledge framework and the relations inside

    CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

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    After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges
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