154,274 research outputs found

    Coordinating Large Distributed Process Structures

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    Representing a business process as interacting small processes has become feasible with data-centric business process management paradigms. These small processes have relations and, thereby, form a relational process structure. The interactions of processes within this relational process structure must be coordinated to arrive at a meaningful overall business goal. However, relational process structures may become arbitrarily large and, with cloud technology, they may additionally be distributed over multiple nodes. Coordination processes have been proposed to coordinate relational process structures, where processes have one-to-many and many-to-many relations at run-time. This paper shows how multiple coordination processes can be used in a decentralized fashion to coordinate large, distributed process structures. The main challenge is to effectively realize the coordination responsibility of each coordination process. Key components of the solution are the subsidiary principle and the hierarchy of the relational process structure. Moreover, from these key components and the technical properties of coordination processes, an implementation based on microservices was developed, which allows fast and concurrent enactment of multiple, decentralized coordination processes in large, distributed process structures

    Coordinating Large Distributed Relational Process Structures

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    Representing a business process as a collaboration of interacting processes has become feasible with the emergence of data-centric business process management paradigms. Usually, these interacting processes have relations and, thereby, form a complex relational process structure. The interactions of processes within this relational process structure need to be coordinated to arrive at a meaningful overall business goal. However, relational process structures may become arbitrarily large. With the use of cloud technology, they may additionally be distributed over multiple nodes, allowing for scalability. Coordination processes have been proposed to coordinate relational process structures, where processes may have one-to-many and many-to-many relations at run-time. This paper shows how multiple coordination processes can be used in a decentralized fashion to more efficiently coordinate large, distributed process structures. The main challenge of using multiple coordination processes is to effectively realize the coordination responsibility of each coordination process. Key components of the solution are the subsidiary principle and the hierarchy of the relational process structure. Finally, an implementation of the coordination process concept based on microservices was developed, which allows for fast and concurrent enactment of multiple, decentralized coordination processes in large, distributed process structures

    Towards structured sharing of raw and derived neuroimaging data across existing resources

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    Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery

    Evaluating distributed cognitive resources for wayfinding in a desktop virtual environment.

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    As 3D interfaces, and in particular virtual environments, become increasingly realistic there is a need to investigate the location and configuration of information resources, as distributed in the humancomputer system, to support any required activities. It is important for the designer of 3D interfaces to be aware of information resource availability and distribution when considering issues such as cognitive load on the user. This paper explores how a model of distributed resources can support the design of alternative aids to virtual environment wayfinding with varying levels of cognitive load. The wayfinding aids have been implemented and evaluated in a desktop virtual environment

    A customizable multi-agent system for distributed data mining

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    We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
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