13,577 research outputs found

    trackr: A Framework for Enhancing Discoverability and Reproducibility of Data Visualizations and Other Artifacts in R

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    Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their own. We present the trackr framework for organizing, automatically annotating, discovering, and retrieving results. We identify sources of automatically extractable metadata for computational results, and we define an extensible system for organizing, annotating, and searching for results based on these and other metadata. We present an open-source implementation of these concepts for plots, computational artifacts, and woven dynamic reports generated in the R statistical computing language

    Taxonomies for Development

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    {Excerpt} Organizations spend millions of dollars on management systems without commensurate investments in the categorization needed to organize the information they rest on. Taxonomy work is strategic work: it enables efficient and interoperable retrieval and sharing of data, information, and knowledge by building needs and natural workflows in intuitive structures. Bible readers think that taxonomy is the worldā€™s oldest profession. Whatever the case, the word is now synonymous with any hierarchical system of classification that orders domains of inquiry into groups and signifies natural relationships among these. (A taxonomic scheme is often depicted as a ā€œtreeā€ and individual taxonomic units as ā€œbranchesā€ in the tree.) Almost anything can be classified according to some taxonomic scheme. Resulting catalogs provide conceptual frameworks for miscellaneous purposes including knowledge identification, creation, storage, sharing, and use, including related decision making

    Situational Enterprise Services

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    The ability to rapidly find potential business partners as well as rapidly set up a collaborative business process is desirable in the face of market turbulence. Collaborative business processes are increasingly dependent on the integration of business information systems. Traditional linking of business processes has a large ad hoc character. Implementing situational enterprise services in an appropriate way will deliver the business more flexibility, adaptability and agility. Service-oriented architectures (SOA) are rapidly becoming the dominant computing paradigm. It is now being embraced by organizations everywhere as the key to business agility. Web 2.0 technologies such as AJAX on the other hand provide good user interactions for successful service discovery, selection, adaptation, invocation and service construction. They also balance automatic integration of services and human interactions, disconnecting content from presentation in the delivery of the service. Another Web technology, such as semantic Web, makes automatic service discovery, mediation and composition possible. Integrating SOA, Web 2.0 Technologies and Semantic Web into a service-oriented virtual enterprise connects business processes in a much more horizontal fashion. To be able run these services consistently across the enterprise, an enterprise infrastructure that provides enterprise architecture and security foundation is necessary. The world is constantly changing. So does the business environment. An agile enterprise needs to be able to quickly and cost-effectively change how it does business and who it does business with. Knowing, adapting to diffident situations is an important aspect of todayā€™s business environment. The changes in an operating environment can happen implicitly and explicitly. The changes can be caused by different factors in the application domain. Changes can also happen for the purpose of organizing information in a better way. Changes can be further made according to the users' needs such as incorporating additional functionalities. Handling and managing diffident situations of service-oriented enterprises are important aspects of business environment. In the chapter, we will investigate how to apply new Web technologies to develop, deploy and executing enterprise services

    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
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