119,326 research outputs found

    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

    A Distributed Sensor Data Search Platform for Internet of Things Environments

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    Recently, the number of devices has grown increasingly and it is hoped that, between 2015 and 2016, 20 billion devices will be connected to the Internet and this market will move around 91.5 billion dollars. The Internet of Things (IoT) is composed of small sensors and actuators embedded in objects with Internet access and will play a key role in solving many challenges faced in today's society. However, the real capacity of IoT concepts is constrained as the current sensor networks usually do not exchange information with other sources. In this paper, we propose the Visual Search for Internet of Things (ViSIoT) platform to help technical and non-technical users to discover and use sensors as a service for different application purposes. As a proof of concept, a real case study is used to generate weather condition reports to support rheumatism patients. This case study was executed in a working prototype and a performance evaluation is presented.Comment: International Journal of Services Computing (ISSN 2330-4472) Vol. 4, No.1, January - March, 201

    Hypermedia-based discovery for source selection using low-cost linked data interfaces

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    Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness

    Interoperability in IoT through the semantic profiling of objects

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    The emergence of smarter and broader people-oriented IoT applications and services requires interoperability at both data and knowledge levels. However, although some semantic IoT architectures have been proposed, achieving a high degree of interoperability requires dealing with a sea of non-integrated data, scattered across vertical silos. Also, these architectures do not fit into the machine-to-machine requirements, as data annotation has no knowledge on object interactions behind arriving data. This paper presents a vision of how to overcome these issues. More specifically, the semantic profiling of objects, through CoRE related standards, is envisaged as the key for data integration, allowing more powerful data annotation, validation, and reasoning. These are the key blocks for the development of intelligent applications.Portuguese Science and Technology Foundation (FCT) [UID/MULTI/00631/2013

    A Graph-Based Web Services Discovery Framework for IoT EcoSystem

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    Nowadays, the Internet of Things (IoT) represents an important topic and research domain with multiple objectives. However, most IoTs communicate poorly across the multitude of network interfaces. It should be preferably used a single universal application layer protocol for the devices and services interconnection, regardless of how they are physically connected. The IoT paradigm boosts the device connectivity and the user accessibility benefits of services introduced within the network of connected objects associated with a context-awareness. Within this frame of reference, Web service is the appropriate technological approach to exhibit a set of related IoT functionalities loosely coupled with other services discovered or composed through the Web. In this work, we consider the heterogeneity of connecting technologies for IoT and the applications and devices integration in a single interoperable framework as a research objective. With this in mind, we introduce a five layers multigraph model for Web Services discovery and recommendation, and we address Web services-based applications for IoT data integration. The launched service discovery process permits the interaction between the user/application and the IoT environment. In this context, the choice of suitable services represents a challenge that covers the functionality and the required quality to combine composite services, namely mashups for IoT data management and interconnection. For proof of concept, we test a RESTful Web Services framework as an experimental platform to animate a graph-based approach for dynamic IoT services discovery. We develop a recommender system that performs graph analytics to produce a set of services according to the user's request. The quality of the recommendation process is evaluated by analyzing the correlation of user satisfaction
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