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      Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

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          Abstract

          The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.

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          Most cited references127

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          A view of cloud computing

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            Survey of clustering algorithms.

            Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
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              Data mining in bioinformatics using Weka.

              The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.
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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                11 July 2016
                July 2016
                : 16
                : 7
                : 1069
                Affiliations
                [1 ]Department of Computing Technology and Data Processing, University of Alicante, Alicante 03690, Spain; hmora@ 123456ua.es
                [2 ]Department of Software and Computing Systems, University of Alicante, Alicante 03690, Spain; antonio@ 123456dlsi.ua.es (A.F.); jperal@ 123456dlsi.ua.es (J.P.)
                Author notes
                [* ]Correspondence: david.gil@ 123456ua.es ; Tel.: +34-96-590-3681
                Article
                sensors-16-01069
                10.3390/s16071069
                4970116
                27409623
                a7adfb04-eaec-4986-85d2-5c4de725fc81
                © 2016 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 April 2016
                : 08 July 2016
                Categories
                Review

                Biomedical engineering
                internet of things,big data,ontology,semantics,data mining with big data,services for big data,social internet of things,cloud computing

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