ABSTRACT
As IoT envisions a future world where a tremendous amount of sensor data will become available, the significance of extracting valuable knowledge out of those data is increasing day by day. IoT analytics are considered a powerful tool towards demystifying user behavior, providing market insights and intelligence in Industry 4.0 as well as discovering useful patterns in everyday phenomena. At the same time, a shift is observed to Service-Oriented-Infrastructure. In this work, a Knowledge as a Service (KnaaS) framework is proposed along with its prototype implementation architecture aiming at providing a conceptual reference architecture for the knowledge discovery in the future IoT. The discussion and analysis show that the proposed framework is in accordance with the best practices in knowledge discovery and IoT consisting a reasonable solution in offering knowledge as a service in the upcoming IoT era.
- 2016. bIoTope Deliverable: D4.2 Knowledge Representation and Inference Framework. (2016). https://goo.gl/e6qMwiGoogle Scholar
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, and others. 2016. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). Savannah, Georgia, USA. Google ScholarDigital Library
- Marco Aiello, Ian Pratt-Hartmann, Johan van Benthem, and others. 2007. Handbook of spatial logics. Vol. 4. Springer. Google ScholarDigital Library
- Pierpaolo Battigalli and Giacomo Bonanno. 1999. Recent results on belief, knowledge and the epistemic foundations of game theory. Research in Economics 53, 2 (1999), 149--225.Google ScholarCross Ref
- James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio. 2010. Theano: A CPU and GPU math compiler in Python. In Proc. 9th Python in Science Conf. 1--7.Google ScholarCross Ref
- Giacomo Bonanno. 2002. Information, knowledge and belief. Bulletin of economic research 54, 1 (2002), 47--67.Google Scholar
- Léon Bottou. 2014. From machine learning to machine reasoning. Machine learning 94, 2 (2014), 133--149. Google ScholarDigital Library
- Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V Vasilakos, and Xiaohui Rong. 2015. Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks (2015). Google ScholarDigital Library
- Li Da Xu, Wu He, and Shancang Li. 2014. Internet of things in industries: A survey. IEEE Transactions on industrial informatics 10, 4 (2014), 2233--2243.Google ScholarCross Ref
- Shoumen Palit Austin Datta. 2016. Emergence of Digital Twins. arXiv preprint arXiv:1610.06467 (2016).Google Scholar
- Peter A Flach and Antonis M Hadjiantonis. 2013. Abduction and Induction: Essays on their relation and integration. Vol. 18. Springer Science & Business Media.Google Scholar
- Carl R Hausman. 1993. Charles S. Peirce's Evolutionary Philosophy. Cambridge: Cambridge UP (1993).Google Scholar
- Jaakko Hintikka. 1962. Knowledge and belief: an introduction to the logic of the two notions. Vol. 4. Cornell University Press Ithaca.Google Scholar
- Antonio J Jara, Dominique Genoud, and Yann Bocchi. 2015. Big data for smart cities with KNIME a real experience in the SmartSantander testbed. Software: Practice and Experience 45, 8 (2015), 1145--1160. Google ScholarDigital Library
- Dimitris Kiritsis. 2013. Semantic technologies for engineering asset life cycle management. International Journal of Production Research 51, 23-24 (2013), 7345--7371.Google ScholarCross Ref
- Ron Kohavi and Foster Provost. 1998. Glossary of terms. Machine Learning 30, 2-3 (1998), 271--274. Google ScholarDigital Library
- Joan Littlefield-Cook, Greg Cook, Laura E Berk, and Helen Bee. 2005. Child development: Principles and perspectives. Vol. 55. Allyn and Bacon.Google Scholar
- Peter Mell, Tim Grance, and others. 2011. The NIST definition of cloud computing. (2011).Google Scholar
- M. Mikusz, S. Clinch, R. Jones, M. Harding, C. Winstanley, and N. Davies. 2015. Repurposing Web Analytics to Support the IoT. Computer 48, 9 (Sept 2015), 42--49.Google ScholarDigital Library
- Ana Milicic, Soumaya El Kadiri, Joerg Clobes, and Dimitris Kiritsis. 2017. An autonomous system for PLM domain data exploitation. International Journal of Computer Integrated Manufacturing 30, 1 (2017), 109--120. Google ScholarDigital Library
- Julien Mineraud, Oleksiy Mazhelis, Xiang Su, and Sasu Tarkoma. 2016. A Gap Analysis of Internet-of-Things Platforms. Comput. Commun. 89, C (Sept. 2016), 5--16. Google ScholarDigital Library
- Andres Munoz. 2014. Machine Learning and Optimization. URL: https://www.cims.nyu.edu/~munoz/files/ml_optimization.pdf {accessed 2016-03-02}{WebCite Cache ID 6fiLfZvnG} (2014).Google Scholar
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, and others. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, Oct (2011), 2825--2830. Google ScholarDigital Library
- Charith Perera, Chi Harold Liu, and Srimal Jayawardena. 2015. The emerging internet of things marketplace from an industrial perspective: A survey. IEEE Transactions on Emerging Topics in Computing 3, 4 (2015), 585--598. Google ScholarDigital Library
- Max Schmachtenberg, Christian Bizer, Anja Jentzsch, and Richard Cyganiak. 2014. Linking open data cloud diagram 2014. The Linking Open Data cloud diagram (2014).Google Scholar
- Vladimir N Vapnik. 2000. Methods of pattern recognition. In The nature of statistical learning theory. Springer, 123--180.Google ScholarDigital Library
- Stéfan van der Walt, S Chris Colbert, and Gael Varoquaux. 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering 13,2 (2011), 22--30. Google ScholarDigital Library
- Xindong Wu, Huanhuan Chen, Gongqing Wu, Jun Liu, Qinghua Zheng, Xiaofeng He, Aoying Zhou, Zhong-Qiu Zhao, Bifang Wei, Ming Gao, and others. 2015. Knowledge engineering with big data. IEEE Intelligent Systems 30, 5 (2015), 46--55.Google ScholarCross Ref
- Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding. 2014. Data mining with big data. ieee transactions on knowledge and data engineering 26, 1 (2014), 97--107. Google ScholarDigital Library
- Xun Xu. 2012. From cloud computing to cloud manufacturing. Robotics and computer-integrated manufacturing 28, 1 (2012), 75--86. Google ScholarDigital Library
Index Terms
- Knowledge as a service in the IoT era
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