José Angel Carvajal Soto
ABSTRACT
The Internet of Things (IoT) is a growing field which is expected to generate and collect data everywhere at any time. Highly scalable cloud analytics systems are frequently being used to handle this data explosion. However, the ubiquitous nature of the IoT data imposes new technical and non-technical requirements which are difficult to address with a cloud deployment. To solve these problems, we need a new set of development technologies such as Distributed Data Mining and Ubiquitous Data Mining targeted and optimized towards IoT applications. In this paper, we present the Complex Event Machine Learning framework which proposes a set of tools for automatic distributed machine learning in (near-) real-time, automatic continuous evaluation tools, and automatic rules management for deployment of rules. These features are implemented for a deployment at the edge of the network instead of the cloud. We evaluate and validate our approach with a well-known classification problem.
- 2010. Growing Pains: Bandwidth on the Internet. Technical Report. Internet Society. 20 pages. http://www.internetsociety.org/sites/default/files/Growing%20Pains-%20Bandwidth%20on%20the%20Internet.pdfGoogle Scholar
- 2011. More than 50 billion connected devices. Technical Report 284 23-3149 Uen. Telefonaktiebolaget L. M. Ericsson. 12 pages. http://www.ericsson.com/openarticle/mwc-connected-devices_1686565587_cGoogle Scholar
- 2014. MQTT Version 3.1.1. (2014). http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/mqtt-v3.1.1.html Accessed: March, 2016.Google Scholar
- Charu C Aggarwal. 2007. Data streams: models and algorithms. Vol. 31.Google Scholar
- Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan. 2003. An introduction to MCMC for machine learning. Machine learning 50, 1-2 (2003), 5--43.Google Scholar
- Plamen Angelov, Dimitar Filev, and Nikola Kasabov. 2008. Guest Editorial: Evolving Fuzzy Systems: preface to the special section. IEEE Transactions on Fuzzy Systems 16, 6 (2008), 1390--1392. Google ScholarDigital Library
- Dario Bonino, Maria Teresa Delgado Alizo, Alexandr Alapetite, Thomas Gilbert, Mathias Axling, Helene Udsen, Jose Angel Carvajal Soto, and Maurizio Spirito. 2015. ALMANAC: Internet of Things for Smart Cities. In The 3rd International Conference on Future Internet of Things and Cloud (FiCloud2015). IEEE. Google ScholarDigital Library
- Danah Boyd and Kate Craford. 2011. Six Provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society (2011).Google Scholar
- Michael C Burl, Charless Fowlkes, Joe Roden, Andre Stechert, and Saleem Mukhtar. 1999. Diamond Eye: A distributed architecture for image data mining. In AeroSense'99. International Society for Optics and Photonics, 197--206.Google Scholar
- José Ángel Carvajal Soto, Otilia Werner-Kytölä, Marco Jahn, Jaroslav Pullmann, Dario Bonino, Claudio Pastrone, and Maurizio Spirito. 2016. Towards a Federation of Smart City Services. Proceeding of International Conference on Recent Advances in Computer Systems (2016).Google ScholarCross Ref
- Carvajal Soto, José Ángel. 2015. LinkSmart IoT Learning Agent. (2015). https://linksmart.eu/redmine/projects/iot-data-processing-agentGoogle Scholar
- Gianpaolo Cugola and Alessandro Margara. 2012. Processing flows of information: From data stream to complex event processing. ACM Computing Surveys (CSUR) 44, 3 (2012), 15. Google ScholarDigital Library
- A Philip Dawid. 1984. Present position and potential developments: Some personal views: Statistical theory: The prequential approach. Journal of the Royal Statistical Society. Series A (General) (1984), 278--292.Google Scholar
- Espertech Inc. 2006. Esper. (2006). http://www.espertech.com/ Accessed: March, 2016.Google Scholar
- Dave Evans. 2011. The Internet of Things: How the Next Evolution of the Internet Is Changing Everything. Technical Report. Cisco Systems, Inc. 10 pages. http://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdfGoogle Scholar
- Mohamed Gaber, Shonali Krishnaswamy, and Arkady Zaslavsky. 2004. Ubiquitous data stream mining. In Proceedings of Current Research and Future Directions Workshop Proceedings, held in conjunction with the Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining.Google Scholar
- Mohamed Medhat Gaber, Shonali Krishnaswamy, and Arkady Zaslavsky. 2005a. Advanced Methods for Knowledge Discovery from Complex Data. London, Chapter On-board Mining of Data Streams in Sensor Networks, 307--335.Google Scholar
- Mohamed Medhat Gaber, Arkady Zaslavsky, and Shonali Krishnaswamy. 2005b. Mining Data Streams: A Review. SIGMOD Rec. 34, 2 (June 2005), 18--26. Google ScholarDigital Library
- Joao Gama. 2010. Knowledge discovery from data streams. CRC Press. Google ScholarDigital Library
- Manyika James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. 2011. Report: Big data: The next frontier for innovation, competition, and productivity. Technical Report. McKinsey Global Institute. 156 pages. http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Business%20Technology/Our%20Insights/Big%20data%20The%20next%20frontier%20for%20innovation/MGI_big_data_full_report.ashxGoogle Scholar
- Stephen Kaisler, Frank Armour, Juan Antonio Espinosa, and William Money. 2013. Big data: Issues and challenges moving forward. In System Sciences (HICSS), 2013 46th Hawaii International Conference on. IEEE, 995--1004. Google ScholarDigital Library
- Hillol Kargupta, Byung-Hoon Park, Sweta Pittie, Lei Liu, Deepali Kushraj, and Kakali Sarkar. 2002. MobiMine: Monitoring the stock market from a PDA. ACM SIGKDD Explorations Newsletter 3, 2 (2002), 37--46. Google ScholarDigital Library
- Stamatis Karnouskos. 2011. Cyber-Physical Systems in the SmartGrid. In 2011 9th IEEE International Conference on Industrial Informatics (INDIN). IEEE, 20--23.Google Scholar
- Machine Learning Group at the University of Waikato. 1993. Weka. (1993). http://www.cs.waikato.ac.nz/ml/weka/ Accessed: March, 2016.Google Scholar
- Aapo Markkanen. 2015. IoT Analytics Today and in 2020. Technical Report. Allied Business Intelligence, Inc. 10 pages. http://www.sigma-software.ca/wp-content/uploads/2015/07/ABI_Research_IoT-Analytics-Today-and-in-2020.pdfGoogle Scholar
- Open Geospatial Consortium. 2015. OGC SensorThings API. (2015). https://ogc-iot.github.io/ogc-iot-api/Google Scholar
- Byung-Hoon Park and Hillol Kargupta. 2002a. Distributed Data Mining: Algorithms, Systems, and Applications. Oxford Internet Institute's, 341--358.Google Scholar
- Byung-Hoon Park and Hillol Kargupta. 2002b. Distributed data mining: Algorithms, systems, and applications. (2002).Google Scholar
- Davy Preuveneers and Yolande Berbers. 2014. SAMURAI: A Streaming Multi-tenant Context-Management Architecture for Intelligent and Scalable Internet of Things Applications. In Intelligent Environments (IE), 2014 International Conference on. IEEE, 226--233. Google ScholarDigital Library
- Project Coordinator: Elisabeth Ilie-Zudor. 2016. EXCELL Project. (2016). http://excell-project.eu/Google Scholar
- Project Coordinator: Markus Eisenhauer. 2013. IMPReSS Project. (2013). http://impress-project.eu/Google Scholar
- Project Coordinator: Maurizio Spirito. 2013. ALMANAC Project. (2013). http://almanac-project.eu/Google Scholar
- Burr Settles. 2009. Active Learning Literature Survey. Computer Sciences Technical Report 1648. University of Wisconsin--Madison.Google Scholar
- skymind. 2016. Deep Learning 4j. (2016). http://deeplearning4j.org/Google Scholar
- Stephen V. Stehman. 1997. Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment 62, 1 (1997), 77--89. DOI: http://dx.doi.org/10.1016/S0034-4257(97)00083-7Google ScholarCross Ref
- Nadeem Ahmed Syed, Syed Huan, Liu Kah, and Kay Sung. 1999. Incremental learning with support vector machines. (1999).Google Scholar
- Steve Tanner, Mohammad Alshayeb, E Criswell, M Iyer, A McDowell, M McEniry, and K Regner. 2002. EVE: On-board process planning and execution. In Earth Science Technology Conference, Pasadena, CA, Vol. 39.Google Scholar
- UC Irvine Machine Learning Repository. 1936. Iris Data Set. (1936). https://archive.ics.uci.edu/ml/datasets/Iris Accessed: December, 2015.Google Scholar
- Ina Wagner. 1999. Ethical issues of healthcare in the information society. Opinion of the European Group on Ethics in Science and New Technologies No. 13, 30 July 1999. (1999). http://aei.pitt.edu/43228/Google Scholar
- EMC Digital Universe with Research & Analysis by IDC. 2014. The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Thingss. Technical Report. http://www.emc.com/leadership/digital-universe/2014iview/digital-universe-of-opportunities-vernon-turner.htmGoogle Scholar
- WSO2. 2015. WSO2 Complex Event Processor. (2015). http://wso2.com/products/complex-event-processor/Google Scholar
- Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, and Graham Holmes. 2014. Active learning with drifting streaming data. Neural Networks and Learning Systems, IEEE Transactions on 25, 1 (2014), 27--39.Google Scholar
Index Terms
- CEML: Mixing and moving complex event processing and machine learning to the edge of the network for IoT applications
Index terms have been assigned to the content through auto-classification.
Recommendations
Enhancing Support for Machine Learning and Edge Computing on an IoT Data Marketplace
AIChallengeIoT'19: Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of ThingsIoT applications are increasingly employing machine learning (ML) algorithms to manage and control the operational environment autonomously while predicting future actions. To leverage these emerging technologies, the application developers require an ...
Cybersecurity Challenges and Opportunities in the New "Edge Computing + IoT" World
SDN-NFV Sec'18: Proceedings of the 2018 ACM International Workshop on Security in Software Defined Networks & Network Function VirtualizationThe paradigm shift to the Internet of Things (IoT) and the emergence of the edge computing concept have brought huge potentials for various future IoT application scenes such as smart home, smart transportation, smart health, smart grids, and smart ...
A top-down survey on securing IoT with machine learning: goals, recent advances and challenges
The Internet of Things (IoT) has seen it all from being just another innovation to a leading technology; it is now a binding force that interconnects various aspects of our lives. The IoT's tremendous growth is driven by emerging applications and evolving ...
Comments