Reference : Beyond Discrete Modeling: A Continuous and Efficient Model for IoT

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/22330

Title :	Beyond Discrete Modeling: A Continuous and Efficient Model for IoT
Language :	English
Author, co-author :	Moawad, Assaad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Hartmann, Thomas [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Fouquet, François [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Nain, Grégory [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Sep-2015
Main document title :	2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)
Editor :	Lethbridge, Timothy
	Cabot, Jordi
	Egyed, Alexander
Publisher :	Conference Publishing Consulting
Pages :	90-99
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-1-4673-6907-7
City :	Passau
Country :	Germany
Event name :	ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)
Event date :	30-09-2015 to 02-10-2015
Event organizer :	University of Ottawa, Canada
Event place (city) :	Ottawa
Event country :	Canada
Keywords :	[en] IoT ; Continuous modeling ; Discrete modeling ; Polynomial ; Extrapolation ; Big Data
Abstract :	[en] Internet of Things applications analyze our past habits through sensor measures to anticipate future trends. To yield accurate predictions, intelligent systems not only rely on single numerical values, but also on structured models aggregated from different sensors. Computation theory, based on the discretization of observable data into timed events, can easily lead to millions of values. Time series and similar database structures can efficiently index the mere data, but quickly reach computation and storage limits when it comes to structuring and processing IoT data. We propose a concept of continuous models that can handle high-volatile IoT data by defining a new type of meta attribute, which represents the continuous nature of IoT data. On top of traditional discrete object-oriented modeling APIs, we enable models to represent very large sequences of sensor values by using mathematical polynomials. We show on various IoT datasets that this significantly improves storage and reasoning efficiency.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/22330

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