@article {890, title = {Machine learning for internet of things data analysis: a survey}, journal = {Digital Communications and Networks}, volume = {4}, year = {2018}, pages = {161-175}, abstract = {

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.

}, keywords = {Internet of Things, Machine learning, Smart City, Smart data}, issn = {2352-8648}, doi = {https://doi.org/10.1016/j.dcan.2017.10.002}, url = {https://www.sciencedirect.com/science/article/pii/S235286481730247X}, author = {Mohammad Saeid Mahdavinejad and Mohammadreza Rezvan}, editor = {Mohammadamin Barekatain and Peyman Adibi and Payam Barnaghi and Amit P. Sheth} } @conference {404, title = {Representation of Parsimonious Covering Theory in OWL-DL}, booktitle = {Proceedings of the 8th International Workshop on OWL: Experiences and Directions {(OWLED} 2011), San Francisco, California, USA, June 5-6, 2011}, volume = {796}, year = {2011}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {San Francisco, California, USA}, abstract = {

The Web Ontology Language has not been designed for representing abductive inference, which is often required for applications such as medical disease diagnosis. As a consequence, existing OWL ontologies have limited ability to encode knowledge for such applications. In the last 150 years, many logic frameworks for the representation of abductive inference have been developed. Among these frameworks, Parsimonious Covering Theory (PCT) has achieved wide recognition. PCT is a formal model of diagnostic reasoning in which knowledge is represented as a network of causal associations, and whose goal is to account for observed symptoms with plausible explanatory hypotheses. In this paper, we argue that OWL does provide some of the expressivity required to approximate diagnostic reasoning, and outline a suitable encoding of PCT in OWL-DL.

}, author = {Cory A. Henson and Krishnaprasad Thirunarayan and Amit P. Sheth and Pascal Hitzler}, editor = {Michel Dumontier and M{\'e}lanie Courtot} } @conference {408, title = {Flexible Bootstrapping-Based Ontology Alignment}, booktitle = {Proceedings of the 5th International Workshop on Ontology Matching (OM-2010), Shanghai, China, November 7, 2010}, volume = {689}, year = {2010}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Shanghai, China}, abstract = {

BLOOMS (Jain et al, ISWC2010, to appear) is an ontology alignment system which, in its core, utilizes the Wikipedia category hierarchy for establishing alignments. In this paper, we present a Plug-and-Play extension to BLOOMS, which allows to flexibly replace or complement the use of Wikipedia by other online or offline resources, including domain-specific ontologies or taxonomies. By making use of automated translation services and of Wikipedia in languages other than English, it makes it possible to apply BLOOMS to alignment tasks where the input ontologies are written in different languages.

}, url = {http://ceur-ws.org/Vol-689/om2010_poster9.pdf}, author = {Prateek Jain and Pascal Hitzler and Amit P. Sheth}, editor = {Pavel Shvaiko and J{\'e}r{\^o}me Euzenat and Fausto Giunchiglia and Heiner Stuckenschmidt and Ming Mao and Isabel F. Cruz} }