02311nas a2200241 4500008004100000022001400041245006800055210006700123300001200190490000600202520156900208653002301777653002101800653001501821653001501836100003401851700002501885700002901910700001801939700002001957700002001977856007201997 2018 eng d a2352-864800aMachine learning for internet of things data analysis: a survey0 aMachine learning for internet of things data analysis a survey a161-1750 v43 a

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.

10aInternet of Things10aMachine learning10aSmart City10aSmart data1 aMahdavinejad, Mohammad, Saeid1 aRezvan, Mohammadreza1 aBarekatain, Mohammadamin1 aAdibi, Peyman1 aBarnaghi, Payam1 aSheth, Amit, P. uhttps://www.sciencedirect.com/science/article/pii/S235286481730247X01489nas a2200181 4500008004100000245006100041210006000102260004800162490000800210520085700218100002101075700003201096700002001128700002001148700002201168700002201190856009501212 2011 eng d00aRepresentation of Parsimonious Covering Theory in OWL-DL0 aRepresentation of Parsimonious Covering Theory in OWLDL aSan Francisco, California, USAbCEUR-WS.org0 v7963 a

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.

1 aHenson, Cory, A.1 aThirunarayan, Krishnaprasad1 aSheth, Amit, P.1 aHitzler, Pascal1 aDumontier, Michel1 aCourtot, Mélanie uhttps://daselab.cs.ksu.edu/publications/representation-parsimonious-covering-theory-owl-dl01253nas a2200217 4500008004100000245005200041210005100093260003300144490000800177520061500185100001800800700002000818700002000838700001900858700002200877700002400899700002700923700001400950700002100964856005000985 2010 eng d00aFlexible Bootstrapping-Based Ontology Alignment0 aFlexible BootstrappingBased Ontology Alignment aShanghai, ChinabCEUR-WS.org0 v6893 a

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.

1 aJain, Prateek1 aHitzler, Pascal1 aSheth, Amit, P.1 aShvaiko, Pavel1 aEuzenat, Jérôme1 aGiunchiglia, Fausto1 aStuckenschmidt, Heiner1 aMao, Ming1 aCruz, Isabel, F. uhttp://ceur-ws.org/Vol-689/om2010_poster9.pdf