By Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)
The identify of this publication is a pun at the use of the preposition “onto” with the purpose of recalling “Ontology”, the time period usually followed within the machine technological know-how group to point the learn of the formal specification for organizing knowledge.
In the sector of data engineering, Ontologies are used for modeling thoughts and relationships on a few domain.
The yr 2013 celebrates the 20th anniversary of the realm vast internet. the easy community of hypermedia has reworked the area of communications with huge, immense implications at the social relationships. even if, conventional world-wide-web is at the moment experiencing a demanding evolution towards the net of items (IoT), this present day possible because of the mixing of pervasive applied sciences in a position to sensing the environment.
The most vital contribution of IoT regards the opportunity of permitting extra effective machine-to-machine cooperation. To such goal, ontologies symbolize the main compatible instrument to allow move and comprehension of knowledge between laptop functions, even these designed and built via unrelated humans in numerous places.
This ebook proposes a suite of contributions illustrating various purposes following those instructions and which are the results of genuine reports constructed within the context of study projects.
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Extra resources for Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful
After the preprocessing of the data we suggest the use of a probabilist model to estimate if the detected changes in sensor measurements can be attributed to users’ actions. The choice of using such a model depends on the fact that we are bound to reason about uncertain knowledge. The interest variables and their possible states are modelled as nodes of a dynamic Bayesian network. The nodes are connected with directed links representing the influence famong the nodes where the influence of parent nodes on a variable X i is quantified by means of conditional probabilities P(X i |Par entsV alues) represented in opportune tables associated to each node.
1 Pre-Processing At this phase, the observed variables trends are analyzed to recognize those events that can be ascribed to human intervention. First of all the data collected by the sensors are preprocessed in order to to detect anomalies, remove invalid values and estimate missing values. For example the assumption of a time correlation in the data can be exploited for the estimation of missing values by means of a linear interpolation between preceding and subsequent observations, while the assumption of spatial correlation in the readings of sensors located in small indoor environments can be profitably employed in order to detect outliers, and to replace them with the combination of neighbour sensors readings.
Two parts were used as a training set for the HMM, while the remaining one was used for verification. The system is able to detect activities with a minimum duration of 10 s and it updates the parameters whenever the recognizer uncertainty exceeds a threshold fixed at 20 %. The experimental evaluation revealed that adaptive behavior makes the proposed recognizer more precise, as shown in Fig. 6. The uncertainty threshold also affects the average number of recognition errors (see Fig. 7). By increasing this threshold, average recognition errors also go up because the system will question the user less frequently and will consequently correct the inference errors less frequently (see Fig.