By Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)
The name of this publication is a pun at the use of the preposition “onto” with the purpose of recalling “Ontology”, the time period in most cases followed within the laptop technology group to point the learn of the formal specification for organizing knowledge.
In the sector of data engineering, Ontologies are used for modeling ideas and relationships on a few domain.
The yr 2013 celebrates the 20 th anniversary of the area broad internet. the straightforward community of hypermedia has reworked the realm of communications with huge, immense implications at the social relationships. in spite of the fact that, conventional world-wide-web is presently experiencing a hard 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 potential of allowing extra effective machine-to-machine cooperation. To such target, ontologies symbolize the main compatible software to let move and comprehension of knowledge between laptop purposes, even these designed and built by means of unrelated humans in numerous places.
This booklet proposes a set of contributions illustrating varied functions following those instructions and which are the results of genuine reviews built within the context of analysis projects.
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Extra resources for Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful
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. 8). The effect of the uncertainty threshold variation on both the average number of recognition errors and on user intervention is shown in Fig. 9. 42 S. Gaglio and G. Martorella Fig. 7 Effect of variation in the uncertainty threshold on the average number of recognition errors Fig. 8 Effect of variation in the uncertainty threshold on the average number of user interventions 5 Conclusions The main goal of an AmI system in a home scenario is to assist users in their daily life and to improve their quality of life.
The inference process allows the system to identify hidden phenomena on the basis of observed events. For our purpose, we use the Viterbi algorithm  to infer activities from a sequence of sensory readings. On the basis of the HMM parameters, this inference algorithm gives as output the most likely sequence of hidden states generating the observed sequence of sensory events. Moreover, the Viterbi algorithm employs dynamic programming techniques to reduce the computational cost of the inference phase.
A profile is based on the prediction and the correction times. Each room contains sensors, each of which has an ID and an instant value. If another system has the same representation of these concepts, interoperability is easily ensured. Moreover, this formal concept representation could allow a higher-level AmI system to enrich its knowledge base. An AMI System for User Daily Routine Recognition and Prediction 41 Fig. 6 Comparison between a recognizer with the adaptive capability and a recognizer without this functionality.
Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful by Alessandra De Paola (auth.), Salvatore Gaglio, Giuseppe Lo Re (eds.)