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SenseQ: Creating relationships between objects to answer questions of humans by using Social Networks

Published: 15 August 2016 Publication History

Abstract

Social Networks are a source of information very use to know what happen around us. While, the Internet of Things is a world that tries to measure everything by gathering data from sensors and interconnect different objects. Why not combine both? In this paper, we propose the creation of a system that gathers information from users' sensors, processes it and exposes this information to Social Networks through questions that users ask to the system. Users will be able to ask for data available in distributed sensors using a Social Network and our system will answer to the users also through the Social Network. In order to gather the data from sensors and combine them, we propose three different type of relationship between sensors: neighboring relationship, familiar relationship and work relationship.

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  • (2017)Social Recommender SystemProceedings of the 4th Multidisciplinary International Social Networks Conference10.1145/3092090.3092121(1-7)Online publication date: 17-Jul-2017

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MISNC, SI, DS 2016: Proceedings of the The 3rd Multidisciplinary International Social Networks Conference on SocialInformatics 2016, Data Science 2016
August 2016
371 pages
ISBN:9781450341295
DOI:10.1145/2955129
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 15 August 2016

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Author Tags

  1. Sensors
  2. Social Networks
  3. The Internet of Things
  4. Twitter

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MISNC, SI, DS 2016

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MISNC, SI, DS 2016 Paper Acceptance Rate 57 of 97 submissions, 59%;
Overall Acceptance Rate 57 of 97 submissions, 59%

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  • (2017)Social Recommender SystemProceedings of the 4th Multidisciplinary International Social Networks Conference10.1145/3092090.3092121(1-7)Online publication date: 17-Jul-2017

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