Presenting the Demo Social Smart Meter: Identifying Energy Consumption Behavior in User-Generated Content
Andrea Mauri is a Postdoctoral Researcher within Web Information System group at the Delft University of Technology. Here he works in the context of the JOIN “Big Data for Evidence-based Youth Policy” project, which aims to investigate to what extent social (big) data can be used to help to identify NEEts and inform policies for improving their integration with the society.
For a brief period he was a PostDoc Researcher in the Research Group of Urban Development Management at the Delft University of Technology where he worked in the context of the BOLD Cities project, which aimed to use big data research to help find solutions for urban problems.
Previously he was a PostDoc Researcher at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of the Politecnico di Milano in the DataScience Group where he studied models, methods and technologies for Crowdsourcing and Human Computation, with special attention to problems related to: crowd and social networks integration, adaptation and control of crowdsourcing task. He also studied model-driven software engineering, with particular focus on language specification and model transformations, and in the definition of business process-based applications integrated with social network practices.
His main research interests include smart city sensing and social content analysis. In particular he applies data science techniques to integrate heterogeneous data sources in order to discover knowledge that would remain hidden otherwise.
PhD in Information Technology, 2016
Politecnico di Milano
MEng in Computer Science Engineering, 2011
Politecnico di Milano
BSc in Computer Science Engineering, 2010
Politecnico di Milano
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The AUTOMOBILE project aims at designing and bringing to the market innovative methodologies, software tools, and vertical applications for the cost-effective implementation of cross-platform, multi-device mobile applications.
BOLD cities use all kinds of data generated by - among others - sensor technologies, social media or classic census data. The emerging urban data landscape is diverse and complex, and involves big data as well as open or linked data. It includes data that is personal and impersonal, individual and aggregate, historical and real time, and so on. It raises questions about storage, analytics, presentation and visualization, but also about appropriate data-governance and management, and particularly about the social and individual consequences of the urban data revolution for people in the city.
The BPM4People project aims at designing and bringing to the market innovative methodologies, software tools, and vertical applications for the implementation of Social Business Process Management (Social BPM).
This project presents a sociological approach and explores the extent to which social interactions, at the level of the neighborhood, might be able to challenge current energy needs and to spur the construction of new meanings of energy, which ultimately might lead to a reduction of energy usage.
CrowdSearcher is a crowd-management system that implements a paradigm that embodies crowds and social network communities as first-class sources for the information management and extraction on the Web.
ECSTASYS gathers in realtime the tweets related to the event, analyses them and links them to the specific sub-events they refer to. The goal is to improve the experience of (local or remote) attendees, by exploiting the contents shared on the social networks.
3cixty is a new initiative launched in 2014 to drive European leadership in future ICT-enabled urban life and mobility solutions. The initiative will realise a platform and related service ecosystem for the provisioning of comprehensive heterogeneous city-related mobility information
A platform that provides vehicle drivers and travellers with smart and real-time adjusted inter-modal itineraries, based on crowd sourcing and mobility analytics services.
The standard Interaction Flow Modeling Language (IFML) is designed for expressing the content, user interaction and control behaviour of the front-end of software applications.
In Stream Reasoning (SR), empirical research on RDF Stream Processing (RSP) is attracting a growing attention. The SR community proposed methodologies and benchmarks to investigate the RSP solution space and improve existing approaches. RSPLab is an infrastructure that reduces the effort required to design and execute reproducible experiments as well as share their results. It integrates two existing RSP benchmarks (LSBench and CityBench) and two RSP engines (C-SPARQL engine and CQELS).
TripleWave is a reusable and generic tool that enables the publication of RDF streams on the Web.
Cities are not mere physical and organizational devices: they are informational landscapes where places are shaped more by the streams of contents and less by the traditional physical evidences.