The University of Southampton

Printed Electronics and Materials Laboratory Printed Electronics and Materials Laboratory
MEng Electronic Engineering with Artificial Intelligence MEng Computer Science with Artificial Intelligence MSc Artificial Intelligence

Date:
2016-2019
Theme:
Data Science / Big Data
Funding:
European Commission (732194)

Big Data integration in European cities is of utmost importance for municipalities and companies to offer effective information services, enable efficient data-driven transportation and mobility, reduce CO2 emissions, assess the efficiency of infrastructure, as well as enhance the quality of life of citizens. At present this integration is substantially limited due to the following factors: 1) Urban Big Data is locked in isolated industrial and public sectors, and 2) The actual Big Data integration is an extremely hard technical problem due to the heterogeneity of data sources, variety of formats, sizes, quality as well as update rates, such that the integration requires significant human intervention.

QROWD addresses these challenges by offering methods to perform cross-sectoral streaming Big Data integration including geographic, transport, meteorological, cross domain and news data, while capitalizing on human feedback channels. The main objectives of QROWD are: (1) Facilitating cross-sectoral Big Data stream integration for urban mobility including real-time data on individual and public transportation combined with further available sources, such as weather conditions and infrastructure information to create a comprehensive overview of the city traffic; (2) Supporting participation and feedback of various stakeholder groups to foster data-driven innovation in cities; and (3) Building a platform providing hybrid computational methods relying on efficient algorithms complemented with human computation and feedback.

The main outcomes of QROWD are: (1) Two data value chains in the sectors of urban mobility and public transportation using a mix of large scale heterogeneous multilingual datasets; and (2) Cross-sectoral and cross-lingual technology, including algorithms and tools covering all phases of the cross-sectoral Big Data Value Chain building on W3C standards and capitalizing on a flexible and efficient combination of human and machine-based computation.

Primary investigator

Secondary investigators

Partners

  • ATOS SPAIN SA
  • TOMTOM DEVELOPMENT GERMANY GMBH
  • COMUNE DI TRENTO

Associated research group

  • Web and Internet Science
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Date:
2017-2020
Themes:
Data Science / Big Data, Open & Linked Data
Funding:
EPSRC (EP/P025676/1)

In the post-truth society we live in, experts must find novel ways to bring hard, factual data to citizens. Data must entertain as well as inform, and excite as well as educate. It must be built with sharing through social channels in mind and become part of our everyday activities and interactions with others. Data Stories will look at novel frameworks and technologies for bringing data to people through art, games, and storytelling. It will examine the impact that varying levels of localisation, topicalisation, participation, and shareability have on the engagement of the general public with factual evidence substantiated by different forms of digital content derived and repurposed from a variety of sources. It will deliver the tools and guidance that community and civic groups need to achieve broader participation and support for their initiatives at local and national level, and empower artists, designers, statisticians, analysts, and journalists to communicate with data in inspiring, informative ways.

Primary investigator

Secondary investigators

Partners

  • BBC
  • Edelman
  • Full Fact
  • House of Commons
  • MundoJumbo Ltd
  • Office for National Statistics
  • Open Data Institute
  • Represent

Associated research group

  • Web and Internet Science
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