Software Campus: Data Driven Decision Recommendation for Administrative Bodies Based on Collective Intelligence

July 5th, 2014  |  Published in Research

The enormous amount of data created by people using every day mobile devices with increasingly sensing capabilities, such as image, location and other data, has led to the establishment of a new crowdsourcing concept called Participatory Sensing. It describes people voluntarily sensing their local environment and sharing this data using mobile phones and the web, creating a body of collective intelligence. This concept enables common and professional users to gather and share their local knowledge. Application areas are mainly urban planning, environmental monitoring and natural resource management.

Motivated by the opportunities of building more intelligent cities, we came up with a vision of harnessing participatory sensing and data analytic to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major infrastructure monitoring issues our cities face today. While in collaborative infrastructure monitoring, the infrastructural issue reporting can be fully automated, the processing of the collected data by civil servants is still done manually. This information (i.e. reported infrastructure issues) will not be useful if its flow exceeds the receiving entity’s capacity to process or respond to it. Intelligent data processing can optimize many processes in this specific case: reports may be grouped and automatically analyzed, whereby duplicate detection is facilitated, thus avoiding (manual) multiple processing of the reports. Additionally, models can be built to make inferences about the importance and urgency of a current issue, leading to a better prioritization in the allocation of resources.

The goal of ESTAData is to leverage the power of Big-Data technologies attached to data mining and machine-learning techniques to extract knowledge from collaborative data, getting new and better insights into events on the urban scale, helping administrative bodies allocate their resources better and make better decisions.

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Start/End

  • 04.2014 — 03.2015

Partners

  • Software AG

Research Topics

  • Crowdsourcing; Spatio-Temporal Analytics; Big Data; Civic Issue Tracking; Smart-Cities

Contact

Selected Publications

Matthias Budde, Julio De Melo Borges, Stefan Tomov, Till Riedel, Michael Beigl (2014) Leveraging Spatio-Temporal Clustering for Participatory Urban Infrastructure Monitoring, The First International Conference on IoT in Urban Space (UrbIoT'14), Best Paper Award, url

Matthias Budde, Julio De Melo Borges, Stefan Tomov, Till Riedel, Michael Beigl (2014) Improving Participatory Urban Infrastructure Monitoring through Spatio-Temporal Analytics, 3rd ACM SIGKDD International Workshop on Urban Computing (UrbComp’14), pdf

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