Real Time Distributed Systems Group

The Real Time Distributed Systems Group is lead by Prof. Vana Kalogeraki. bestcanvas

We explore the world around us by turning sensed data into information. For years, research in fields such as distributed systems, participatory sensing systems, crowdsourcing, social networks and machine learning (ML), has steadily improved techniques for revealing underlying and hidden information from crowdsensed data. Together these trends have the potential for ushering in a new era in understanding and improving the world we live in.

Research Highlights

Featured Project

On Topic Aware Recommendation to Increase Popularity in Microblogging Services, OTM Confederated International Conferences, Rhodes, Greece, 24-28 October 2016

INSIGHT: Dynamic Traffic Management Using Heterogeneous Urban Data, ECML, Riva del Garda, Italy,19-23 September 2016

Intelligent Urban Data Monitoring for Smart Cities, ECML,Riva del Garda, Italy,19-23 September 2016

Location privacy for crowdsourcing applications, UbiComp, Heidelberg, Germany, September 12-16 2016

ChEsS: Cost-Effective Scheduling across multiple heterogeneous mapreduce clusters, ICAC, Wurzburg, Germany, 18-22 July 2016

A fast and efficient entity resolution approach for preserving privacy in mobile data, International Congress on Big Data, San Francisco, USA, June 27 – July 2, 2016

Dynamic Load Balancing Techniques for Distributed Complex Event Processing Systems, DAIS, Heraklion, Crete, Greece, June 6-9, 2016

Using Human Social Sensors for Robust Event Location Detection, DCOSS, Washington DC, USA, May 26 – 28 2016

Pareto-Based Scheduling of MapReduce Workloads, ISORC, York, UK, May 17-20, 2016

Our beta version of our CrowdAlert app is released

CoDiet: Combatting Diet Related Non-Communicable Disease Through Enhanced Surveillance

Unhealthy diets are associated with metabolic changes and increased risk of non-communicable diseases (NCDs). However, little is known about the dietary mechanisms that actually drive NCDs, and the tools used to collect dietary information are still inaccurate. There is also a lack of data among vulnerable groups, where NCDs are often over-represented. The EU-funded CoDiet project will address the knowledge gaps and develop a tool that will assess diet-induced NCD risk. Specifically,

it will develop an enhanced method of dietary assessment using AI technologies.
It will also develop a diet–NCD monitoring tool that will enable change in NCDs in response to diet to be monitored at the population level.
The overall goal is to promote the uptake of an NCD-protective diet at a population level.

For more information, visit the project page here