Exploring New Dimensions in Big Data


BigData illustration

Big Data is to a high degree the result from an unprecedented scale of user-generated data, increasing availability of open access to governmental data, and the declining cost of collecting and storing enterprise data and scientific data sets.

A large fraction of Big Data is textual, and has spatial and temporal dimensions. A prime example is textual social media data, which can have associated the location of the user when he/she wrote the message and the timestamp of when it was posted. In ExiBiDa, we will focus on exploratory analysis of data containing such spatiotemporal-textual (STT) contents, and develop frameworks and scalable techniques (i.e., efficient algorithms and index structures) for supporting analytical queries on such data.

About the ExiBiDa project.


Professor Kjetil Nørvåg
Project Leader
Kjetil Nørvåg


ExiBiDa aims to disseminate its research results to the scientific community through scientific publications at the top venues of our research area.

View ExiBiDa publications


  • 2017.03.21: Our paper "Towards Building a Knowledge Base of Monetary Transactions from a News Collection" accepted at JCDL'2017!
  • 2016.10.11: Our paper "Anticipating Information Needs Based on Check-in Activity" accepted at WSDM'2017!
  • 2016.10.11: Our paper " Efficient Processing of Top-k Joins in MapReduce" accepted at IEEE BigData'2016!
  • 2016.10.11: "Exploratory product search using top-k join queries", accepted for the Information Systems journal is now available online!
  • 2015.12.26: Our poster "Top-k Dominating Queries, in Parallel, in Memory" accepted at EDBT'2016!



Kjetil NørvågSean ChesterKrisztian BalogChristos DoulkeridisOrestis GkorgkasAkrivi VlachouStella MaropakiMalene S. Søholm

Strategic Research Area

The ExiBiDa project is one of several projects of the BigData strategic research area, hosted by the Faculty of Information Technology, Mathematics and Electrical Engineering.