Recent advances in communication, sensors and processors have made pervasive systems more computationally powerful and increasingly popular. These systems are deployed everywhere all the time while

Traditionally, recommender systems modelled the physical and cyber contextual influence on people’s moving, querying, and browsing behaviours in isolation. Yet, searching, querying and moving beh

The ever increasing size of graphs makes them difficult to query and store. In this paper, we present Shrink, a compression method that reduces the size of the graph while preserving the distances

Network alignment is useful for multiple applications that require increasingly large graphs to be processed. Existing research approaches this as an optimization problem or computes the similarit

We propose flexgrid2vec, a novel approach for image representation learning. Existing visual representation methods suffer from several issues, including the need for highly intensive computation

Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity an

XML is becoming the de-facto standard for exchanging information in distributed applications and services. This has resulted in the development of a large number of XML documents with their associ