Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

GAKE: Graph Aware Knowledge Embedding

Knowledge embedding, which projects triples in a given knowledge base to d-dimensional vectors, has attracted considerable research efforts recently. Most existing approaches treat the given knowledge base as a set of triplets, each of whose representation is then learned separately. However, as a fact, triples are connected and depend on each other. In this paper, we propose a graph aware know...

Context-aware Modeling for Spatio-temporal Data Transmitted from a Wireless Body Sensor Network

Context-aware systems must be interoperable and work across different platforms at any time and in any place. Context data collected from wireless body area networks (WBAN) may be heterogeneous and imperfect, which makes their design and implementation difficult. In this research, we introduce a model which takes the dynamic nature of a context-aware system into consideration. This model is con...

Dealing with Multiple Source Spatio-temporal Data in Urban Dynamics Analysis

Capturing, representing, modelling and visualizing the dynamics of urban mobility have been attracting the interest of the research community recently. One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Applications for these studies are diverse and include urban planning, security, intelligent transportation sy...

Uncertainty-aware Visual Analytics for Spatio-temporal Data Exploration

Analyzing and Predicting Large Vector-, Graph- and Spatio-Temporal Data

Large social graph datasets, pertaining to millions of social network users and the billions of relationships between them; complex, high dimensional vector data of large database systems; and petabytes of environmental sensor data are being generated every day. Employing this flood of data for the benefit of all, is one of the main challenges of the 21st century[129, 88, 51]. This thesis advan...