MOVING OBJECTS MANAGEMENT FOR LOCATION-BASED SERVICES

Ph.DDOCTOR OF PHILOSOPH

Hypergraph-based optimisations for scalable graph analytics and learning

Graph-structured data has benefits of capturing inter-connectivity (topology) and hetero geneous knowledge (node/edge features) simultaneously. Hypergraphs may glean even more information reflecting complex non-pairwise relationships and additional metadata. Graph- and hypergraph-based partitioners can model workload or communication patterns of analytics and learning algorithms, enabling data-parallel scalability while preserving the solution quality. Hypergraph-based optimisations remain under-explored for graph neural networks (GNNs), which have complex access patterns compared to analytics workloads. Furthermore, special optimisations are needed when representing dynamic graph topologies and learning incrementally from streaming data. This thesis explores hypergraph-based optimisations for several scalable graph analytics and learning tasks. First, a hypergraph sampling approach is presented that supports large-scale dynamic graphs when modelling information cascades. Next, hypergraph partitioning is applied to scale approximate similarity search, by caching the computed features of replicated vertices. Moving from analytics to learning tasks, a data-parallel GNN training algorithm is developed using hypergraph-based construction and partitioning. Its communication scheme allows scalable distributed full-batch GNN training on static graphs. Sparse adja cency patterns are captured to perform non-blocking asynchronous communications for considerable speedups (10x single machine state-of-the-art baseline) in limited memory and bandwidth environments. Distributing GNNs using the hypergraph approach, compared to the graph approach, halves the running time and achieves 15% lower message volume. A new stochastic hypergraph sampling strategy further improves communication efficiency in distributed mini-batch GNN training. The final contribution is the design of streaming partitioners to handle dynamic data within a dataflow framework. This online partitioning pipeline allows complex graph or hypergraph streams to be processed asynchronously. It facilitates low latency distributed GNNs through replication and caching. Overall, the hypergraph-based optimisations in this thesis enable the development of scalable dynamic graph applications

An Outlook into the Future of Egocentric Vision

What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.Comment: We invite comments, suggestions and corrections here: https://openreview.net/forum?id=V3974SUk1

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov