High-Throughput Network Distance Computations for Spatial Analytics Inside Any Store

In the past decades, shortest distance computation methods for road networks have been developed that focus on how to speed up the latency of a single source-target pair distance query. Large analytic applications on road networks including simulations (e.g., evacuation planning), logistics, location-based advertisement, and transportation planning require methods that provide high throughput (i.e., distance computations per second) and the ability to "scale out" by using large distributed computing clusters. Although decreasing the latency time for one source-target query results in reducing the total response time for a spatial analytic query, it is far from enough since these methods don’t take into account considerations such as cache results, query optimization, multi-threads, distributed systems, etc. This thesis broadly expands on the use of the distance oracle on road networks to achieve above goals. In the first part, we present a new framework termed the All-Store Distance Oracle (ASDO) for large road networks and shows how to efficiently compute it for any large road network in a distributed cluster. The ASDO representation is a well-separated pair decomposition (WSPD) of a road network using network distance instead of Euclidean distance. The ASDO representation benefits from the small size of the WSPD which enables the ASDO representation to answer ε-approximate network distance queries in a high-throughput rate and can be easily embedded within any database system including RDBMS, Column-oriented DBMS, and key-value stores. Experimental results show that the ASDO representation of the USA road network can be computed in a few hours using a modest size cluster. In comparison, previous database-centric approaches either do not scale to large road networks or are several orders of magnitude slower than the proposed ASDO for spatial queries. In the second part , we show how useful the ASDO representation is in real applications evaluating two proposed architectures on a variety of spatial analytic queries in common use such as KNN, distance matrix, and trajectory queries. One architecture is our ASDO representation embedded in PostgreSQL, and the other one is a widely used hybrid architecture in industry. Embedding the ASDO representation inside PostgreSQL supports the performance of complex analytic queries on road networks using standard SQL. This makes the results of ASDO simple to use, yet considerably expressive, compared to traditional methods that require extensive development effort. Experimental results indicate that our ASDO architecture within PostgreSQL can compute more than 60K road distance operations per second on a large road network (e.g., USA), which achieves 20× more throughput compared to the state-of-the-art shortest distance computation methods. In the third part, as some applications require the ability to scale out on large distributed computing clusters, a framework called SPDO is presented which implements an extremely fast distributed algorithm for computing spatial analytic queries on Apache Spark. The approach extends the ASDO representation which has now been adapted to use Spark’s resilient distributed dataset (RDD). SPDO improves the throughput by at least two orders of magnitude, which makes the approach suitable for applications that need to compute millions of network distances per second. Interviews with tens of related companies whom we deemed to be needy of performing some analytic queries on road networks led us to observe that they are usually concentrated in a local area spanning several cities, and need a high-throughput solution such as performing millions of shortest distance computations per second. In the forth part, we first demonstrate a solution, termed City Distance Oracles (CDO) to achieve as many as 7 million shortest distance computations per second per commodity machine on a city road network. Next, we extend CDO to yield a new distance oracle system (DOS) for general road networks. It can solve most spatial analytic queries, and its throughput achieves 5M distance computations per second even on the whole USA road network. In addition, a 10K × 10K origin-distance (OD) matrix can be computed in 20 seconds

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels

Geospatial Computing: Architectures and Algorithms for Mapping Applications

Beginning with the MapTube website (1), which was launched in 2007 for crowd-sourcing maps, this project investigates approaches to exploratory Geographic Information Systems (GIS) using web-based mapping, or ‘web GIS’. Users can log in to upload their own maps and overlay different layers of GIS data sets. This work looks into the theory behind how web-based mapping systems function and whether their performance can be modelled and predicted. One of the important questions when dealing with different geospatial data sets is how they relate to one another. Internet data stores provide another source of information, which can be exploited if more generic geospatial data mining techniques are developed. The identification of similarities between thousands of maps is a GIS technique that can give structure to the overall fabric of the data, once the problems of scalability and comparisons between different geographies are solved. After running MapTube for nine years to crowd-source data, this would mark a natural progression from visualisation of individual maps to wider questions about what additional knowledge can be discovered from the data collected. In the new ‘data science’ age, the introduction of real-time data sets introduces a new challenge for web-based mapping applications. The mapping of real-time geospatial systems is technically challenging, but has the potential to show inter-dependencies as they emerge in the time series. Combined geospatial and temporal data mining of realtime sources can provide archives of transport and environmental data from which to accurately model the systems under investigation. By using techniques from machine learning, the models can be built directly from the real-time data stream. These models can then be used for analysis and experimentation, being derived directly from city data. This then leads to an analysis of the behaviours of the interacting systems. (1) The MapTube website: http://www.maptube.org

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Journal of Telecommunications and Information Technology, 2017, nr 1

kwartalni