Generating public transport data based on population distributions for RDF benchmarking

When benchmarking RDF data management systems such as public transport route planners, system evaluation needs to happen under various realistic circumstances, which requires a wide range of datasets with different properties. Real-world datasets are almost ideal, as they offer these realistic circumstances, but they are often hard to obtain and inflexible for testing. For these reasons, synthetic dataset generators are typically preferred over real-world datasets due to their intrinsic flexibility. Unfortunately, many synthetic dataset that are generated within benchmarks are insufficiently realistic, raising questions about the generalizability of benchmark results to real-world scenarios. In order to benchmark geospatial and temporal RDF data management systems such as route planners with sufficient external validity and depth, we designed PODiGG, a highly configurable generation algorithm for synthetic public transport datasets with realistic geospatial and temporal characteristics comparable to those of their real-world variants. The algorithm is inspired by real-world public transit network design and scheduling methodologies. This article discusses the design and implementation of PODiGG and validates the properties of its generated datasets. Our findings show that the generator achieves a sufficient level of realism, based on the existing coherence metric and new metrics we introduce specifically for the public transport domain. Thereby, PODiGG provides a flexible foundation for benchmarking RDF data management systems with geospatial and temporal data

A Framework for Hybrid Intrusion Detection Systems

Web application security is a definite threat to the world’s information technology infrastructure. The Open Web Application Security Project (OWASP), generally defines web application security violations as unauthorized or unintentional exposure, disclosure, or loss of personal information. These breaches occur without the company’s knowledge and it often takes a while before the web application attack is revealed to the public, specifically because the security violations are fixed. Due to the need to protect their reputation, organizations have begun researching solutions to these problems. The most widely accepted solution is the use of an Intrusion Detection System (IDS). Such systems currently rely on either signatures of the attack used for the data breach or changes in the behavior patterns of the system to identify an intruder. These systems, either signature-based or anomaly-based, are readily understood by attackers. Issues arise when attacks are not noticed by an existing IDS because the attack does not fit the pre-defined attack signatures the IDS is implemented to discover. Despite current IDSs capabilities, little research has identified a method to detect all potential attacks on a system. This thesis intends to address this problem. A particular emphasis will be placed on detecting advanced attacks, such as those that take place at the application layer. These types of attacks are able to bypass existing IDSs, increase the potential for a web application security breach to occur and not be detected. In particular, the attacks under study are all web application layer attacks. Those included in this thesis are SQL injection, cross-site scripting, directory traversal and remote file inclusion. This work identifies common and existing data breach detection methods as well as the necessary improvements for IDS models. Ultimately, the proposed approach combines an anomaly detection technique measured by cross entropy and a signature-based attack detection framework utilizing genetic algorithm. The proposed hybrid model for data breach detection benefits organizations by increasing security measures and allowing attacks to be identified in less time and more efficiently

Approximate Assertional Reasoning Over Expressive Ontologies

In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed

Semantic-based framework for the generation of travel demand

Traffic and transportation have a wide-ranging impact on the daily lives of the human population and society. Activity-based travel demand generation models and traffic simulators are tools that have been developed to investigate traffic and transport problems and assist in developing solutions. The closer modelling of human behaviour, the emergence of new technologies and the availability of more detailed datasets is leading to greater modelling complexity. The robustness of conclusions in investigations is supported by comparison of multiple techniques and models yet variations in the platform, data requirements and dataset availability present barriers to their breadth. This thesis investigates the development of a Semantic Web framework for activity-based travel demand generation. It is proposed that the application of a knowledge-based approach and development of an orchestrating framework will enable a loosely coupled modular architecture. This approach will reduce the burden in preparing and accessing datasets through the construction of a platform-independent knowledge-base and facilitate switching between modules and datasets. The principal contributions of this work are the application of a knowledge-based approach to travel demand generation; the development of a Semantic-based framework to control the configuration of the process and the design; and demonstration of the Semantic based framework through the implementation and evaluation of the modular travel demand generation process, including integration with two third-party traffic simulators. The investigation found that the proposed approach can be successfully applied to model and control the travel demand generation process. Multiple configurations were explored, including utilising network communications, and found that this had a noticeable impact on execution duration but also the potential for mitigation through distributed computing. This presents the opportunity for an online infrastructure of datasets and module implementations for travel demand generation that users can select and access through the framework. This infrastructure would remove the need for ad hoc interfaces; data format conversion or platform dependence to facilitate the process of traffic modelling becoming quicker and more robust

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Impact of the pricing policy on the environmental performance of urban waste management systems

This PhD. thesis has as main objective the evaluation of the environmental impacts derived from the application of unit base (variable) pricing systems for financing urban waste management. To this purpose, a pilot experience with variable pricing schemes which took place in the Portuguese city of Aveiro was chosen as case of study. The evaluation was mainly performed making use of Life Cycle Assessment (LCA) as an analytical tool for measuring environmental impacts. Both modelling approaches of LCA (attributional and consequential) were used in order to perform a comprehensive environmental comparison between the situation found in waste management before and after the pilot experience