Improving the transparency, openness and efficiency of e-government in Qatar in the era of Open Government Data, and beyond

This thesis investigates how Open Government Data (OGD) concepts and practices might be implemented in the State of Qatar to achieve more transparent, effective and accountable government. The thesis concludes with recommendations as to how Qatar, as a developing country, might enhance the accessibility and usability of its OGD and implement successful and sustainable OGD systems and practices.

miRPlant : an integrated tool for identification of plant miRNA from RNA sequencing data

Background Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. Result We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram. Conclusions We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.

A random key genetic algorithm for live migration of multiple virtual machines in data centers

Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Data mining the relationship between road crash and skid resistance

Road asset managers are seeking analysis of the whole road network to supplement statistical analyses of small subsets of homogeneous roadway. This study outlines the use of data mining capable of analyzing the wide range of situations found on the network, with a focus on the role of skid resistance in the cause of crashes. Results from the analyses show that on non-crash-prone roads with low crash rates, skid resistance contributes only in a minor way, whereas on high-crash roadways, skid resistance often contributes significantly in the calculation of the crash rate. The results provide evidence supporting a causal relationship between skid resistance and crashes and highlight the importance of the role of skid resistance in decision making in road asset management.

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications.

Synthesizing form and data : developing practices for student engagement and learning

We explore the relationship between form and data as a design agenda and learning strategy for novice visual information designers. Our students are university seniors in digital, visual design but novices to information design, manipulation and interpretation. We describe design strategies developed to scaffold sophisticated aesthetic and conceptual engagement despite limited understanding of the domain of designing with information. These revolve around an open-ended design project where students created a physical design from data of their choosing and research. The accompanying learning strategies concern this relationship between data and form to investigate it materially, formally and through ideation. Exemplifying student works that cross media and design domains are described.

Dataset validation within big data security architecture

In recent years, increasing focus has been made on making good business decisions utilizing the product of data analysis. With the advent of the Big Data phenomenon, this is even more apparent than ever before. But the question is how can organizations trust decisions made on the basis of results obtained from analysis of untrusted data? Assurances and trust that data and datasets that inform these decisions have not been tainted by outside agency. This study will propose enabling the authentication of datasets specifically by the extension of the RESTful architectural scheme to include authentication parameters while operating within a larger holistic security framework architecture or model compliant to legislation.

Spatial analysis of risk factors for transmission of the Barmah forest virus in Queensland, Australia

Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia but few data are available on the risk factors. We assessed the impact of spatial climatic, socioeconomic and ecological factors on the transmission of BFV disease in Queensland, Australia, using spatial regression. All our analyses indicate that spatial lag models provide a superior fit to the data compared to spatial error and ordinary least square models. The residuals of the spatial lag models were found to be uncorrelated, indicating that the models adequately account for spatial and temporal autocorrelation. Our results revealed that minimum temperature, distance from coast and low tide were negatively and rainfall was positively associated with BFV disease in coastal areas, whereas minimum temperature and high tide were negatively and rainfall was positively associated with BFV disease (all P-value.