We present a novel approach to text visualization called descriptive non-photorealistic rendering which exploits the inherent spatial and abstract dimensions in text documents to integrate 3D non-photorealistic rendering with information visualization. The visualization encodes text data onto 3D models, emphasizing the relative signi ficance of words in the text and the physical, real-world relationships between those words. Analytic exploration is supported through a collection of interactive widgets and direct multitouch interaction with the 3D models. We applied our method to analyze a collection of vehicle complaint reports from National Highway Traffic Safety Administration (NHTSA), and through a qualitative evaluation study, we demonstrate how our system can support tasks such as comparing the reliability of di fferent makes and models, finding interesting facts, and revealing possible causal relations between car parts. / UOIT

How patterns arise from an apparently uniform group of cells is one of the classical problems in developmental biology. The mechanism is complicated by the fact that patterning occurs on a growing medium. Therefore, changes in an organism’s size and shape affect the patterning processes. In turn, patterning itself may affect growth. This interaction between growth and patterning leads to the generation of complex shapes and structures from simpler ones. Studying such interactions requires the possibility to monitor both processes in vivo. To this end, we developed a new technique to monitor and quantify vein patterning in a growing leaf over time using the leaves of Arabidopsis thaliana as a model system. We used a transgenic line with fluorescent markers associated with the venation. Individual leaves are followed in many samples in vivo through time-lapse imaging. Custom-made software allowed us to extract the leaf surface and vein pattern from images of each leaf at each time point. Then average spatial maps from multiple samples that were generated revealed spatio-temporal gradients. Our quantitative description of wild type vein patterns during leaf development revealed that there is no constant size at which a part of tissue enclosed by vasculature will become irrigated by a new vein. Instead, it seemed that vein formation depends on the growth rate of the tissue. This is the first time that vein patterning in growing leaves was quantified. The techniques developed will later be used to explore the interaction between growth and patterning through a variety of approaches, including mutant analysis, pharmacological treatments and variation of environmental conditions.

The INSPIRE initiative requires that every European member state establishes a national SDI and related web services. Among these a WFS must be implemented to query spatial data. Therefore several Quality of Service specifications must be fullled by this web service, among these are performance, capacity and availability. In this study work a WFS will be implemented for Nature SDI plus. In this European project the WFS will provide spatial data from several data specications of ANNEX I and III. The main object is to test the conformance to the INSPIRE specification. Therefore load and performance tests will be performed.

Thesis (M.S.) in Spatial Information Science and Engineering--University of Maine, 2009. / Includes vita. Includes bibliographical references (leaves 109-114).

Thesis (M.S.)--University of West Florida, 2009. / Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 45 pages. Includes bibliographical references.

How patterns arise from an apparently uniform group of cells is one of the classical problems in developmental biology. The mechanism is complicated by the fact that patterning occurs on a growing medium. Therefore, changes in an organism’s size and shape affect the patterning processes. In turn, patterning itself may affect growth. This interaction between growth and patterning leads to the generation of complex shapes and structures from simpler ones. Studying such interactions requires the possibility to monitor both processes in vivo. To this end, we developed a new technique to monitor and quantify vein patterning in a growing leaf over time using the leaves of Arabidopsis thaliana as a model system. We used a transgenic line with fluorescent markers associated with the venation. Individual leaves are followed in many samples in vivo through time-lapse imaging. Custom-made software allowed us to extract the leaf surface and vein pattern from images of each leaf at each time point. Then average spatial maps from multiple samples that were generated revealed spatio-temporal gradients. Our quantitative description of wild type vein patterns during leaf development revealed that there is no constant size at which a part of tissue enclosed by vasculature will become irrigated by a new vein. Instead, it seemed that vein formation depends on the growth rate of the tissue. This is the first time that vein patterning in growing leaves was quantified. The techniques developed will later be used to explore the interaction between growth and patterning through a variety of approaches, including mutant analysis, pharmacological treatments and variation of environmental conditions.

In the past decade efforts to develop spatial data infrastructures (SDIs) have migrated from the initial “top-down” national approaches to “bottom-up” and cross jurisdictional efforts at the sub-national level. Although national SDI developments are fundamental to building the SDI culture and policy, it is sub-national and local SDI development that will deliver the immediate benefits to citizens and the community. In countries which have highly decentralised federations of states such as Australia, United States and Canada, the challenge is how to co-ordinate the literally thousands of often small local government jurisdictions which are important contributors to state and local SDIs. In recent years, a number of co-operative spatial data sharing partnerships between local and state government have emerged in various countries around the world. These partnerships are relatively new initiatives that have been established to facilitate more effective sharing of spatial data between organisations, but also as a mechanism to contribute to SDI development. To maximise the benefits from these partnerships it is essential to understand the factors that contribute to their successful operation and sustainability. Therefore, the focus of this research is to understand these collaborative arrangements so that future data sharing initiatives can be improved and sustained.

In response to a growing recognition of the importance of spatial information, the concept of Spatial Data Infrastructure (SDI) has evolved. Designed to facilitate an environment that promotes access and sharing of spatial information, SDI development has benefited from, and in due course adopted, advances in information technology (particularly improvements in desktop computing capabilities and communication networks such as the Internet). / The never ceasing progression of technology now enables communication and data access via mobile phones and a myriad of portable, networked computing devices. Indeed over the last few years, the proliferation of mobile phones has exceeded many expectations and is enabling nomadic users to communicate and access data services with ease. Location is one of the unique characteristics of mobility that is encompassed by this form of wireless communication and has been capitalised on in the form of enhanced safety initiatives. In turn, the infrastructure required for these safety services has encouraged additional Location Based Services (LBS) to flourish / LBS act as spatial decision making tools, providing information to end users based on their location, or on the location of some target. LBS are not restricted to the wireless environment however this is their current area of promotion. The principles of accessing spatial information that are encompassed by LBS mirror those of SDIs, and as a result SDI models need to accommodate for this new medium of information access and delivery. This research aimed to expand the SDI model to support applications that assist with spatial decision making, such as LBS. Focused specifically on LBS that are accessible for wirelessly networked, portable devices, this research implemented a theoretical and practical approach to identify the additional requirements for SDIs in this domain. A prototype LBS application for public transport information and navigation was developed and evaluated as part of this process. It is proposed that the resulting model (which details the additional requirements as well as their relative importance) act as an example framework for future LBS implementations so that they may gain the benefits from a standard, integrated infrastructure as offered by SDIs.

Doctor of Philosophy (PhD) / Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002.

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