Advanced data driven visualisation for geo-spatial data

Most current 3D landscape visualisation systems either use bespoke hardware solutions, or offer a limited amount of interaction and detail when used in realtime mode. We are developing a modular, data driven 3D visualisation system that can be readily customised to specific requirements. By utilising the latest software engineering methods and bringing a dynamic data driven approach to geo-spatial data visualisation we will deliver an unparalleled level of customisation in near-photo realistic, realtime 3D landscape visualisation. In this paper we show the system framework and describe how this employs data driven techniques. In particular we discuss how data driven approaches are applied to the spatiotemporal management aspect of the application framework, and describe the advantages these convey. © Springer-Verlag Berlin Heidelberg 2006.

Introduction to data analysis

With the latest development in computer science, multivariate data analysis methods became increasingly popular among economists. Pattern recognition in complex economic data and empirical model construction can be more straightforward with proper application of modern softwares. However, despite the appealing simplicity of some popular software packages, the interpretation of data analysis results requires strong theoretical knowledge. This book aims at combining the development of both theoretical and applicationrelated data analysis knowledge. The text is designed for advanced level studies and assumes acquaintance with elementary statistical terms. After a brief introduction to selected mathematical concepts, the highlighting of selected model features is followed by a practice-oriented introduction to the interpretation of SPSS1 outputs for the described data analysis methods. Learning of data analysis is usually time-consuming and requires efforts, but with tenacity the learning process can bring about a significant improvement of individual data analysis skills.

Web-based report generators, data visualization, and Web-to-database connectivity

Because some Web users will be able to design a template to visualize information from scratch, while other users need to automatically visualize information by changing some parameters, providing different levels of customization of the information is a desirable goal. Our system allows the automatic generation of visualizations given the semantics of the data, and the static or pre-specified visualization by creating an interface language. We address information visualization taking into consideration the Web, where the presentation of the retrieved information is a challenge. ^ We provide a model to narrow the gap between the user's way of expressing queries and database manipulation languages (SQL) without changing the system itself thus improving the query specification process. We develop a Web interface model that is integrated with the HTML language to create a powerful language that facilitates the construction of Web-based database reports. ^ As opposed to other papers, this model offers a new way of exploring databases focusing on providing Web connectivity to databases with minimal or no result buffering, formatting, or extra programming. We describe how to easily connect the database to the Web. In addition, we offer an enhanced way on viewing and exploring the contents of a database, allowing users to customize their views depending on the contents and the structure of the data. Current database front-ends typically attempt to display the database objects in a flat view making it difficult for users to grasp the contents and the structure of their result. Our model narrows the gap between databases and the Web. ^ The overall objective of this research is to construct a model that accesses different databases easily across the net and generates SQL, forms, and reports across all platforms without requiring the developer to code a complex application. This increases the speed of development. In addition, using only the Web browsers, the end-user can retrieve data from databases remotely to make necessary modifications and manipulations of data using the Web formatted forms and reports, independent of the platform, without having to open different applications, or learn to use anything but their Web browser. We introduce a strategic method to generate and construct SQL queries, enabling inexperienced users that are not well exposed to the SQL world to build syntactically and semantically a valid SQL query and to understand the retrieved data. The generated SQL query will be validated against the database schema to ensure harmless and efficient SQL execution. (Abstract shortened by UMI.)^

A semantic paradigm for intelligent data access

An implementation of Sem-ODB—a database management system based on the Semantic Binary Model is presented. A metaschema of Sem-ODB database as well as the top-level architecture of the database engine is defined. A new benchmarking technique is proposed which allows databases built on different database models to compete fairly. This technique is applied to show that Sem-ODB has excellent efficiency comparing to a relational database on a certain class of database applications. A new semantic benchmark is designed which allows evaluation of the performance of the features characteristic of semantic database applications. An application used in the benchmark represents a class of problems requiring databases with sparse data, complex inheritances and many-to-many relations. Such databases can be naturally accommodated by semantic model. A fixed predefined implementation is not enforced allowing the database designer to choose the most efficient structures available in the DBMS tested. The results of the benchmark are analyzed. ^ A new high-level querying model for semantic databases is defined. It is proven adequate to serve as an efficient native semantic database interface, and has several advantages over the existing interfaces. It is optimizable and parallelizable, supports the definition of semantic userviews and the interoperability of semantic databases with other data sources such as World Wide Web, relational, and object-oriented databases. The query is structured as a semantic database schema graph with interlinking conditionals. The query result is a mini-database, accessible in the same way as the original database. The paradigm supports and utilizes the rich semantics and inherent ergonomics of semantic databases. ^ The analysis and high-level design of a system that exploits the superiority of the Semantic Database Model to other data models in expressive power and ease of use to allow uniform access to heterogeneous data sources such as semantic databases, relational databases, web sites, ASCII files, and others via a common query interface is presented. The Sem-ODB engine is used to control all the data sources combined under a unified semantic schema. A particular application of the system to provide an ODBC interface to the WWW as a data source is discussed. ^

Dynamic data retrieval on the World Wide Web

Methods for accessing data on the Web have been the focus of active research over the past few years. In this thesis we propose a method for representing Web sites as data sources. We designed a Data Extractor data retrieval solution that allows us to define queries to Web sites and process resulting data sets. Data Extractor is being integrated into the MSemODB heterogeneous database management system. With its help database queries can be distributed over both local and Web data sources within MSemODB framework. ^ Data Extractor treats Web sites as data sources, controlling query execution and data retrieval. It works as an intermediary between the applications and the sites. Data Extractor utilizes a twofold “custom wrapper” approach for information retrieval. Wrappers for the majority of sites are easily built using a powerful and expressive scripting language, while complex cases are processed using Java-based wrappers that utilize specially designed library of data retrieval, parsing and Web access routines. In addition to wrapper development we thoroughly investigate issues associated with Web site selection, analysis and processing. ^ Data Extractor is designed to act as a data retrieval server, as well as an embedded data retrieval solution. We also use it to create mobile agents that are shipped over the Internet to the client's computer to perform data retrieval on behalf of the user. This approach allows Data Extractor to distribute and scale well. ^ This study confirms feasibility of building custom wrappers for Web sites. This approach provides accuracy of data retrieval, and power and flexibility in handling of complex cases. ^

Semantic distributed database management with applications to the Internet dissemination of environmental data

The research presented in this dissertation is comprised of several parts which jointly attain the goal of Semantic Distributed Database Management with Applications to Internet Dissemination of Environmental Data. ^ Part of the research into more effective and efficient data management has been pursued through enhancements to the Semantic Binary Object-Oriented database (Sem-ODB) such as more effective load balancing techniques for the database engine, and the use of Sem-ODB as a tool for integrating structured and unstructured heterogeneous data sources. Another part of the research in data management has pursued methods for optimizing queries in distributed databases through the intelligent use of network bandwidth; this has applications in networks that provide varying levels of Quality of Service or throughput. ^ The application of the Semantic Binary database model as a tool for relational database modeling has also been pursued. This has resulted in database applications that are used by researchers at the Everglades National Park to store environmental data and to remotely-sensed imagery. ^ The areas of research described above have contributed to the creation TerraFly, which provides for the dissemination of geospatial data via the Internet. TerraFly research presented herein ranges from the development of TerraFly's back-end database and interfaces, through the features that are presented to the public (such as the ability to provide autopilot scripts and on-demand data about a point), to applications of TerraFly in the areas of hazard mitigation, recreation, and aviation. ^

3D TerraFly: Quality of service management for online interactive 3D geographic information system

3D geographic information system (GIS) is data and computation intensive in nature. Internet users are usually equipped with low-end personal computers and network connections of limited bandwidth. Data reduction and performance optimization techniques are of critical importance in quality of service (QoS) management for online 3D GIS. In this research, QoS management issues regarding distributed 3D GIS presentation were studied to develop 3D TerraFly, an interactive 3D GIS that supports high quality online terrain visualization and navigation. ^ To tackle the QoS management challenges, multi-resolution rendering model, adaptive level of detail (LOD) control and mesh simplification algorithms were proposed to effectively reduce the terrain model complexity. The rendering model is adaptively decomposed into sub-regions of up-to-three detail levels according to viewing distance and other dynamic quality measurements. The mesh simplification algorithm was designed as a hybrid algorithm that combines edge straightening and quad-tree compression to reduce the mesh complexity by removing geometrically redundant vertices. The main advantage of this mesh simplification algorithm is that grid mesh can be directly processed in parallel without triangulation overhead. Algorithms facilitating remote accessing and distributed processing of volumetric GIS data, such as data replication, directory service, request scheduling, predictive data retrieving and caching were also proposed. ^ A prototype of the proposed 3D TerraFly implemented in this research demonstrates the effectiveness of our proposed QoS management framework in handling interactive online 3D GIS. The system implementation details and future directions of this research are also addressed in this thesis. ^

Data structures for highly efficient querying of spatial data

The virtual quadrilateral is the coalescence of novel data structures that reduces the storage requirements of spatial data without jeopardizing the quality and operability of the inherent information. The data representative of the observed area is parsed to ascertain the necessary contiguous measures that, when contained, implicitly define a quadrilateral. The virtual quadrilateral then represents a geolocated area of the observed space where all of the measures are the same. The area, contoured as a rectangle, is pseudo-delimited by the opposite coordinates of the bounding area. Once defined, the virtual quadrilateral is representative of an area in the observed space and is represented in a database by the attributes of its bounding coordinates and measure of its contiguous space. Virtual quadrilaterals have been found to ensure a lossless reduction of the physical storage, maintain the implied features of the data, facilitate the rapid retrieval of vast amount of the represented spatial data and accommodate complex queries. The methods presented herein demonstrate that virtual quadrilaterals are created quite easily, are stable and versatile objects in a database and have proven to be beneficial to exigent spatial data applications such as geographic information systems. ^

Data partitioning and replication management in distributed geographic information system (GIS) database

This research presents several components encompassing the scope of the objective of Data Partitioning and Replication Management in Distributed GIS Database. Modern Geographic Information Systems (GIS) databases are often large and complicated. Therefore data partitioning and replication management problems need to be addresses in development of an efficient and scalable solution. ^ Part of the research is to study the patterns of geographical raster data processing and to propose the algorithms to improve availability of such data. These algorithms and approaches are targeting granularity of geographic data objects as well as data partitioning in geographic databases to achieve high data availability and Quality of Service(QoS) considering distributed data delivery and processing. To achieve this goal a dynamic, real-time approach for mosaicking digital images of different temporal and spatial characteristics into tiles is proposed. This dynamic approach reuses digital images upon demand and generates mosaicked tiles only for the required region according to user's requirements such as resolution, temporal range, and target bands to reduce redundancy in storage and to utilize available computing and storage resources more efficiently. ^ Another part of the research pursued methods for efficient acquiring of GIS data from external heterogeneous databases and Web services as well as end-user GIS data delivery enhancements, automation and 3D virtual reality presentation. ^ There are vast numbers of computing, network, and storage resources idling or not fully utilized available on the Internet. Proposed "Crawling Distributed Operating System "(CDOS) approach employs such resources and creates benefits for the hosts that lend their CPU, network, and storage resources to be used in GIS database context. ^ The results of this dissertation demonstrate effective ways to develop a highly scalable GIS database. The approach developed in this dissertation has resulted in creation of TerraFly GIS database that is used by US government, researchers, and general public to facilitate Web access to remotely-sensed imagery and GIS vector information. ^

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Knowledge assisted data management and retrieval in multimedia database systems

With the proliferation of multimedia data and ever-growing requests for multimedia applications, there is an increasing need for efficient and effective indexing, storage and retrieval of multimedia data, such as graphics, images, animation, video, audio and text. Due to the special characteristics of the multimedia data, the Multimedia Database management Systems (MMDBMSs) have emerged and attracted great research attention in recent years. Though much research effort has been devoted to this area, it is still far from maturity and there exist many open issues. In this dissertation, with the focus of addressing three of the essential challenges in developing the MMDBMS, namely, semantic gap, perception subjectivity and data organization, a systematic and integrated framework is proposed with video database and image database serving as the testbed. In particular, the framework addresses these challenges separately yet coherently from three main aspects of a MMDBMS: multimedia data representation, indexing and retrieval. In terms of multimedia data representation, the key to address the semantic gap issue is to intelligently and automatically model the mid-level representation and/or semi-semantic descriptors besides the extraction of the low-level media features. The data organization challenge is mainly addressed by the aspect of media indexing where various levels of indexing are required to support the diverse query requirements. In particular, the focus of this study is to facilitate the high-level video indexing by proposing a multimodal event mining framework associated with temporal knowledge discovery approaches. With respect to the perception subjectivity issue, advanced techniques are proposed to support users' interaction and to effectively model users' perception from the feedback at both the image-level and object-level.

Adding concurrent data transfer to transport layer

In recent years, the internet has grown exponentially, and become more complex. This increased complexity potentially introduces more network-level instability. But for any end-to-end internet connection, maintaining the connection's throughput and reliability at a certain level is very important. This is because it can directly affect the connection's normal operation. Therefore, a challenging research task is to improve a network's connection performance by optimizing its throughput and reliability. This dissertation proposed an efficient and reliable transport layer protocol (called concurrent TCP (cTCP)), an extension of the current TCP protocol, to optimize end-to-end connection throughput and enhance end-to-end connection fault tolerance. The proposed cTCP protocol could aggregate multiple paths' bandwidth by supporting concurrent data transfer (CDT) on a single connection. Here concurrent data transfer was defined as the concurrent transfer of data from local hosts to foreign hosts via two or more end-to-end paths. An RTT-Based CDT mechanism, which was based on a path's RTT (Round Trip Time) to optimize CDT performance, was developed for the proposed cTCP protocol. This mechanism primarily included an RTT-Based load distribution and path management scheme, which was used to optimize connections' throughput and reliability. A congestion control and retransmission policy based on RTT was also provided. According to experiment results, under different network conditions, our RTT-Based CDT mechanism could acquire good CDT performance. Finally a CWND-Based CDT mechanism, which was based on a path's CWND (Congestion Window), to optimize CDT performance was introduced. This mechanism primarily included: a CWND-Based load allocation scheme, which assigned corresponding data to paths based on their CWND to achieve aggregate bandwidth; a CWND-Based path management, which was used to optimize connections' fault tolerance; and a congestion control and retransmission management policy, which was similar to regular TCP in its separate path handling. According to corresponding experiment results, this mechanism could acquire near-optimal CDT performance under different network conditions.

Ranked search on data graphs

Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity—users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. The purpose of this dissertation was to develop techniques for user-friendly, high quality and efficient searching of graph structured databases. Several ranked search methods on data graphs have been studied in the recent years. Given a top-k keyword search query on a graph and some ranking criteria, a keyword proximity search finds the top-k answers where each answer is a substructure of the graph containing all query keywords, which illustrates the relationship between the keyword present in the graph. We applied keyword proximity search on the web and the page graph of web documents to find top-k answers that satisfy user’s information need and increase user satisfaction. Another effective ranking mechanism applied on data graphs is the authority flow based ranking mechanism. Given a top- k keyword search query on a graph, an authority-flow based search finds the top-k answers where each answer is a node in the graph ranked according to its relevance and importance to the query. We developed techniques that improved the authority flow based search on data graphs by creating a framework to explain and reformulate them taking in to consideration user preferences and feedback. We also applied the proposed graph search techniques for Information Discovery over biological databases. Our algorithms were experimentally evaluated for performance and quality. The quality of our method was compared to current approaches by using user surveys.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.