Computing and Information Systems - Theses
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ItemDigital forensics: increasing the evidential weight of system activity logs( 2007)The application of investigative techniques within digital environments has lead to the emergence of a new field of specialization that may be termed ‘digital forensics’. Perhaps the primary challenge concerning digital forensic investigations is how to preserve evidence of system activity given the volatility of digital environments and the delay between the time of the incident and the start of the forensic investigation. This thesis hypothesizes that system activity logs present in modern operating systems may be used for digital forensic evidence collection. This is particularly true in modern organizations where there is growing recognition that forensic readiness may have considerable benefits in case of future litigation. An investigation into the weighting of evidence produced by system activity logs present in modern operating systems takes place in this thesis. The term ‘evidential weight’ is used loosely as a measure of the suitability of system activity logs to digital forensic investigations. This investigation is approached from an analytical perspective. The first contribution of this thesis is to determine the evidence collection capability of system activity logs by a simple model of the logging mechanism. The second contribution is the development of evidential weighting criteria that can be applied to system activity logs. A unique and critical role for system activity logs by which they establish the reliability of other kinds of computer-derived evidence from hard disk media is also identified. The primary contribution of this thesis is the identification of a comprehensive range of forensic weighting issues arising from the use of log evidence that concern investigators and legal authorities. This contribution is made in a comprehensive analytical discussion utilizing both the logging model and the evidential weighting criteria. The practical usefulness of the resulting evidential weighting framework is demonstrated by rigorous and systematic application to a real-world logging system.
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ItemUnderstanding the business benefits of ERP system use( 2006)ERP systems are large, complex, integrated software packages used for business transaction processing by thousands of major organizations worldwide. Yet outcomes from ERP system implementation and use can be very different, and current understanding of how and why such variation exists is limited. Since most studies of ERP systems to date have focused on ERP implementation, this research focused on the post-implementation period. The aim was to better understand the 'what', 'how' and 'why' of achieving business benefits from ERP systems during ERP use. Achieving business benefits from ERP systems was considered as a process of organizational change occurring over time within various societal and organizational contexts. A retrospective, interpretive case study approach was used to study this process. The post-implementation periods of four Australian manufacturing organizations that had implemented ERP systems were studied. This study makes three important contributions to the information systems research literature. First, a new framework was developed to explain 'how' and 'why' business benefits were achieved from ERP systems. This explanatory framework is theoretically based and is firmly grounded in the empirical data. Three types of themes, along with the interrelationships between them, were identified as influencing the business benefits achieved from ERP systems. The first group of themes, the process themes, are 'Education, training and support', 'Technochange management' and 'People resources'. The second group of themes, the outcome themes, are 'Efficient and effective use of the ERP system', 'Business process improvement' and 'New projects to leverage off the ERP system'. The third group of themes, the contextual themes, are the 'External context', the 'Internal context' and the 'ERP planning and implementation phases'. This new framework makes a significant contribution to understanding how and why some organizations achieve more business benefits from ERP systems than others. Second, the case studies provide a rich description of four manufacturing organizations that have implemented and used ERP systems. Examining the 'what' of business benefits from ERP systems in these four manufacturing organizations resulted in a confirmed, amended and improved Shang and Seddon (2000) ERP business benefits framework. This replication and extension of previous research is the third contribution of this study. The results of this research are of interest not only to information systems researchers, but also to information systems practitioners and senior management in organizations that either plan to, or have implemented ERP systems. Overall this research provides an improved understanding of business benefits from ERP systems and a sound foundation for future studies of ERP system use.
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ItemQoS-based scheduling of workflows on global grids( 2007-10)Grid computing has emerged as a global cyber-infrastructure for the next-generation of e-Science applications by integrating large-scale, distributed and heterogeneous resources. Scientific communities are utilizing Grids to share, manage and process large data sets. In order to support complex scientific experiments, distributed resources such as computational devices, data, applications, and scientific instruments need to be orchestrated while managing the application workflow operations within Grid environments. This thesis investigates properties of Grid workflow management systems, presents a workflow engine and algorithms for mapping scientific workflow applications to Grid resources based on specified QoS (Quality of Service) constraints. (For complete abstract open document)
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ItemCoordinated resource provisioning in federated grids( 2007-07)A fundamental problem in building large scale Grid resource sharing system is the need for efficient and scalable techniques for discovery and provisioning of resources for delivering expected Quality of Service (QoS) to users’ applications. The current approaches to Grid resource sharing based on resource brokers are non-coordinated since these brokers make scheduling related decisions independent of the others in the system. Clearly, this worsens the load-sharing and utilisation problems of distributed Grid resources as sub-optimal schedules are likely to occur. Further, existing brokering systems rely on centralised information services for resource discovery. Centralised or hierarchical resource discovery systems are prone to single-point failure, lack scalability and fault-tolerance ability. In the centralised model, the network links leading to the server are very critical to the overall functionality of the system, as their failure might halt the entire distributed system operation.
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ItemStigmergic collaboration: a theoretical framework for mass collaboration( 2007-12)This thesis presents an application-oriented theoretical framework for generalised and specific collaborative contexts with a special focus on Internet-based mass collaboration. The proposed framework is informed by the author’s many years of collaborative arts practice and the design, building and moderation of a number of online collaborative environments across a wide range of contexts and applications. The thesis provides transdisciplinary architecture for describing the underlying mechanisms that have enabled the emergence of mass collaboration and other activities associated with ‘Web 2.0’ by incorporating a collaboratively developed definition and general framework for collaboration and collective activity, as well as theories of swarm intelligence, stigmergy, and distributed cognition. (For complete abstract open document)
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ItemStructured classification for multilingual natural language processing( 2007-06)This thesis investigates the application of structured sequence classification models to multilingual natural language processing (NLP). Many tasks tackled by NLP can be framed as classification, where we seek to assign a label to a particular piece of text, be it a word, sentence or document. Yet often the labels which we’d like to assign exhibit complex internal structure, such as labelling a sentence with its parse tree, and there may be an exponential number of them to choose from. Structured classification seeks to exploit the structure of the labels in order to allow both generalisation across labels which differ by only a small amount, and tractable searches over all possible labels. In this thesis we focus on the application of conditional random field (CRF) models (Lafferty et al., 2001). These models assign an undirected graphical structure to the labels of the classification task and leverage dynamic programming algorithms to efficiently identify the optimal label for a given input. We develop a range of models for two multilingual NLP applications: word-alignment for statistical machine translation (SMT), and multilingual super tagging for highly lexicalised grammars.
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ItemA declarative debugger for Haskell( 2006-12)This thesis considers the design and implementation of a Declarative Debugger for Haskell. At its core is a tree which captures the logical dependencies between function calls in a given execution of the program being debugged (the debuggee). The debuggee is transformed into a new Haskell program which produces the tree in addition to its normal value. A bug is identified in the tree when a call returns the wrong result but all the calls it depends upon are correct.
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ItemScheduling distributed data-intensive applications on global grids( 2006-07)The next generation of scientific experiments and studies are being carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for such collaborations as it aids communities in sharing resources to achieve common objectives. Data Grids provide services for accessing, replicating and managing data collections in these collaborations. Applications used in such Grids are distributed data-intensive, that is, they access and process distributed datasets to generate results. These applications need to transparently and efficiently access distributed data and computational resources. This thesis investigates properties of data-intensive computing environments and presents a software framework and algorithms for mapping distributed data-oriented applications to Grid resources. (For complete abstract open document)
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ItemOnline vicarious-experience: using technology to help consumers evaluate physical products over the Internet( 2006-09)This research investigates ways to help shoppers evaluate physical products via the Internet. The primary research issue is, therefore, how to provide experience vicariously. The study was undertaken in three parts. First, an extensive range of Web sites belonging to Internet-based retailers was examined, together with literature on vicarious experience and Web page design. These helped to explore the question of ‘What components of Web-based representations of physical products might assist shoppers when trying to evaluate those products as part of a purchase decision?’ Online store systems that are representative of the main communication styles found in the Web survey were then evaluated in a series of laboratory-based experiments. This second part of the study makes a broad assessment of the impact of representative technologies on the product evaluation process. Finally, a smaller-scale, more targeted investigation was conducted, also using a laboratory-based experiment. This third part of the study assesses the impact of an individual’s evaluation style on the perceived success of representative technologies.
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ItemScaling conditional random fields for natural language processing( 2007-01)This thesis deals with the use of Conditional Random Fields (CRFs; Lafferty et al. (2001)) for Natural Language Processing (NLP). CRFs are probabilistic models for sequence labelling which are particularly well suited to NLP. They have many compelling advantages over other popular models such as Hidden Markov Models and Maximum Entropy Markov Models (Rabiner, 1990; McCallum et al., 2001), and have been applied to a number of NLP tasks with considerable success (e.g., Sha and Pereira (2003) and Smith et al. (2005)). Despite their apparent success, CRFs suffer from two main failings. Firstly, they often over-fit the training sample. This is a consequence of their considerable expressive power, and can be limited by a prior over the model parameters (Sha and Pereira, 2003; Peng and McCallum, 2004). Their second failing is that the standard methods for CRF training are often very slow, sometimes requiring weeks of processing time. This efficiency problem is largely ignored in current literature, although in practise the cost of training prevents the application of CRFs to many new more complex tasks, and also prevents the use of densely connected graphs, which would allow for much richer feature sets. (For complete abstract open document)