Memory management and parallelization of data intensive all-to-all comparison in shared-memory systems

This thesis presents a novel program parallelization technique incorporating with dynamic and static scheduling. It utilizes a problem specific pattern developed from the prior knowledge of the targeted problem abstraction. Suitable for solving complex parallelization problems such as data intensive all-to-all comparison constrained by memory, the technique delivers more robust and faster task scheduling compared to the state-of-the art techniques. Good performance is achieved from the technique in data intensive bioinformatics applications.

Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

Optimizing I/O cost and managing memory for composition vector method based on correlation matrix calculation in bioinformatics

The generation of a correlation matrix for set of genomic sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. Each sequence may be millions of bases long and there may be thousands of such sequences which we wish to compare, so not all sequences may fit into main memory at the same time. Each sequence needs to be compared with every other sequence, so we will generally need to page some sequences in and out more than once. In order to minimize execution time we need to minimize this I/O. This paper develops an approach for faster and scalable computing of large-size correlation matrices through the maximal exploitation of available memory and reducing the number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different bioinformatics problems with different correlation matrix sizes. The significant performance improvement of the approach over previous work is demonstrated through benchmark examples.

Capping computation time and storage requirements for appearance-based localization with CAT-SLAM

Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.

Letters & Numbers : a vehicle to illustrate mathematical and computing fundamentals

The television quiz program Letters and Numbers, broadcast on the SBS network, has recently become quite popular in Australia. This paper explores the potential of this game to illustrate and engage student interest in a range of fundamental concepts of computer science and mathematics. The Numbers Game in particular has a rich mathematical structure whose analysis and solution involves concepts of counting and problem size, discrete (tree) structures, language theory, recurrences, computational complexity, and even advanced memory management. This paper presents an analysis of these games and their teaching applications, and presents some initial results of use in student assignments.

In-memory business process management

In-memory databases have become a mainstay of enterprise computing offering significant performance and scalability boosts for online analytical and (to a lesser extent) transactional processing as well as improved prospects for integration across different applications through an efficient shared database layer. Significant research and development has been undertaken over several years concerning data management considerations of in-memory databases. However, limited insights are available on the impacts of applications and their supportive middleware platforms and how they need to evolve to fully function through, and leverage, in-memory database capabilities. This paper provides a first, comprehensive exposition into how in-memory databases impact Business Pro- cess Management, as a mission-critical and exemplary model-driven integration and orchestration middleware. Through it, we argue that in-memory databases will render some prevalent uses of legacy BPM middleware obsolete, but also open up exciting possibilities for tighter application integration, better process automation performance and some entirely new BPM capabilities such as process-based application customization. To validate the feasibility of an in-memory BPM, we develop a surprisingly simple BPM runtime embedded into SAP HANA and providing for BPMN-based process automation capabilities.

Proactive communication management beats hostile media exposure : training for multi-cultural community leaders in living with mass media

Proactive communication management instead of mortification in the glare of hostile media attention became the theme of a four-day training program for multi-cultural community leaders, the object of this research. The program in Brisbane from December 2009 through to February this year was conducted under auspices of a Community Media Link grant program shared by Griffith University and the Queensland Ethnic Communities Council, together with Journalism academics from the Queensland University of Technology. Twenty-eight participants from 23 organisations took part, with a team of nine facilitators from the host organisations, and guest presenters from the news media. This paper reviews the process, taking into account: its objectives, to empower participants by showing how Australian media operate and introducing participants to journalists; pedagogical thrust, where overview talks, with role play seminars with guest presenters from the media, were combined with practice in interviews and writing for media; and outcomes, assessed on the basis of participants’ responses. The research methodology is qualitative, in that the study is based on discussions to review the planning and experience of sessions, and anonymous, informal feed-back questionnaires distributed to the participants. Background literature on multiculturalism and community media was referred to in the study. The findings indicate positive outcomes for participants from this approach to protection of persons unversed in living in the Australian “mediatised” environment. Most affirmed that the “production side” perspective of the exercise had informed and motivated them effectively, such that henceforth they would venture far more into media management, in their community leadership roles.

Cork as canvas : exploring intersections of citizenship and collective memory in the Shandon Big Wash Up murals

Urban space has the potential to shape people's experience and understanding of the city and of the culture of a place. In some respects, murals and allied forms of wall art occupy the intersection of street art and public art; engaging, and sometimes, transforming the urban space in which they exist and those who use it. While murals are often conceived as a more ‘permanent’ form of painted art there has been a trend in recent years towards more deliberately transient forms of wall art such as washed-wall murals and reverse graffiti. These varying forms of public wall art are embedded within the fabric of the urban space and history. This paper will explore the intersection of public space, public art and public memory in a mural project in the Irish city of Cork. Focussing on the washed-wall murals of Cork's historic Shandon district, we explore the sympathetic and synergetic relationship of this wall art with the heritage architecture of the built environment and of the murals as an expression of and for the local community, past and present. Through the Shandon Big Wash Up murals we reflect on the function of participatory public art as an explicit act of urban citizenship which works to support community-led re-enchantment in the city through a reconnection with its past.

Developing transactive memory systems : theoretical contributions from a social identity perspective

Transactive memory system (TMS) theory explains how expertise is recognized and coordinated in teams. Extending current TMS research from a group information-processing perspective, our article presents a theoretical model that considers TMS development from a social identity perspective. We discuss how two features of communication (quantity and quality) important to TMS development are linked to TMS through the group identification mechanism of a shared common team identity. Informed by social identity theory, we also differentiate between intragroup and intergroup contexts and outline how, in multidisciplinary teams, professional identification and perceived equality of status among professional subgroups have a role to play in TMS development. We provide a theoretical discussion of future research directions aimed at testing and extending our model.

Predicting transactive memory systems in multidisciplinary teams : The interplay between team and professional identities

In explaining how communication quality predicts TMS in multidisciplinary teams, we drew on the social identity approach to investigate the mediating role of team identification and the moderating role of professional identification. Recognizing that professional identification could trigger intergroup biases among professional subgroups, or alternatively, could bring resources to the team, we explored the potential moderating role of professional identification in the relationship between team identification and TMS. Using data collected from 882 healthcare personnel working in 126 multidisciplinary hospital teams, results supported our hypothesis that perceived communication quality predicted TMS through team identification. Furthermore, findings provided support for a resource view of professional subgroup identities with results indicating that high levels of professional identification compensated for low levels of team identification in predicting TMS. We provide recommendations on how social identities may be used to promote TMS in multidisciplinary teams.

Virtual business role-play : leveraging familiar environments to prime stakeholder memory during process elicitation

Business process models have traditionally been an effective way of examining business practices to identify areas for improvement. While common information gathering approaches are generally efficacious, they can be quite time consuming and have the risk of developing inaccuracies when information is forgotten or incorrectly interpreted by analysts. In this study, the potential of a role-playing approach for process elicitation and specification has been examined. This method allows stakeholders to enter a virtual world and role-play actions as they would in reality. As actions are completed, a model is automatically developed, removing the need for stakeholders to learn and understand a modelling grammar. Empirical data obtained in this study suggests that this approach may not only improve both the number of individual process task steps remembered and the correctness of task ordering, but also provide a reduction in the time required for stakeholders to model a process view.