A framework for fully decentralised cycle stealing

Ordinary desktop computers continue to obtain ever more resources – in-creased processing power, memory, network speed and bandwidth – yet these resources spend much of their time underutilised. Cycle stealing frameworks harness these resources so they can be used for high-performance computing. Traditionally cycle stealing systems have used client-server based architectures which place significant limits on their ability to scale and the range of applica-tions they can support. By applying a fully decentralised network model to cycle stealing the limits of centralised models can be overcome. Using decentralised networks in this manner presents some difficulties which have not been encountered in their previous uses. Generally decentralised ap-plications do not require any significant fault tolerance guarantees. High-performance computing on the other hand requires very stringent guarantees to ensure correct results are obtained. Unfortunately mechanisms developed for traditional high-performance computing cannot be simply translated because of their reliance on a reliable storage mechanism. In the highly dynamic world of P2P computing this reliable storage is not available. As part of this research a fault tolerance system has been created which provides considerable reliability without the need for a persistent storage. As well as increased scalability, fully decentralised networks offer the ability for volunteers to communicate directly. This ability provides the possibility of supporting applications whose tasks require direct, message passing style communication. Previous cycle stealing systems have only supported embarrassingly parallel applications and applications with limited forms of communication so a new programming model has been developed which can support this style of communication within a cycle stealing context. In this thesis I present a fully decentralised cycle stealing framework. The framework addresses the problems of providing a reliable fault tolerance sys-tem and supporting direct communication between parallel tasks. The thesis includes a programming model for developing cycle stealing applications with direct inter-process communication and methods for optimising object locality on decentralised networks.

Marketplace forces and the history of retailing : The cycle of control

This paper traces the history of store (retailer-controlled) and national (manufacture controlled)brands; identifies the key historical characteristics of the past 200 years of marketing history;describes the four main time periods of U.S. retail marketing (1800 - 2000); and comments on the most likely developments within the current phases of brand marketing. Will the future focus on technology and new forms of communications? The Internet exemplifies an unconventional retailing environment, with etailer numbers growing rapidly. The central proposition of this paper is that a "cycle of control" - a pattern of marketing developments within the history of retailing and national marketing communications - Can indicate the success of marketing strategies in the future.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Whole of life cycle cost analysis in bridge rehabilitation

The report presents a methodology for whole of life cycle cost analysis of alternative treatment options for bridge structures, which require rehabilitation. The methodology has been developed after a review of current methods and establishing that a life cycle analysis based on a probabilistic risk approach has many advantages including the essential ability to consider variability of input parameters. The input parameters for the analysis are identified as initial cost, maintenance, monitoring and repair cost, user cost and failure cost. The methodology utilizes the advanced simulation technique of Monte Carlo simulation to combine a number of probability distributions to establish the distribution of whole of life cycle cost. In performing the simulation, the need for a powerful software package, which would work with spreadsheet program, has been identified. After exploring several products on the market, @RISK software has been selected for the simulation. In conclusion, the report presents a typical decision making scenario considering two alternative treatment options.

Strategies for minimising the whole of life cycle cost of reinforced concrete bridge exposed to aggressive environments

n design of bridge structures, it is common to adopt a 100 year design life. However, analysis of a number of case study bridges in Australia has indicated that the actual design life can be significantly reduced due to premature deterioration resulting from exposure to aggressive environments. A closer analysis of the cost of rehabilitation of these structures has raised some interesting questions. What would be the real service life of a bridge exposed to certain aggressive environments? What is the strategy of conducting bridge rehabilitation? And what are the life cycle costs associated with rehabilitation? A research project funded by the CRC for Construction Innovation in Australia is aimed at addressing these issues. This paper presents a concept map for assisting decision makers to appropriately choose the best treatment for bridge rehabilitation affected by premature deterioration through exposure to aggressive environments in Australia. The decision analysis is referred to a whole of life cycle cost analysis by considering appropriate elements of bridge rehabilitation costs. In addition, the results of bridges inspections in Queensland are presented

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues.

Life cycle inventory for Australian building materials

This paper discusses challenges to developers of a national Life Cycle Inventory (LCI) database on which to base assessment of building environmental impacts and a key to development of a fully integrated eco-design tool created for automated eco-efficiency assessment of commercial building design direct from 3D CAD. The scope of this database includes Australian and overseas processing burdens involved in acquiring, processing, transporting, fabricating, finishing and using metals, masonry, timber, glazing, ceramics, plastics, fittings, composites and coatings. Burdens are classified, calculated and reported for all flows of raw materials, fuels, energy and emissions to and from the air, soil and water associated with typical products and services in building construction, fitout and operation. The aggregated life cycle inventory data provides the capacity to generate environmental impact assessment reports based on accepted performance indicators. Practitioners can identify hot spots showing high environmental burdens of a proposed design and drill down to report on specific building components. They can compare assessments with case studies and operational estimates to assist in eco-efficient design of a building, fitout and operation.

Redefining life cycle for a building sustainability assessment framework

Understanding the differences between the temporal and physical aspects of the building life cycle is an essential ingredient in the development of Building Environmental Assessment (BEA) tools. This paper illustrates a theoretical Life Cycle Assessment (LCA) framework aligning temporal decision-making with that of material flows over building development phases. It was derived during development of a prototype commercial building design tool that was based on a 3-D CAD information and communications technology (ICT) platform and LCA software. The framework aligns stakeholder BEA needs and the decision-making process against characteristics of leading green building tools. The paper explores related integration of BEA tool development applications on such ICT platforms. Key framework modules are depicted and practical examples for BEA are provided for: • Definition of investment and service goals at project initiation; • Design integrated to avoid overlaps/confusion over the project life cycle; • Detailing the supply chain considering building life cycle impacts; • Delivery of quality metrics for occupancy post-construction/handover; • Deconstruction profiling at end of life to facilitate recovery.

A comparison of two business cycle dating methods

We study the suggestion that Markov switching (MS) models should be used to determine cyclical turning points. A Kalman filter approximation is used to derive the dating rules implicit in such models. We compare these with dating rules in an algorithm that provides a good approximation to the chronology determined by the NBER. We find that there is very little that is attractive in the MS approach when compared with this algorithm. The most important difference relates to robustness. The MS approach depends on the validity of that statistical model. Our approach is valid in a wider range of circumstances.

Developing a life-cycle costing analysis model for sustainability enhancement in road infrastructure project

With increasing pressure to provide environmentally responsible infrastructure products and services, stakeholders are putting significant foci on the early identification of financial viability and outcome of infrastructure projects. Traditionally, there has been an imbalance between sustainable measures and project budget. On one hand, the industry tends to employ the first-cost mentality and approach to developing infrastructure projects. On the other, environmental experts and technology innovators often push for the ultimately green products and systems without much of a concern for cost. This situation is being quickly changed as the industry is under pressure to continue to return profit, while better adapting to current and emerging global issues of sustainability. For the infrastructure sector to contribute to sustainable development, it will need to increase value and efficiency. Thus, there is a great need for tools that will enable decision makers evaluate competing initiatives and identify the most sustainable approaches to procuring infrastructure projects. In order to ensure that these objectives are achieved, the concept of life-cycle costing analysis (LCCA) will play significant roles in the economics of an infrastructure project. Recently, a few research initiatives have applied the LCCA models for road infrastructure that focused on the traditional economics of a project. There is little coverage of life-cycle costing as a method to evaluate the criteria and assess the economic implications of pursuing sustainability in road infrastructure projects. To rectify this problem, this paper reviews the theoretical basis of previous LCCA models before discussing their inability to determinate the sustainability indicators in road infrastructure project. It then introduces an on-going research aimed at developing a new model to integrate the various new cost elements based on the sustainability indicators with the traditional and proven LCCA approach. It is expected that the research will generate a working model for sustainability based life-cycle cost analysis.

Incorporating sustainability measures in life-cycle financial decision making for highway construction

Public awareness and the nature of highway construction works demand that sustainability measures are first on the development agenda. However, in the current economic climate, individual volition and enthusiasm for such high capital investments do not present as strong cases for decision making as the financial pictures of pursuing sustainability. Some stakeholders consider sustainability to be extra work that costs additional money. Though, stakeholders realised its importance in infrastructure development. They are keen to identify the available alternatives and financial implications on a lifecycle basis. Highway infrastructure development is a complex rocess which requires expertise and tools to evaluate investment options, such as environmentally sustainable features for road and highway development. Life-cycle cost analysis (LCCA) is a valuable approach for investment decision making for construction works. However, LCCA applications in highway development are still limited. Current models, for example focus on economic issues alone and do not deal with sustainability factors, which are more difficult to quantify and encapsulate in estimation modules. This paper reports the research which identifies sustainability related factors in highway construction projects, in quantitative and qualitative forms of a multi-criteria analysis. These factors are then incorporated into past and proven LCCA models to produce a new long term decision support model. The research via questionnaire, model building, analytical hierarchy processes (AHP) and case studies have identified, evaluated and then processed highway sustainability related cost elements. These cost elements need to be verified by industry before being integrated for further development of the model. Then the Australian construction industry will have a practical tool to evaluate investment decisions which provide an optimum balance between financial viability and sustainability deliverables.