Using Agents for Mining Maintenance Data while interacting in 3D Objectoriented Virtual Environments

This report demonstrates the development of: (a) object-oriented representation to provide 3D interactive environment using data provided by Woods Bagot; (b) establishing basis of agent technology for mining building maintenance data, and (C) 3D interaction in virtual environments using object-oriented representation. Applying data mining over industry maintenance database has been demonstrated in the previous report.

Developing Software Agents for Data Mining : [AIMM] Agents for Improving Maintenance Management

This report demonstrates the development of: • Development of software agents for data mining • Link data mining to building model in virtual environments • Link knowledge development with building model in virtual environments • Demonstration of software agents for data mining • Populate with maintenance data

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.

Barriers and drivers of new public-private infrastructure: sewer mining

The rate of water reform in Australia is gathering pace with Federal and State initiatives promoting a more integrated approach to water management. This approach encompasses a more competitive environment and a greater role for the private sector. There is a growing recognition of the importance of water recycling in these initiatives and the need to provide opportunities for its development. In March 2008 the Productivity Commission published its discussion paper on urban water reform (Productivity Commission, 2008). The paper cited inadequate institutional arrangements for the management of Australian urban water resources and noted the benefits to be gained from a comprehensive public review of urban water management. This development can be supported through the promotion of a sewer mining industry. This industry, offers flexible and innovative solutions to water recycling demands in a variety of situations and structures. In addition it has the capability of satisfying government competition and private sector policy initiatives.

Preliminary report on data mining techniques for life prediction

This project is an extension of a previous CRC project (220-059-B) which developed a program for life prediction of gutters in Queensland schools. A number of sources of information on service life of metallic building components were formed into databases linked to a Case-Based Reasoning Engine which extracted relevant cases from each source. In the initial software, no attempt was made to choose between the results offered or construct a case for retention in the casebase. In this phase of the project, alternative data mining techniques will be explored and evaluated. A process for selecting a unique service life prediction for each query will also be investigated. This report summarises the initial evaluation of several data mining techniques.

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Submission to Brisbane City Council : Draft Brisbane Economic Development Plan

As with any strategic planning process, evidence-based estimates are needed to plan effectively for the future. Comments below are based upon data drawn from the Brisbane Long Term Infrastructure Plan (Department of Local Government, Planning, Sport and Recreation, 2005) and the Brisbane Long Term Planning Economic Indicators (National Institute of Economic and Industry Research, 2005), as these are cited as the underpinning research for the economic plan. This submission focuses on one critical aspect of the strategic plan — the relationship between population growth, employment growth, and infrastructure provision. While the focus of the strategic plan is on the changes which would occur within Brisbane, it is important that consideration of predicted changes in surrounding local government areas be also carried out.

Data mining on prices of highway project main material based on WEB

The management of main material prices of provincial highway project quota has problems of lag and blindness. Framework of provincial highway project quota data MIS and main material price data warehouse were established based on WEB firstly. Then concrete processes of provincial highway project main material prices were brought forward based on BP neural network algorithmic. After that standard BP algorithmic, additional momentum modify BP network algorithmic, self-adaptive study speed improved BP network algorithmic were compared in predicting highway project main prices. The result indicated that it is feasible to predict highway main material prices using BP NN, and using self-adaptive study speed improved BP network algorithmic is the relatively best one.

Beyond the inner city : real and imagined places in creative place policy and practice

As the economic and social benefits of creative industries development become increasingly visible, policymakers worldwide are working to create policy drivers to ensure that certain places become or remain ‘creative places’. Richard Florida’s work has become particularly influential among policymakers, as has Landry’s. But as the first wave of creative industrial policy development and implementation wanes, important questions are emerging. It is by now clear that an ‘ideal creative place’ has arisen from creative industries policy and planning literature, and that this ideal place is located in inner cities. This article shifts its focus away from the inner city to where most Australians live: the outer suburbs. It reports on a qualitative research study into the practices of outer-suburban creative industries workers in Redcliffe, Australia. It argues that the accepted geography of creative places requires some recalibration once the material and experiential aspects of creative places are taken into account.

Green a city grow a wall

As Brisbane grows, it is rapidly becoming akin to any other city in the world with its typical stark grey concrete buildings rather than being characterized by its subtropical element of abundant green vegetation. Living Walls can play a vital role in restoring the loss of this distinct local element of a subtropical city. This paper will start by giving an overview of the traditional methods of greening subtropical cities with the use of urban parks and street trees. Then, by examining a recent heat imaging map of Brisbane, the effect of green cover with the built environment will be shown. With this information from a macro level, this paper will proceed to examine a typical urban block within the Central Business District (CBD) to demonstrate urban densification in relation to greenery in the city. Then, this paper will introduce the new technology where Living Walls have the untapped potential of effectively greening a city where land is scarce and given over to high density development. Living Walls incorporated into building design does not only enhance the subtropical lifestyle that is being lost in modern cities but is also an effective means for addressing climate change. This paper will serve as a preliminary investigation into the effects of incorporating Living Walls into cities. By growing a Living Wall onto buildings, we can be part of an effective design solution for countering global warming and at the same time, Living Walls can return local character to subtropical cities, thereby greening the city as well.

The city is connections : Seoul as an urban network

With the rise of ubiquitous computing in recent years, concepts of spatiality have become a significant topic of discussion in design and development of multimedia systems. This article investigates spatial practices at the intersection of youth, technology, and urban space in Seoul, and examines what the author calls ‘transyouth’: in the South Korean context, these people are between the ages of 18 and 24, situated on the delicate border between digital natives and immigrants in Prensky’s (2001) terms. In the first section, the article sets out the technosocial environment of contemporary Seoul. This is followed by a discussion of social networking processes derived from semi-structured interviews conducted in 2007-8 with Seoul transyouth about their ‘lived experiences of the city.’ Interviewees reported how they interact to play, work, and live with and within the city’s unique environment. The article develops a theme of how technosocial convergence (re)creates urban environments and argues for a need to consider such user-driven spatial recreation in designing cities as (ubiquitous) urban networks in recognition of its changing technosocial contours of connections. This is explored in three spaces of different scales: Cyworld as an online social networking space; cocoon housing – a form of individual residential space which is growing rapidly in many Korean cities – as a private living space; and u-City (ubiquitous City) as the future macro-space of Seoul.

Mining trading partners’ preferences for efficient multi-issue bargaining in E‑business

Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e‑marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e‑marketplaces.

Mining Negative Relevance Feedback for Information Filtering

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

State-of-the-Art Review on Opinion Mining from Online Customers’ Feedback

Dealing with the ever-growing information overload in the Internet, Recommender Systems are widely used online to suggest potential customers item they may like or find useful. Collaborative Filtering is the most popular techniques for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating about a service provider, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service providers’. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a state of the art review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and sub-categories. Each of those steps is analyzed with their strength and limitations in this paper.