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.

Foucault Goes to Law School : Using Foucault to Examine Australian Legal Education

This paper will consider the way that Foucault’s work has been utilised to examine Australian legal education, particularly in the context of understanding the construction of the legal identity. While remaining sensitive to the many potential ‘uses’ of Foucault’s tools, as well as his problematisation of the author as an organising feature of discourse, this paper will argue that legal education scholarship overwhelmingly utilises concepts such as ‘discourse’ and ‘power-knowledge’, which, while useful, cannot provide a nuanced understanding of the construction of the legal identity. Consequently, this paper suggests that future legal education research utilise Foucault’s concepts of ‘ethics’ and ‘governmentality’ to address these issues.

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

Contemporary consumer law : the role and effectiveness of regulation in consumer protection

This issue of the Griffith Law Review focuses on consumer law, and the pervasive nature of this area of law. We are all consumers, but do not necessarily identify as such, nor are we a homogeneous group. The boundaries of

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.

Termination of a minor’s pregnancy : critical issues for consent and the criminal law

The recent Supreme Court decision of Queensland v B [2008] 2 Qd R 562 has significant implications for the law that governs consent and abortions. The judgment purports to extend the ratio of Secretary, Department of Health and Community Services (NT) v JWB and SMB (1991) 175 CLR 218 (Marion’s Case) and impose a requirement of court approval for terminations of pregnancy for minors who are not Gillick-competent. This article argues against the imposition of this requirement on the ground that such an approach is an unjustifiable extension of the reasoning in Marion’s Case. The decision, which is the first judicial consideration in Queensland of the position of medical terminations, also reveals systemic problems with the criminal law in that State. In concluding that the traditional legal excuse for abortions will not apply to those which are performed medically, Queensland v B provides further support for calls to reform this area of law.

A University Support Model for eResearch

Queensland University of Technology (QUT) is faced with a rapidly growing research agenda built upon a strategic research capacity-building program. This presentation will outline the results of a project that has recently investigated QUT’s research support requirements and which has developed a model for the support of eResearch across the university. QUT’s research building strategy has produced growth at the faculty level and within its research institutes. This increased research activity is pushing the need for university-wide eResearch platforms capable of providing infrastructure and support in areas such as collaboration, data, networking, authentication and authorisation, workflows and the grid. One of the driving forces behind the investigation is data-centric nature of modern research. It is now critical that researchers have access to supported infrastructure that allows the collection, analysis, aggregation and sharing of large data volumes for exploration and mining in order to gain new insights and to generate new knowledge. However, recent surveys into current research data management practices by the Australian Partnership for Sustainable Repositories (APSR) and by QUT itself, has revealed serious shortcomings in areas such as research data management, especially its long term maintenance for reuse and authoritative evidence of research findings. While these internal university pressures are building, at the same time there are external pressures that are magnifying them. For example, recent compliance guidelines from bodies such as the ARC, and NHMRC and Universities Australia indicate that institutions need to provide facilities for the safe and secure storage of research data along with a surrounding set of policies, on its retention, ownership and accessibility. The newly formed Australian National Data Service (ANDS) is developing strategies and guidelines for research data management and research institutions are a central focus, responsible for managing and storing institutional data on platforms that can be federated nationally and internationally for wider use. For some time QUT has recognised the importance of eResearch and has been active in a number of related areas: ePrints to digitally publish research papers, grid computing portals and workflows, institutional-wide provisioning and authentication systems, and legal protocols for copyright management. QUT also has two widely recognised centres focused on fundamental research into eResearch itself: The OAK LAW project (Open Access to Knowledge) which focuses upon legal issues relating eResearch and the Microsoft QUT eResearch Centre whose goal is to accelerate scientific research discovery, through new smart software. In order to better harness all of these resources and improve research outcomes, the university recently established a project to investigate how it might better organise the support of eResearch. This presentation will outline the project outcomes, which include a flexible and sustainable eResearch support service model addressing short and longer term research needs, identification of resource requirements required to establish and sustain the service, and the development of research data management policies and implementation plans.

Power law distributions in pattern dynamics of attacker-defender dyads in the team sport of Rugby Union : phenomena in a region of self-organized criticality?

In the region of self-organized criticality (SOC) interdependency between multi-agent system components exists and slight changes in near-neighbor interactions can break the balance of equally poised options leading to transitions in system order. In this region, frequency of events of differing magnitudes exhibits a power law distribution. The aim of this paper was to investigate whether a power law distribution characterized attacker-defender interactions in team sports. For this purpose we observed attacker and defender in a dyadic sub-phase of rugby union near the try line. Videogrammetry was used to capture players’ motion over time as player locations were digitized. Power laws were calculated for the rate of change of players’ relative position. Data revealed that three emergent patterns from dyadic system interactions (i.e., try; unsuccessful tackle; effective tackle) displayed a power law distribution. Results suggested that pattern forming dynamics dyads in rugby union exhibited SOC. It was concluded that rugby union dyads evolve in SOC regions suggesting that players’ decisions and actions are governed by local interactions rules.

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.