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

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.

Responding to an ageing workforce in an Australian state

Many industrialised nations have changing demographic profiles, as increased longevity and decreased birth rates lead to an ageing population. This presents significant challenges for workforces, as older employees retire and there are insufficient numbers of younger employees to take their place. This leads to skills shortages, and strong competition for those who are available. This paper considers these issues in the context of Queensland, the third largest state of Australia. The Queensland Government is addressing the issues for all industries in the state, primarily through a Skills Plan and an Experience Pays Awareness Strategy. As the largest employer in the state, the Queensland Government has commenced implementing the Experience Pays Awareness Strategy within its own workforce. The approach touches on many facets of HRM. The HRM policy framework and tools are examined for their potential to support increased participation of older employees. A range of issues are addressed for older workers, including their competence and health and safety issues. Issues for managers include addressing myths and subtle discrimination against older workers, as well as managing cross-generational workforce. Other strategies and methods are targeted at cultural factors, such as the expectations of older workers, and the myths and discrimination against older workers. Yet other strategies are aimed at organisational issues such retention of knowledge and succession planning.

The failure of Australian legislation on indirect discrimination to detect the systemic racism which prevents Aboriginal people from fully participating in the workforce

Government figures put the current indigenous unemployment rate at around 23%, 3 times the unemployment rate for other Australians. This thesis aims to assess whether Australian indirect discrimination legislation can provide a remedy for one of the causes of indigenous unemployment - the systemic discrimination which can result from the mere operation of established procedures of recruitment and hiring. The impact of those practices on indigenous people is examined in the context of an analysis of anti-discrimination legislation and cases from all Australian jurisdictions from the time of the passing of the Racial Discrimination Act by the Commonwealth in 1975 to the present. The thesis finds a number of reasons why the legislation fails to provide equality of opportunity for indigenous people seeking to enter the workforce. In nearly all jurisdictions it is obscurely drafted, used mainly by educated middle class white women, and provides remedies which tend to be compensatory damages rather than change to recruitment policy. White dominance of the legal process has produced legislative and judicial definitions of "race" and "Aboriginality" which focus on biology rather than cultural difference. In the commissions and tribunals complaints of racial discrimination are often rejected on the grounds of being "vexatious" or "frivolous", not reaching the required standard of proof, or not showing a causal connection between race and the conduct complained of. In all jurisdictions the cornerstone of liability is whether a particular employment term, condition or practice is reasonable. The thesis evaluates the approaches taken by appellate courts, including the High Court, and concludes that there is a trend towards an interpretation of reasonableness which favours employer arguments such as economic rationalism, the maintenance of good industrial relations, managerial prerogative to hire and fire, and the protection of majority rights. The thesis recommends that separate, clearly drafted legislation should be passed to address indigenous disadvantage and that indigenous people should be involved in all stages of the process.

The right person, in the right job, with the right skills, at the right time. A workforce-planning model that goes beyond metrics

Purpose – The paper aims to describe a workforce-planning model developed in-house in an Australian university library that is based on rigorous environmental scanning of an institution, the profession and the sector. Design/methodology/approach – The paper uses a case study that describes the stages of the planning process undertaken to develop the Library’s Workforce Plan and the documentation produced. Findings – While it has been found that the process has had successful and productive outcomes, workforce planning is an ongoing process. To remain effective, the workforce plan needs to be reviewed annually in the context of the library’s overall planning program. This is imperative if the plan is to remain current and to be regarded as a living document that will continue to guide library practice. Research limitations/implications – Although a single case study, the work has been contextualized within the wider research into workforce planning. Practical implications – The paper provides a model that can easily be deployed within a library without external or specialist consultant skills, and due to its scalability can be applied at department or wider level. Originality/value – The paper identifies the trends impacting on, and the emerging opportunities for, university libraries and provides a model for workforce planning that recognizes the context and culture of the organization as key drivers in determining workforce planning. Keywords - Australia, University libraries, Academic libraries, Change management, Manpower planning Paper type - Case study

neXus2. An investigation into the library and information services workforce in Australia: The institutional perspective. Final report prepared for the Australian Library and Information Association and National and State Libraries Australia

The neXus2 research project has sought to investigate the library and information services (LIS) workforce in Australia, from the institutional or employer perspective. The study builds on the neXus1 study, which collected data from individuals in the LIS workforce in order to present a snapshot of the profession in 2006, highlighting the demographics, educational background and career details of library and information professionals in Australia. To counterbalance this individual perspective, library institutions were invited to participate in a survey to contribute further data as employers. This final report on the neXus2 project compares the findings from the different library sectors, ie academic libraries, TAFE libraries, the National and State libraries, public libraries, special libraries and school libraries.

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.

Supervision practice for allied mental health professionals within a large mental health workforce : exploring the phenomenon

This paper reports on an exploration of the concept of 'supervision' as applied to allied health professionals within a large mental health service in one Australian State. A two-part methodology was used, with focus group interviews conducted with allied health professionals, and semi-structured telephone interviews with service managers. Fifty-eight allied health professionals participated in a series of seven focus groups. Semi-structured interviews were conducted with the Directors or Managers of mental health services in all 21 regions in the state. Allied health professionals and service managers both considered supervision to be an important mechanism for ensuring staff competence and best practice outcomes for consumers and carers. There was strong endorsement of the need for clarification and articulation of supervision policies within the organization, and the provision of appropriate resourcing to enable supervision to occur. Current practice in supervision was seen as ad hoc and of variable standard; the need for training in supervision was seen as critical. The supervision needs of newly graduated allied health professionals and those working in rural and regional areas were also seen as important. The need for a flexible and accessible model of supervision was clearly demonstrated.

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.

Using resilience to reconceptualise child protection workforce capacity

Current approaches to managing and supporting staff and addressing turnover in child protection predominantly rely on deficit-based models that focus on limitations, shortcomings, and psychopathology. This article explores an alternative approach, drawing on models of resilience, which is an emerging field linked to trauma and adversity. To date, the concept of resilience has seen limited application to staff and employment issues. In child protection, staff typically face a range of adverse and traumatic experiences that have flow-on implications, creating difficulties for staff recruitment and retention and reduced service quality. This article commences with discussion of the multifactorial influences of the troubled state of contemporary child protection systems on staffing problems. Links between these and difficulties with the predominant deficit models are then considered. The article concludes with a discussion of the relevance and utility of resilience models in developing alternative approaches to child protection staffing issues.

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.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

Applying data mining to assess crash risk on curves

The wide range of contributing factors and circumstances surrounding crashes on road curves suggest that no single intervention can prevent these crashes. This paper presents a novel methodology, based on data mining techniques, to identify contributing factors and the relationship between them. It identifies contributing factors that influence the risk of a crash. Incident records, described using free text, from a large insurance company were analysed with rough set theory. Rough set theory was used to discover dependencies among data, and reasons using the vague, uncertain and imprecise information that characterised the insurance dataset. The results show that male drivers, who are between 50 and 59 years old, driving during evening peak hours are involved with a collision, had a lowest crash risk. Drivers between 25 and 29 years old, driving from around midnight to 6 am and in a new car has the highest risk. The analysis of the most significant contributing factors on curves suggests that drivers with driving experience of 25 to 42 years, who are driving a new vehicle have the highest crash cost risk, characterised by the vehicle running off the road and hitting a tree. This research complements existing statistically based tools approach to analyse road crashes. Our data mining approach is supported with proven theory and will allow road safety practitioners to effectively understand the dependencies between contributing factors and the crash type with the view to designing tailored countermeasures.

A role mining inspired approach to representing user behaviour in ERP systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the most predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Application of text mining in analysing road crashes for road asset management

Traffic safety is a major concern world-wide. It is in both the sociological and economic interests of society that attempts should be made to identify the major and multiple contributory factors to those road crashes. This paper presents a text mining based method to better understand the contextual relationships inherent in road crashes. By examining and analyzing the crash report data in Queensland from year 2004 and year 2005, this paper identifies and reports the major and multiple contributory factors to those crashes. The outcome of this study will support road asset management in reducing road crashes.