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.

Report to the Government 2.0 Taskforce : Project 4 - Copyright Law and Intellectual Property

This report is the primary output of Project 4: Copyright and Intellectual Property, the aim of which was to produce a report considering how greater access to and use of government information could be achieved within the scope of the current copyright law. In our submission for Project 4, we undertook to address: •the policy rationales underlying copyright and how they apply in the context of materials owned, held and used by government; • the recommendations of the Copyright Law Review Committee (CLRC) in its 2005 report on Crown copyright; • the legislative and regulatory barriers to information sharing in key domains, including where legal impediments such as copyright have been relied upon (whether rightly or wrongly) to justify a refusal to provide access to government data; • copyright licensing models appropriate to government materials and examples of licensing initiatives in Australia and other relevant jurisdictions; and • issues specific to the galleries, libraries, archives and museums (“GLAM”) sector, including management of copyright in legacy materials and “orphan” works. In addressing these areas, we analysed the submissions received in response to the Government 2.0 Taskforce Issues Paper, consulted with members of the Task Force as well as several key stakeholders and considered the comments posted on the Task Force’s blog. This Project Report sets out our findings on the above issues. It puts forward recommendations for consideration by the Government 2.0 Task Force on steps that can be taken to ensure that copyright and intellectual property promote access to and use of government information.

Internships : effective work integrated learning for law students

Work experience which is integrated into an undergraduate law degree has a vital role to play in assisting law students to develop the skills and attributes they need in order to be effective legal practitioners. Work integrated learning provides a context for students to develop their skills, to see the link between theory and practice and supports students in making the transition from university to practice. The literature in Australian legal education has given little consideration to the design of legal internship subjects (as distinct from legal clinic programs). Accordingly the design of internship subjects needs to be carefully considered to ensure alignment of learning objectives, learning tasks and assessment. This paper will examine the literature relating to internships, particularly in a legal context, and will propose some principles for the design of legal internships. These principles will be considered in light of an evaluation of a newly designed undergraduate legal internship subject.

'Homophobia' in the university classroom : law and justice students' conceptualizations of queer identity

Heteronormative discourses provide the most common lens through which sexuality is understood within university curricula. This means that sexuality is discussed in terms of categories of identity, with heterosexuality accorded primacy and all ‘others’ indeed ‘othered.’ This paper reports on research carried out by the authors in a core first year university justice class, in which students of law and/or justice were required to engage with, discuss, and reflect on discourses on sexuality. It uses a poststructural framework to identify how students understand non-heterosexualities and how they personally relate to queer identities, in the sense that it asks questions about gender and sexual identity, and the discourses surrounding them. It was found that strongly negative attitudes to non-heterosexualities are quite resistant to challenge, and that some students express being confronted with queerness as a deep-seated fear of being drawn into otherness against their will. The result was that, while many students were able to unpack their attitudes towards queerness and engage in critical reflection and re-evaluation of their attitudes, students with strongly negative views towards non-heterosexualities conversely refused to engage at all, typically perceiving even the engagement itself as a threat to their core heterosexual identity. However, the authors caution against relying on the idea that students are simply “homophobic” to explain this reluctance, as this term does not necessarily account for the complexity of the discourses that inform students’ reactions in this context. This “homophobia” may simply be related to a way of performing gender and sexual identity as opposed to overt discrimination and fear.

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.

Enabling authentic collaborative education in an online environment : the QUT Virtual Law Placement

This paper explores an innovative model for work-integrated learning using a virtual paradigm – The Virtual Law Placement Unit at Queensland University of Technology (QUT) Australia. It builds upon the conceptual model previously explored at WACE 2007 and provides an account of the lessons learned from the pilot offering of the unit which was conducted and evaluated in 2008. ----- The Virtual Placement Unit offers students the opportunity to complete an authentic workplace task under the guidance of a real-life workplace supervisor, where student-student communication and student-supervisor communication is all conducted virtually (and potentially asynchronously) to create an engaging but flexible learning environment using a combination of Blackboard and SharePoint technologies. This virtual experience is pioneering in the sense that it enables law students to access an unprecedented range of law graduate destination workplaces and projects, including international and social justice placements, absent the constraints traditionally associated with arranging physical placements. ----- All aspects of this unit have been designed in conjunction with community partners with a view to balancing student learning objectives with community needs through co-operative education. This paper will also explore how the implementation of the project is serving to strengthen those partnerships with the wider community, simultaneously addressing the community engagement agenda of the University and enabling students to engage meaningfully with local, national and international community partners in the real world of work.

Assessing reflection skills in law using criterion-referenced assessment

This paper focuses on the assessment of reflective practice, an issue that has not been fully explored within legal education literature. While the issue of how reflective practice should be taught is one that requires careful consideration, it is beyond the scope of this paper to consider both the teaching and the assessment of reflective practice. Part II of this paper conceptualises reflective practice, and Part III explores the benefits of reflective practice in legal education and the use of reflective writing to assess experiential learning in a legal context. Part IV considers the diverse issues that arise in assessing reflective practice and whether there is an objective method for assessing reflection. Part V of the paper examines the assessment of reflective practice in the context of an exemplar undergraduate law subject that uses a reflective report to assess students’ experiential learning during a court visit.14 Finally, Part VI offers a rubric to facilitate criterion-referenced assessment of reflective practice and thereby provides a framework for assessing reflection skills. It is suggested that the rubric is transferable not only to other law subjects but also to subjects in other disciplines.

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.

Butterworths Questions and Answers : Criminal Law in Queensland and Western Australia

Criminal Law in Queensland and Western Australia is a new title in the Butterworths Questions and Answers (BQA) series, focusing on the criminal law in the main code states – Queensland and WA.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

Granule-based knowledge representation for intra and inter transaction association mining

Abstract With the phenomenal growth of electronic data and information, there are many demands for the development of efficient and effective systems (tools) to perform the issue of data mining tasks on multidimensional databases. Association rules describe associations between items in the same transactions (intra) or in different transactions (inter). Association mining attempts to find interesting or useful association rules in databases: this is the crucial issue for the application of data mining in the real world. Association mining can be used in many application areas, such as the discovery of associations between customers’ locations and shopping behaviours in market basket analysis. Association mining includes two phases. The first phase, called pattern mining, is the discovery of frequent patterns. The second phase, called rule generation, is the discovery of interesting and useful association rules in the discovered patterns. The first phase, however, often takes a long time to find all frequent patterns; these also include much noise. The second phase is also a time consuming activity that can generate many redundant rules. To improve the quality of association mining in databases, this thesis provides an alternative technique, granule-based association mining, for knowledge discovery in databases, where a granule refers to a predicate that describes common features of a group of transactions. The new technique first transfers transaction databases into basic decision tables, then uses multi-tier structures to integrate pattern mining and rule generation in one phase for both intra and inter transaction association rule mining. To evaluate the proposed new technique, this research defines the concept of meaningless rules by considering the co-relations between data-dimensions for intratransaction-association rule mining. It also uses precision to evaluate the effectiveness of intertransaction association rules. The experimental results show that the proposed technique is promising.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.