Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

Grassroots leaders: Reclaiming 'Native Leadership' as content for enhancing formal leadership preparation

It is almost a truism that persons who occupy formal bureaucratic positions in schools may not actually be leaders if they were not role incumbents in a bureaucracy. It is also clear from studies of grassroots leaders that without the qualities of skills of leadership no one would follow them because they have no formal, hierarchical role upon which others were dependent to them. One of the reasons for re-examining the nature of grassroots leaders is to attempt to recapture those tactics or strategies which might be reconceptualized and utilized within more formal settings so that role dependent leadership becomes more effectual and trustworthy than one that is totally dependent on role authority. This reasoning is especially a critical need if there is a desire to work towards more democratic and collaborative working arrangements between leaders and followers, and where more flexible and dynamic relationships promise higher levels of commitment and productivity. Hecksher (1994) speaks of such a reconceptualization as part of a shift from an emphasis on power to one centered on influence. This paper examines the nature of leadership before it was subjected to positivistic science and later behavioural studies. This move follows the advice of Heilbrunn (1996) who trenchantly observed that for leadership studies to grow as a discipline, “it will have to cast a wider net” (p.11). Willis et. Al. (2008) make a similar point when they lament that social scientist have forced favoured understanding bureaucracies rather than grassroots community organizations, yet much can be gained by being aware of the tactics and strategies used by grassroots leaders who depend on influence as opposed to power. This paper, then, aims to do this by posing a tentative model of grassroots leadership and then considering how this model might inform and be used by those responsible for developing school leaders.

Integrating collaborative filtering and search-based techniques for personalized online product recommendation

The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.

Determining the appropriate proportion of owner-provided design in design-build contracts - a content analysis approach

The Request For Proposal (RFP) with the design‐build (DB) procurement arrangement is a document in which an owner develops his requirements and conveys the project scope to DB contractors. Owners should provide an appropriate level of design in DB RFPs to adequately describe their requirements without compromising the prospects for innovation. This paper examines and compares the different levels of owner‐provided design in DB RFPs by the content analysis of 84 requests for RFPs for public DB projects advertised between 2000 and 2010 with an aggregate contract value of over $5.4 billion. A statistical analysis was also conducted in order to explore the relationship between the proportion of owner‐provided design and other project information, including project type, advertisement time, project size, contractor selection method, procurement process and contract type. The results show that the majority (64.8%) of the RFPs provide less than 10% of the owner‐provided design. The owner‐provided design proportion has a significant association with project type, project size, contractor selection method and contract type. In addition, owners are generally providing less design in recent years than hitherto. The research findings also provide owners with perspectives to determine the appropriate level of owner‐provided design in DB RFPs.

Strengthening the role of the adult education sector in training workers for the digital content industries in Australia and Singapore

In 2009, QUT’s Office of Research and the Institute for Adult Learning Singapore funded a six-month pilot project that represented the first stage of a larger international comparative study. The study is the first of its kind to investigate to what extent and how digital content workers’ learning needs are being met by adult education and training in Australia and Singapore. The pilot project involved consolidating key theoretical literature, studies, policies, programs and statistical data relevant to the digital content industries in Australia and Singapore. This had not been done before, and represented new knowledge generation. Digital content workers include professionals within and beyond the creative industries as follows: Visual effects and animation (including virtual reality and 3D products); Interactive multimedia (e.g. websites, CD-ROMs) and software development; Computer and online games; and Digital film & TV production and film & TV post-production. In the last decade, the digital content industries have been recognised as an industry sector of strong and increasing significance. The project compared Australia and Singapore on aspects of the digital content industries’ labour market, skill requirements, human capital challenges, the role of adult education in building a workforce for the digital content industries, and innovation policies. The consolidated report generated from the project formed the basis of the proposal for an ARC Linkage Project application submitted in the May 2010 round.

Teen content creators : experiences of using information to learn

As access to networked digital communities increases, a growing number of teens participate in digital communities by creating and sharing a variety of content. The affordances of social media - ease of use, ubiquitous access, and communal nature - have made creating and sharing content an appealing process for teens. Teens primarily learn the practices of encountering and using information through social interaction and participation within digital communities. This article adopts the position that information literacy is the experience of using information to learn. It reports on an investigation into teens experiences in the United States, as they use information to learn how to create content and participate within the context of social media. Teens that participate in sharing art on sites such as DeiviantArt, website creation, blogging, and/or posting edited videos via YouTube and Vimeo, were interviewed. The interviews explored teens' information experiences within particular social and digital contexts. Teens discussed the information they used, how information was gathered and accessed, and explored the process of using that information to participate in the communities.

Trust-based automated recommendation making

Recommender systems are one of the recent inventions to deal with ever growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbours, generated from a database made up of the preferences of past users. With sufficient background information of item ratings, its performance is promising enough but research shows that it performs very poorly in a cold start situation where there is not enough previous rating data. As an alternative to ratings, trust between the users could be used to choose the neighbour for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world "friend of a friend" recommendations. To extend the boundaries of the neighbour, an effective trust inference technique is required. This thesis proposes a trust interference technique called Directed Series Parallel Graph (DSPG) which performs better than other popular trust inference algorithms such as TidalTrust and MoleTrust. Another problem is that reliable explicit trust data is not always available. In real life, people trust "word of mouth" recommendations made by people with similar interests. This is often assumed in the recommender system. By conducting a survey, we can confirm that interest similarity has a positive relationship with trust and this can be used to generate a trust network for recommendation. In this research, we also propose a new method called SimTrust for developing trust networks based on user's interest similarity in the absence of explicit trust data. To identify the interest similarity, we use user's personalised tagging information. However, we are interested in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbours used in the automated recommender system. Our experimental results show that our proposed tag-similarity based method outperforms the traditional collaborative filtering approach which usually uses rating data.

People, content, location : sweet spotting urban screens for situated engagement

A growing body of research is looking at ways to bring the processes and benefits of online deliberation to the places they are about and in turn allow a larger, targeted proportion of the urban public to have a voice, be heard, and engage in questions of city planning and design. Seeking to take advantage of the civic opportunities of situated engagement through public screens and mobile devices, our research informed a public urban screen content application DIS that we deployed and evaluated in a wide range of real world public and urban environments. For example, it is currently running on the renowned urban screen at Federation Square in Melbourne. We analysed the data from these user studies within a conceptual framework that positions situated engagement across three key parameters: people, content, and location. We propose a way to identify the sweet spot within the nexus of these parameters to help deploy and run interactive systems to maximise the quality of the situated engagement for civic and related deliberation purposes.

Australian content and convergence

This article is a response to Kim Dalton's 2011 Henry Mayer Lecture. It focuses on Dalton's discussion of Australian content in the context of the government's ongoing Convergence Review.

The effect of mill orientation angle on bagasse moisture content

THIS PAPER DESCRIBES an experimental investigation to explore a concept designed to reduce the moisture content of bagasse. It takes advantage of gravity to separate juice from bagasse by feeding bagasse upwards into the nip of the mill while juice drains downwards under gravity. The investigation found that orienting the feed to a mill upwards does reduce bagasse moisture content and that the benefit is expected to be greater than two units of moisture. While an advantage was found in orienting the feed up to 50° above the horizontal, no extra benefit was found in increasing the angle higher (up to 60° was explored) and so a 50° orientation was identified as the preferred angle for this design concept.

Industry needs and course content in project management

The new competitive international business environment is characterised by constant change and uncertainty. This is particularly true in project-oriented industries such as construction where subcontracting and competitive tendering add new dimensions to an already uncertain working environment. Many management writers and practitioners argue that the changing business environment and the speed required to design, develop and market products and services will lead to increasing use of project management in the future. This means that project management skills will become a competitive weapon for those individuals and firms that properly develop them.

A common neighbour based two-way collaborative recommendation method

Traditional recommendation methods offer items, that are inanimate and one way recommendation, to users. Emerging new applications such as online dating or job recruitments require reciprocal people-to-people recommendations that are animate and two-way recommendations. In this paper, we propose a reciprocal collaborative method based on the concepts of users' similarities and common neighbors. The dataset employed for the experiment is gathered from a real life online dating network. The proposed method is compared with baseline methods that use traditional collaborative algorithms. Results show the proposed method can achieve noticeably better performance than the baseline methods.