An ontology-based mining approach for user search intent discovery

Discovering proper search intents is a vi- tal process to return desired results. It is constantly a hot research topic regarding information retrieval in recent years. Existing methods are mainly limited by utilizing context-based mining, query expansion, and user profiling techniques, which are still suffering from the issue of ambiguity in search queries. In this pa- per, we introduce a novel ontology-based approach in terms of a world knowledge base in order to construct personalized ontologies for identifying adequate con- cept levels for matching user search intents. An iter- ative mining algorithm is designed for evaluating po- tential intents level by level until meeting the best re- sult. The propose-to-attempt approach is evaluated in a large volume RCV1 data set, and experimental results indicate a distinct improvement on top precision after compared with baseline models.

Central Queensland Mining Supplies Pty Ltd v Columbia Steel Casting Co Ltd

In Central Queensland Mining Supplies Pty Ltd v Columbia Steel Casting Co Ltd [2011] QSC 183 Applegarth J considered complaints made by the defendant about the approach the plaintiff had taken in its endeavour to comply with its disclosure obligation under r 211 of the Uniform Civil Procedure Rules 1999 (Qld). The judgment also provides an indication of the direction the court is taking in relation to disclosure and document management in matters involving large numbers of documents.

Optimum utilisation of rolling stocks for iron ore mining industries

In this paper, a generic and flexible optimisation methodology is developed to represent, model, solve and analyse the iron ore supply chain system by integrating of iron ore shipment, stockpiles and railing within a whole system. As a result, an integrated train-stockpile-ship timetable is created and optimised for improving efficiency of overall supply chain system. The proposed methodology provides better decision making on how to significantly improve rolling stock utilisation with the best cost-effectiveness ratio. Based on extensive computational experiments and analysis, insightful and quantitative advices are suggested for iron ore mine industry practitioners. The proposed methodology contributes to the sustainability of the environment by reducing pollution due to better utilisation of transportation resources and fuel.

Using operations research methodologies to improve operating theatre scheduling

A hospital consists of a number of wards, units and departments that provide a variety of medical services and interact on a day-to-day basis. Nearly every department within a hospital schedules patients for the operating theatre (OT) and most wards receive patients from the OT following post-operative recovery. Because of the interrelationships between units, disruptions and cancellations within the OT can have a flow-on effect to the rest of the hospital. This often results in dissatisfied patients, nurses and doctors, escalating waiting lists, inefficient resource usage and undesirable waiting times. The objective of this study is to use Operational Research methodologies to enhance the performance of the operating theatre by improving elective patient planning using robust scheduling and improving the overall responsiveness to emergency patients by solving the disruption management and rescheduling problem. OT scheduling considers two types of patients: elective and emergency. Elective patients are selected from a waiting list and scheduled in advance based on resource availability and a set of objectives. This type of scheduling is referred to as ‘offline scheduling’. Disruptions to this schedule can occur for various reasons including variations in length of treatment, equipment restrictions or breakdown, unforeseen delays and the arrival of emergency patients, which may compete for resources. Emergency patients consist of acute patients requiring surgical intervention or in-patients whose conditions have deteriorated. These may or may not be urgent and are triaged accordingly. Most hospitals reserve theatres for emergency cases, but when these or other resources are unavailable, disruptions to the elective schedule result, such as delays in surgery start time, elective surgery cancellations or transfers to another institution. Scheduling of emergency patients and the handling of schedule disruptions is an ‘online’ process typically handled by OT staff. This means that decisions are made ‘on the spot’ in a ‘real-time’ environment. There are three key stages to this study: (1) Analyse the performance of the operating theatre department using simulation. Simulation is used as a decision support tool and involves changing system parameters and elective scheduling policies and observing the effect on the system’s performance measures; (2) Improve viability of elective schedules making offline schedules more robust to differences between expected treatment times and actual treatment times, using robust scheduling techniques. This will improve the access to care and the responsiveness to emergency patients; (3) Address the disruption management and rescheduling problem (which incorporates emergency arrivals) using innovative robust reactive scheduling techniques. The robust schedule will form the baseline schedule for the online robust reactive scheduling model.

A fragment of the LG3 peptide of endorepellin is present in the urine of physically active mining workers : a potential marker of physical activity

Biomarker analysis has been implemented in sports research in an attempt to monitor the effects of exertion and fatigue in athletes. This study proposed that while such biomarkers may be useful for monitoring injury risk in workers, proteomic approaches might also be utilised to identify novel exertion or injury markers. We found that urinary urea and cortisol levels were significantly elevated in mining workers following a 12 hour overnight shift. These levels failed to return to baseline over 24h in the more active maintenance crew compared to truck drivers (operators) suggesting a lack of recovery between shifts. Use of a SELDI-TOF MS approach to detect novel exertion or injury markers revealed a spectral feature which was associated with workers in both work categories who were engaged in higher levels of physical activity. This feature was identified as the LG3 peptide, a C-terminal fragment of the anti-angiogenic / anti-tumourigenic protein endorepellin. This finding suggests that urinary LG3 peptide may be a biomarker of physical activity. It is also possible that the activity mediated release of LG3 / endorepellin into the circulation may represent a biological mechanism for the known inverse association between physical activity and cancer risk / survival.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Models of South-East Asian organised crime drug operations in Queensland

In 2002, the United Nations Office on Drugs and Crime (UNODC) issued a report entitled Results of a pilot survey of forty selected organized criminal groups in sixteen countries which established five models of organised crime. This paper reviews these and other common organised crime models and drug trafficking models, and applies them to cases of South East Asian drug trafficking in the Australian state of Queensland. The study tests the following hypotheses: (1) South-East Asian drug trafficking groups in Queensland will operate within a criminal network or core group; (2) Wholesale drug distributors in Queensland will not fit consistently under any particular UN organised crime model; and (3) Street dealers will have no organisational structure. The study concluded that drug trafficking or importation closely resembles a criminal network or core group structure. Wholesale dealers did not fit consistently into any UN organised crime model. Street dealers had no organisational structure as an organisational structure is typically found in mid- to high-level drug trafficking.

Considering the importance of a systems approach to integrating ESD with campus operations

Society faces an unprecedented global education challenge to equip professionals with the knowledge and skills to address emerging 21st Century challenges, spanning climate change mitigation through to adaptation measures to deal with issues such as temperature and sea level rise, and diminishing fresh water and fossil fuel reserves. This paper discusses the potential for systemic and synergistic integration of curriculum with campus operations to accelerate curriculum renewal towards ESD, drawing on the authors' experiences within engineering education. The paper begins by a providing a brief overview of the need for timely curriculum renewal towards ESD in tertiary education. The paper then highlights some examples of academic barriers that need to be overcome for integration efforts to be successful, and opportunities for promoting the benefits of such integration. The paper concludes by discussing the rational for planning green campus initiatives within a larger system of curriculum renewal considerations, including awareness raising and developing a common understanding, identifying and mapping graduate attributes, curriculum auditing, content development and strategic renewal, and bridging and outreach.

Finding and matching communities in social networks using data mining

The rapid growth in the number of users using social networks and the information that a social network requires about their users make the traditional matching systems insufficiently adept at matching users within social networks. This paper introduces the use of clustering to form communities of users and, then, uses these communities to generate matches. Forming communities within a social network helps to reduce the number of users that the matching system needs to consider, and helps to overcome other problems from which social networks suffer, such as the absence of user activities' information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased using the community information.

Discovering novel knowledge using granule mining

This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.

Corporate, risk, mining camps and knowledge/power

Australia is currently in the midst of a major resources boom. However the benefits from the boom are unevenly distributed, with state governments collecting billions in royalties, and mining companies billions in profits. The costs are borne mostly at a local level by regional communities on the frontier of the mining boom, surrounded by thousands of men housed in work camps. The escalating reliance on non–resident workers housed in camps carries significant risks for individual workers, host communities and the provision of human services and infrastructure. These include rising rates of fatigue–related death and injuries, rising levels of alcohol–fuelled violence, illegally erected and unregulated work camps, soaring housing costs and other costs of living, and stretched basic infrastructure undermining the sustainability of these towns. But these costs have generally escaped industry, government and academic scrutiny. This chapter directs a critical gaze at the hopelessly compromised industry–funded research vital to legitimating the resource sector’s self–serving knowledge claims that it is committed to social sustainability and corporate responsibility. The chapter divides into two parts. The first argues that post–industrial mining regimes mask and privatise these harms and risks, shifting them on to workers, families and communities. The second part links the privatisation of these risks with the political economy of privatised knowledge embedded in the approvals process for major resource sector projects.

A practical approach for identifying expected solution performance and robustness in operations research applications

A practical approach for identifying solution robustness is proposed for situations where parameters are uncertain. The approach is based upon the interpretation of a probability density function (pdf) and the definition of three parameters that describe how significant changes in the performance of a solution are deemed to be. The pdf is constructed by interpreting the results of simulations. A minimum number of simulations are achieved by updating the mean, variance, skewness and kurtosis of the sample using computationally efficient recursive equations. When these criterions have converged then no further simulations are needed. A case study involving several no-intermediate storage flow shop scheduling problems demonstrates the effectiveness of the approach.

Established-outsider relations and fear of crime in mining towns

Using Elias and Scotson's (1994) account of established-outsider relations, this article examines how the organisational capacity of specific social groups is significant in determining the quality of crime-talk in isolated and rural settings. In particular, social 'oldness' and notions of what constitutes 'community' are significant in determining what activities and individuals are salient within crime-talk. Individual and gorup interviews, conducted in a West Australian mining town, revealed how crime-talk is an artefact of specific social figurations and the relative ability of groups to act as cohesive and integrated networks. We argue that anxieties regarding crime are a product of specific social figurations and the shifting power ratios of groups within such figurations.

Environmental reporting in the Australian mining industry : complying with regulation or meeting international best practice?

It is nearly 10 years since the introduction of s 299(1)(f) Corporations Act , which requires the disclosure of information regarding a company's environmental performance within its annual report. This provision has generated considerable debate in the years since its introduction, fundamentally between proponents of either a voluntary or mandatory environmental reporting framework. This study examines the adequacy of the current regulatory framework. The environmental reporting practices of 24 listed companies in the resources industries are assessed relative to a standard set by the Global Reporting Initiative (GRI) Sustainability Reporting Guidelines. These Guidelines are argued to represent "international best practice" in environmental reporting and a "scorecard" approach is used to score the quality of disclosure according to this voluntary benchmark. Larger companies in the sample tend to report environmental information over and above the level required by legislation. Some, but not all companies present a stand-alone environmental/sustainability report. However, smaller companies provide minimal information in compliance with s 299(1)(f) . The findings indicate that "international best practice" environmental reporting is unlikely to be achieved by Australian companies under the current regulatory framework. In the current regulatory environment that scrutinises s 299(1)(f) , this article provides some preliminary evidence of the quality of disclosures generated in the Australian market.