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Today's information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of 'oceans of data' by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia's largest insurance companies. [ABSTRACT FROM AUTHOR]

The LawCite Project, developed by the Australasian Legal Information Institute (AustLII) since 2008, aims to maximise the value of documents located on, or known about by, free access legal information institute (LIIs) involved in the project. The principal application of the project to date is the LawCite citator, which currently contains index records of the citation histories of almost five million cases, law journal articles, law reform documents and treaties. The citator is international, containing citation records in significant numbers from court decisions in 75 countries (primarily but not exclusively from common law countries). The citator is free access to all users, and built for and by non-profit legal information institutes (AustLII and other collaborating LIIs), which also have access to the project's other resources. This free-access context provides benefits - and imposes constraints - which make it unique. This article examines how LawCite citator and the databases from which it is generated have been built by entirely automated means without editorial intervention, using data mining techniques based on heuristic recognition of citations in source documents. Although the citator is the first and most visible product of the project, the data mining techniques used, and the data sets generated by their use, have other valuable applications. Current additional applications are explained, and possible future extended uses examined. [ABSTRACT FROM AUTHOR]

Although research has examined population growth and decline using functional specialisation, little attention has been paid to the possible combined effects of functional specialisation and socio-economic factors on population change. Using the Australian Bureau of Statistics Census Data 2001–2006 for statistical local areas, this study presents an investigation of the role of both functional specialisation and socio-economic factors in population change in non-metropolitan areas under the sustenance framework. The uniqueness of the study is twofold. Conceptually it develops a framework to compare the combined role of functional specialisation and socio-economic factors on population change; and, empirically it uses data mining (cluster analysis) techniques to investigate the extent of this combined role. The results show the significance of both functional specialisation and socio-economic factors. Policy implications of the study indicate the need to examine regional development and population change in relation to functional specialisation and socio-economic factors and their impact on viability of non-metropolitan areas. [ABSTRACT FROM AUTHOR]

The relationships between team performance indicators and match outcome have been examined in many team sports, however are limited in Australian Rules football. Using data from the 2013 and 2014 Australian Football League (AFL) regular seasons, this study assessed the ability of commonly reported discrete team performance indicators presented in their relative form (standardised against their opposition for a given match) to explain match outcome (Win/Loss). Logistic regression and decision tree (chi-squared automatic interaction detection (CHAID)) analyses both revealed relative differences between opposing teams for “kicks” and “goal conversion” as the most influential in explaining match outcome, with two models achieving 88.3% and 89.8% classification accuracies, respectively. Models incorporating a smaller performance indicator set displayed a slightly reduced ability to explain match outcome (81.0% and 81.5% for logistic regression and CHAID, respectively). However, both were fit to 2014 data with reduced error in comparison to the full models. Despite performance similarities across the two analysis approaches, the CHAID model revealed multiple winning performance indicator profiles, thereby increasing its comparative feasibility for use in the field. Coaches and analysts may find these results useful in informing strategy and game plan development in Australian Rules football, with the development of team-specific models recommended in future. [ABSTRACT FROM PUBLISHER]

Copyright of Regional Studies is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To examine whether a structured data quality report (SDQR) and feedback sessions with practice principals and managers improve the quality of routinely collected data in EHRs.Methods: The intervention was conducted in four general practices participating in the Fairfield neighborhood electronic Practice Based Research Network (ePBRN). Data were extracted from their clinical information systems and summarised as a SDQR to guide feedback to practice principals and managers at 0, 4, 8 and 12 months. Data quality (DQ) metrics included completeness, correctness, consistency and duplication of patient records. Information on data recording practices, data quality improvement, and utility of SDQRs was collected at the feedback sessions at the practices. The main outcome measure was change in the recording of clinical information and level of meeting Royal Australian College of General Practice (RACGP) targets.Results: Birth date was 100% and gender 99% complete at baseline and maintained. DQ of all variables measured improved significantly (p<0.01) over 12 months, but was not sufficient to comply with RACGP standards. Improvement was greatest with allergies. There was no significant change in duplicate records.Conclusions: SDQRs and feedback sessions support general practitioners and practice managers to focus on improving the recording of patient information. However, improved practice DQ, was not sufficient to meet RACGP targets. Randomised controlled studies are required to evaluate strategies to improve data quality and any associated improved safety and quality of care. [ABSTRACT FROM AUTHOR]

Soil calcium carbonate is a key component of the regolith, particularly in arid and semi-arid regions. It influences soil properties, is an important terrestrial carbon store, and is used in mineral exploration. Previous national overviews of soil carbonate distribution have been compiled from regional soil, regolith or geological maps with varying degrees of confidence and consistency. Here we develop a decision tree approach based on a piecewise linear regression model to map soil calcium carbonate abundance at the continental scale. The model is based on relationships established from 1311 field sites sampled across Australia at two depths (0–10 and ~ 60–80 cm) and 49 national environmental predictor datasets reflecting fundamental soil formation processes (e.g., climate, parent material, relief). The resulting map of soil calcium carbonate distribution is based on the median results of twenty model runs generated by randomly selecting 90% training and 10% validation splits of the input data. Results present an average coefficient of determination (R 2 ) of 0.5 on the validation dataset. The predictors used in the modelling are consistent with our understanding of the controls on the sources (inputs), formation, and preservation of soil calcium carbonate across Australian landscapes. The model produces a continuous, internally consistent, quantitative prediction of soil calcium carbonate abundance in surficial regolith at a resolution of 90 m with associated uncertainty estimates. This type of analysis provides a framework for better understanding environmental controls on regolith processes and has the potential to also be used to predict other soil geochemical properties. Furthermore, the methodology has utility globally where geochemical survey data are available. [ABSTRACT FROM AUTHOR]

Microseismic monitoring is a widely used technique in coal mine safe production. Microseismic events classification is one of the most important steps in microseismic monitoring. CNN has been proven to be the potential tool to identify microseismic events automatically. When the noise is serious and the signal is weak, the recognition ability of CNN is limited. Some scholars use denoising methods, such as wavelet transform, to enhance the signal-to-noise ratio (SNR) of the input data. However, the effective signal will be broken if the incorrect threshold parameters are set in this wavelet transform-based denoising method. In this paper, we intend to make the data volume of the time-frequency graph obtained by WPT as the input of CNN. But it may lead to a dimensional disaster if taking the time-frequency domain signals as input directly. To utilize the effective information extraction and reduce the data dimension, we propose a dual-channel CNN model by combining time domain information and wavelet packet decomposition coefficients (T-WPD CNN). The wavelet packet decomposition coefficients highlight the characteristics of the signal and suppress the characteristics of noise. In addition, the coefficients have the same dimension as the original signal. These advantages are useful for enhancing the performance of CNN. Two field datasets are used to test the network. One from Australia and another from Jiayang coal mine, Sichuan, China. The feature map of the convolutional layer with different inputs are shown to illustrate the influence effect of raw time domain signal and WPD coefficient on the classification performance. The final results show that the T-WPD CNN is superior to the traditional CNN method in accuracy and robustness. • The network combines time-domain information and wavelet packet decomposition coefficients of the microseismic data. • The more obvious feature map of the microseismic is extracted, which makes the classification performance better. • The wavelet packet decomposition coefficients can avoid the dimensional disaster of the input data. [ABSTRACT FROM AUTHOR]

Introduction: Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers., Methods: An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy., Results: The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10,417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data., Conclusion: The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Objective. Suicide prevention, including among youth, has been a national priority in Australia for some time. Yet despite this, rates of suicide, and related behaviour, remain high. The aim of this study was to review all suicide-prevention research that had been conducted in Australia between January 2007 and December 2011, with a specific emphasis on studies relating to young people, in order to determine whether or not we are prioritising the sort of research that can adequately inform policy development and guide 'best practice'. Methods. Data were collected from two sources. First, several electronic databases were searched in October 2012 in order to identify published journal articles relating to suicide, written by Australian authors. Second, summary data obtained from the National Health and Medical Research Council, the Australian Rotary Health Research Fund and the Australian Research Council were examined in order to identify currently funded studies that relate to suicide. Studies were then classified according to whether or not they had a focus on youth, and according to research type, type of suicide-related behaviour under investigation and method of suicide. Results. There were 224 articles published and 12 grants funded that specifically focussed on suicide-related behaviour over the period January 2007 to December 2011. Of these, 47 articles (21%) and five funded grants (42%) focussed on young people. Youth studies, in particular those reported in the published articles, tended to be epidemiological in nature and only six of the published articles (13%) and two of the funded grants related to intervention studies. Conclusions. Although the focus on youth is welcome, the lack of intervention studies is disappointing. Given that rates of suicide and related behaviour remain high, there is a clear need for a stronger body of intervention research that can inform national policy, if we are to successfully develop effective approaches to reducing suicide risk. [ABSTRACT FROM AUTHOR]

Previous research has established that student outcomes are strongly associated with the socioeconomic composition of a school, also known as school socioeconomic status. Less is known, however, about the ways in which the relationship varies for different students, schools and national education systems. Here, we conduct a secondary analysis of an international dataset to examine the strength of the relationship between school socioeconomic status and achievement in math and reading for Canada and Australia. The history, economy and culture of these two countries are similar, as are many aspects of their education systems. One important difference, however, is the degree to which their education systems are marketised. Our findings show that in both countries, school socioeconomic status is strongly associated with academic achievement for all students, regardless of their individual socioeconomic status. Nevertheless, the relationship between school socioeconomic status and academic achievement is substantially stronger in Australia than in Canada. We conclude that student outcomes are more equitable in Canada than in Australia, and suggest that this may be due to differences in the ways in which the two education systems are funded and structured. [ABSTRACT FROM PUBLISHER]

Abstract: Age-coded multi-layered geological datasets are becoming increasingly prevalent with the surge in open-access geodata, yet there are few methodologies for extracting geological information and knowledge from these data. We present a novel methodology, based on the open-source GPlates software in which age-coded digital palaeogeographic maps are used to “data-mine” spatio-temporal patterns related to the occurrence of Australian opal. Our aim is to test the concept that only a particular sequence of depositional/erosional environments may lead to conditions suitable for the formation of gem quality sedimentary opal. Time-varying geographic environment properties are extracted from a digital palaeogeographic dataset of the eastern Australian Great Artesian Basin (GAB) at 1036 opal localities. We obtain a total of 52 independent ordinal sequences sampling 19 time slices from the Early Cretaceous to the present-day. We find that 95% of the known opal deposits are tied to only 27 sequences all comprising fluvial and shallow marine depositional sequences followed by a prolonged phase of erosion. We then map the total area of the GAB that matches these 27 opal-specific sequences, resulting in an opal-prospective region of only about 10% of the total area of the basin. The key patterns underlying this association involve only a small number of key environmental transitions. We demonstrate that these key associations are generally absent at arbitrary locations in the basin. This new methodology allows for the simplification of a complex time-varying geological dataset into a single map view, enabling straightforward application for opal exploration and for future co-assessment with other datasets/geological criteria. This approach may help unravel the poorly understood opal formation process using an empirical spatio-temporal data-mining methodology and readily available datasets to aid hypothesis testing. [Copyright &y& Elsevier]

What is known and Objective: Pharmacists frequently see patients with asthma in the community who have suboptimal management. This study aimed to compare the uptake and effectiveness of pharmacist-initiated mailed and face-to-face interventions for patients whose asthma may not be well managed. Methods: Seventy-one community pharmacies in South Australia, Tasmania and Victoria (Australia) installed a software application that data-mined dispensing records, generating a list of patients who had received six or more asthma reliever inhalers in the preceding 12 months. The pharmacists were randomized, by pharmacy, to perform either a mailed or face-to-face intervention, whereby these patients received educational material and a referral to their general practitioner (GP) for an asthma management review. Matching patients from each pharmacy were also randomly assigned to a control group for 'usual care'. Results and Discussion: A total of 1483 patients were identified and grouped as follows: 510 (34·4%) mailed intervention, 480 (32·4%) face-to-face intervention and 493 (33·2%) controls. Significantly fewer face-to-face interventions were offered than mailed interventions (66·6% vs. 89·4%, respectively; χ2 = 64·2, P < 0·0001). There were significant improvements in the preventer-to-reliever ratio after the intervention period ( P < 0·0001) in each group. In a per-protocol analysis, the magnitude of improvement in the face-to-face intervention group was greater than in the mailed intervention group. The reverse was true in an intention-to-treat analysis. The improvement in the P : R ratios was mainly due to significant decreases in reliever usage. What is new and Conclusion: Community pharmacy dispensing records can effectively identify patients with suboptimal asthma management, who can then be referred to their GP for review. Time constraints in busy pharmacies may limit the uptake and effectiveness of face-to-face interventions in the 'real world' setting, making mailed interventions an attractive option. [ABSTRACT FROM AUTHOR]

Australia produces over 90% of the world's precious opal from highly weathered Cretaceous sedimentary rocks within the Great Artesian Basin. Since opal was first discovered around 1870 until the present day, opal mining has been carried out by private operators working a claim no larger than 50 × 50 m, usually in the direct vicinity of areas that have yielded precious opal in the past. Currently there is no formal exploration model for opal and its formation in the geological environment is poorly understood. Here we make the first systematic attempt to formulate a predictive model for opal exploration using a powerful data mining approach, which considers almost the entire Great Artesian Basin as a potential reservoir for precious opal. Our methodology uses all known locations where opal has been mined to date. Its formation and preservation in weathered Cretaceous host rocks is evaluated by a joint analysis of large digital data sets that include topography, regional geology, regolith and soil type, radiometric data and depositional environments through time. By combining these data sets as layers enabling spatio-temporal data mining using the GPlates PaleoGIS software, we produce the first opal prospectivity map for the Great Artesian Basin. Our approach reduces the entire area of the Great Artesian Basin to a mere 6% that is deemed to be prospective for opal exploration. It successfully identifies two known major opal fields (Mintabie and Lambina) that were not included as part of the classification dataset owing to lack of documentation regarding opal mine locations, and it significantly expands the prospective areas around known opal fields particularly in the vicinity of Coober Pedy in South Australia and in the northern and southern sectors of the Eromanga Basin in Queensland. The combined characteristics of these areas also provide a basis for future work aimed at improving our understanding of opal formation. [ABSTRACT FROM AUTHOR]

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.

As many sensor networks are currently being deployed for environmental monitoring, there is a growing need to develop systems and applications for managing, processing and retrieving massive amounts of data generated from those networks. In this research, a query answering system with pattern mining techniques is investigated specifically for marine sensor data. We consider three applications of pattern mining: similar pattern search, predictive query and query by clustering. In pattern mining for query answering, we adopt the dynamic time warping (DTW) method for similarity measurement. We also propose the use of a query relaxation approach that recommends users change parameters of a given query to get an answer. Finally, we show implementation results of pattern query answering in a marine sensor network deployed in the South East of Tasmania, Australia. Pattern query answering system benefits in accessing and discovering knowledge from sensor data for decision making purposes. [ABSTRACT FROM AUTHOR]

This paper reports on the results of an overlap study of Australian research library collections. The study used OCLC's WorldCat Collection Analysis software to mine data recording Australian holdings on the WorldCat database. The data is analysed according to the results obtained for six 'groups' which represent various coalitions of academic, national/state, and special libraries. The data is focussed on the incidence and distribution of overlap and unique items, and the analysis considers the implications for the future management of the nation's research collections. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores the application of fuzzy causal networks (FCNs) to evaluating effect of health warnings in influencing Australian smokers’ psychosocial and quitting behaviour. The sample data used in this study are selected from the International Tobacco Control Policy Evaluation Survey project. Our research findings have demonstrated that new health warnings implemented in Australia have obvious impacts on smokers’ psychosocial and quitting behaviours. FCN is a useful framework to investigate such impacts that overcome the limitation of using traditional statistical techniques, such as linear regression and logistics regression, to analyse non-linear data. [Copyright &y& Elsevier]

Purpose -- The purpose of this study is to investigate the relationship between leadership behaviors and knowledge management (KM) practices. More specifically, it aims to examine the influence of transformational and transactional leadership behaviors on KM, and the moderating effect of organizational culture on this relationship, in the context of small-to-medium sized enterprises (SMEs) operating in Australia. Design/methodology/approach -- A total of four hypotheses were proposed for testing. It also provides is a succinct review of KM basics relevant to the study, the relationship between leadership and KM, and leadership and organizational culture. Findings -- The results suggest that both transformational and transactional leadership are positively related to KM practices. They also reveal that charismatic leadership and contingent reward leadership behaviors have greater influence on all the dimensions of KM practices. Research limitations/implications -- A key limitation of this study is its cross-sectional nature. It is possible that at least certain aspects of leadership and organizational culture, and its impact on KM practices emerge with some kind of time lag. A longitudinal treatment of data might yield additional insights into the impact of leadership behaviors and organizational culture. This study was also unable to actually observe managers interacting with followers. Practical implications -- The results of the study are generally consistent with theoretical predictions based on extant research. Originality/value -- The results of this study provide compelling evidence in support of the moderating role of organizational culture on the relationship between transactional leadership and KM and will be of interest to those in the field. [ABSTRACT FROM AUTHOR]

Summary: Genetic Programming is able to systematically explore many alternative model structures of different complexity from available input and response data. We hypothesised that Genetic Programming can be used to test the structure of hydrological models and to identify dominant processes in hydrological systems. To test this, Genetic Programming was used to analyse a data set from a lysimeter experiment in southeastern Australia. The lysimeter experiment was conducted to quantify the deep percolation response under surface irrigated pasture to different soil types, watertable depths and water ponding times during surface irrigation. Using Genetic Programming, a simple model of deep percolation was recurrently evolved in multiple Genetic Programming runs. This simple and interpretable model supported the dominant process contributing to deep percolation represented in a conceptual model that was published earlier. Thus, this study shows that Genetic Programming can be used to evaluate the structure of hydrological models and to gain insight about the dominant processes in hydrological systems. [ABSTRACT FROM AUTHOR]

Data-mining is a method for practice-based research that draws on existing organizational data to inform practice issues and build social work knowledge. This article reports on how a study investigating social work practice questions about the nature and extent of single session work in hospitals was enhanced by accessing the extensive hospital patient data system. Useful descriptive statistics on the level, purpose, location, and activities of single session social work in nine hospitals in a region of Australia were generated from the patient data. The researchers also report on the importance of a consultative and collaborative process for effective data-mining outcomes. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Music Education is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the systematic archiving of qualitative data emerging as a distinct possibility in Australia, both the practices of qualitative research and how subsequent outputs are 'used' are coming under increased scrutiny and reflection. Drawing on a series of focus groups with qualitative researchers, this article critically explores the meanings ascribed to qualitative research practice and the perceived challenges posed by contemporary innovations in data management, access, and analysis through electronic archiving. The accounts presented provide much needed insight into key debates (and divergences) within the qualitative community regarding the values and meanings of qualitative practice, but also how contemporary innovations may come to challenge these core values. [ABSTRACT FROM AUTHOR]

We present a piecewise linear decision tree model for predicting percent of soil organic C (SOC) in the agricultural zones of Australia generated using a machine learning approach. The inputs for the model are a national database of soil data, national digital surfaces of climate, elevation, and terrain variables, Landsat multispectral scanner data, lithology, land use, and soil maps. The model and resulting map are evaluated, and insights into biogeological surficial processes are discussed. The decision tree splits the overall data set into more homogenous subsets, thus in this case, it identifies areas where SOC responds closely to climatic and other environmental variables. The spatial pattern of SOC corresponds well to maps of estimated primary productivity and bioclimatic zones. Topsoil organic C levels are highest in the high rainfall, temperate regions of Tasmania, Victoria, and Western Australia, along the coast of New South Wales and in the wet tropics of Queensland; and lowest in arid and semiarid inland regions. While this pattern broadly follows continental vegetation, soil moisture, and temperature patterns, it is governed by a spatially variable hierarchy of different climatic and other variables across bioregions of Australia. At the continental scale, soil moisture level, rather than temperature, seems most important in controlling SOC. [ABSTRACT FROM AUTHOR]

The article focuses on the use of Web 2.0 technology to assess the risk of depression in young people in Australia, Great Britain and New Zealand. It discusses the use of data warehousing technologies and text mining in determining and monitoring depressed bloggers who are prone to suicide risk. It provides a literature review on the affective and emotional content text mining as well as suicide research. Studies show that text mining technologies can be used in determining depressed bloggers. It points out that identifying at-risk bloggers and offering appropriate interventions can prevent tragic consequences.

The article presents a study regarding the contribution of qualitative methods and techniques in increasing the understanding of relationship marketing theory in Australia. The study states that the methods provided data which allowed a number of commonalities to emerge relating to the adoption of the relationship marketing within an industry. The study suggests that researchers consider methods that add quality to their findings when trying to develop theory. The study also mentions that triangulation and multiple case study can improve the robustness and transferability of qualitative research.

Abstract: Accurate yield forecasts are pivotal for the success of any agricultural industry that plans or sells ahead of the annual harvest. Biophysical models that integrate information about crop growing conditions can give early insight about the likely size of a crop. At a point scale, where highly detailed knowledge about environmental and management conditions are known, the performance of reputable crop modelling approaches like APSIM have been well established. However, regional growing conditions tend not to be homogenous. Heterogeneity is common in many agricultural systems, and particularly in sugarcane systems. To overcome this obstacle, hundreds of model settings (‘models’ for convenience) that represent different environmental and management conditions were created for Ayr, a major sugarcane growing region in north eastern Australia. Statistical data mining methods that used ensembles were used to select and assign weights to the best models. One technique, called a lasso approximation produced the best results. This procedure, produced a predictive correlation (r cv) of 0.71 when predicting end of season sugarcane yields some 4 months prior to the start of the harvest season, and 10 months prior to harvest completion. This continuous forecasting methodology based on statistical ensembles represents a considerable improvement upon previous research where only categorical forecast predictions had been employed. [Copyright &y& Elsevier]

This paper presents the results of a data-driven; field-modeling (DDFM) evaluation applied to a high-temperature reservoir in Australia for the purpose of determining the significance of chemistry, reservoir, well, and hydraulic-fracture characteristics on well production. The DDFM approach has identified key production drivers for a gas-well field in Australia. This information has been useful in explaining hydraulic-fracture well production and providing guidelines for future fracture stimulation success. A DDFM process is used to develop a model for 32 wells completed in a complex, 250 to 350°F gas reservoir in Australia. This type modeling technique uses data from the field, including chemical formulation, geology, reservoir, well, completion, hydraulic-fracture stimulation, and production results. The data is integrated into a common format and resolution, and then a visual and statistical evaluation is performed. Relevant correlations and useful trends are noted. Next, an effort is made to develop a predictive model that can be used to provide an overall explanation as to what parameters drive production in the well field. Or, in effect, derive a high-level understanding about the effect of the fracturing process on the reservoir. This is accomplished by the use of data modeling/optimization technologies, including artificial neural network (ANN) and genetic algorithms. The resulting ANN model can then be used to evaluate the production associated with various hydraulic-fracturing scenarios and/or characteristics. Validation of conclusions and/or resolution of difficult interpretation issues are done by detailed evaluation and modeling of key wells. Hydraulic-fracture stimulation scenario evaluations performed by the ANN model have yielded some expected as well as unexpected results. As expected, reservoir characteristics, such as pay thickness, porosity, and water saturation have a dominant effect on well production. What was unexpected is the significance of well operations and stimulation fluid chemistry on well production. The practice of killing the well after stimulation and using inappropriate perforation techniques can reduce gas production by as much as one-half, while the use of a high-temperature gel breaker in combination with a reduction in base-gel polymer load can provide a 67% increase in production. [ABSTRACT FROM AUTHOR]

Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [ABSTRACT FROM AUTHOR]

Abstract: Environmental factors which influence the distributions of malaria vectors in northern Australia (Anopheles farauti ss, A. farauti 2 and A. farauti 3) were investigated by ecological niche modelling and data mining using an extensive data set of species presence and absence records obtained by systematic field surveys. Models were generated with GARP (the genetic algorithm for rule-set prediction) using geographical coverages of 41 climatic and topographic parameters for the north of the continent. Environmental variables associated with species records were identified with the ranking procedures of the decision tree software packages CART and KnowledgeSeeker. There was consistent agreement in the variables ranked by both methods. This permitted the selection of reduced sets of environmental variables to develop GARP models for the three target species with equivalent predictive accuracy to those which used all of the environmental information. The environmental parameters which define the realised distributions of A. farauti ss and A. farauti 3 were well described by this approach but the results were less satisfactory for A. farauti 2. Atmospheric moisture was shown to be a critical variable for each species which accords with many field and laboratory observations concerning the influence of humidity on adult mosquito survival. [Copyright &y& Elsevier]

This paper emerged from an earlier survey of developments in knowledge management within the Australian Public Service (APS). The earlier study pointed to future research possibilities in an emerging relationship between knowledge management and archives and records. The resultant case-based research reported here shows that this relationship operates most strongly in the context of key drivers of knowledge management within government agencies and departments, and in linkages between the work of the National Archives of Australia (NAA) and that of other Commonwealth organisations. Knowledge management also emerges as an important strategic issue for the National Archives of Australia. Eleven issues emerging as common in the previous study were discussed at the NAA: Perceptions, Approaches, Drivers, Links to core business, Obstacles, Enablers, Structures, Frameworks, Metrics, Champions and inter-agency networks. In overall terms the NAA emerged as an organisation that was at the development stage for knowledge management and one that was taking conscious strides in this direction. [ABSTRACT FROM AUTHOR]

The national health insurance system in Australia records details on medical services and claims provided to its population. An effective method to the discovery of temporal behavioral patterns in the data set is proposed in this paper. The method consists of a two-step approach which is applied recursively to the data set. First, a clustering algorithm is used to segment the data into classes. Then, hidden Markov models are employed to find the underlying temporal behavioral patterns. These steps are applied recursively to features extracted from the data set until convergence. The main objective is to minimize the misclassification of patient profiles into various classes. This results in a hierarchical tree model consisting of a number of classes; each class groups similar patient temporal behavioral patterns together. The capabilities of the proposed method are demonstrated through the application to a subset of the Australian national health insurance data set. It is shown that the proposed method not only clusters data into various categories of interest, but it also automatically marks the periods in which similar temporal behavioral patterns occurred. [ABSTRACT FROM AUTHOR]

Abstract: The concept of knowledge and knowledge management is not new. Researchers identified the practice of knowledge management as early as 4000 years ago. However, knowledge management systems (KMS), which involve the application of IT systems and other organizational resources to manage knowledge strategically, are a relatively recent phenomenon. While the literature on knowledge management covers various issues, it lacks comprehensive studies of factors and variables of adoption and diffusion of KMS. This paper studies these factors and variables in the context of some Australian organizations. A qualitative field study is undertaken in this research, where six organizations of various sizes, all in various stages of KMS adoption and diffusion, are studied via interviews with key personnel. Content analysis is then performed to extract the factors and variables and a comprehensive model of KMS adoption and diffusion is developed. The results of the interviews identify four major variables affecting KMS diffusion as: organizational culture, top management support, benefits to individuals, and dream of KMS. The paper also highlights the research and managerial implications of the KMS diffusion model. [Copyright &y& Elsevier]

Objectives: Understanding the successful characteristics of team formation during different scenarios in Australian Football matches can assist coaches in making important tactical match-day and training decisions. The aims of this study were to explore the outcomes of entries inside 50 m of the goal, in Australian Football and to determine whether there was an association between team formation and team defensive performance after a turnover., Design: Observational., Methods: Global Positioning System (GPS) data, technical event data and video files from 22 matches in one season were obtained from an elite Australian Football club. Of 1092 forward 50 entries, 392 possession chains that resulted in a turnover were analysed. Variables representing team formation of players at the occurrence of turnover were compared between positive and negative outcomes of the subsequent possession chain. Logistic regression and decision tree modelling were also used to explore associations and variable importance., Results: None of 18 team formation characteristics differed between positive and negative outcomes of turnovers. Multivariate modelling identified that having a team formation with greater width than length made it more likely to result in a positive outcome (Decision tree classification accuracy = 69.5%, AUROC = 0.72)., Conclusions: No single characteristic of team formation affects the outcome of a turnover possession chain, however team formation that was wider than it was long may be associated with a more desirable outcome. The lack of association between most team formation characteristics and defensive outcomes, highlight the risk of over emphasising team formation in tactical planning for some phases of play., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]

Open-source intelligence (OSINT) is a technique involving the use of publicly available information for the purpose of addressing a specific intelligence issue. Open-source, user-reported data on ecstasy pills was extracted from a clear-web site known as Pill Reports (available at www.pillreports.net) and analysed to establish whether drug intelligence could be derived to assist law enforcement and policymakers in taking effective action against the illicit ecstasy market. A comprehensive database of 4358 ecstasy pills reported by users in Australia and New Zealand between September 2005 and April 2020 was established. Using this database, intelligence was gathered under two categories: general summary data and geographic movement trends. General summary data included pill characteristics (i.e., logo, colour and shape variations), regional distribution and temporal usage trends. Geographic movement data was used to ascertain whether a common sequence of flow (i.e., trafficking patterns) could be identified both (i) across state lines and (ii) east to west across Australia. Open-source investigation of online ecstasy report data proved to be most effective for obtaining general summary data which was concordant with other more costly and onerous population-wide approaches like wastewater analyses and population surveys. There was some evidence to suggest where MDMA is entering Australian borders, and an east-to-west onshore distribution pathway is suggested, taking approximately one year to run its course. This OSINT technique illustrates a novel strategic approach that can be used to monitor drug trends in real-time. • Uses a novel strategic technique whereby drug trends can be monitored in real-time. • 512 different ecstasy pills variations were identified over 15 years in Australia. • User-reported data provides insight into drug characteristics. • There is evidence to suggest that MDMA is being imported via NSW and VIC. • Average trafficking delay from the east to west coast of Australia is 363 days. [ABSTRACT FROM AUTHOR]

Over-prediction of subsidence levels of mined land may be an issue for getting mining approval and tighter conditions in some places can be imposed to better inform mining operations and planning. Under-prediction of subsidence, present safety, social and environmental consequences during or after longwall extractions have occurred. In this study we collected subsidence data from reports related to underground coal mining environmental impact statements (EIS) and end of panel reports, and compared predicted subsidence data with the observed data to compare the accuracy of prediction. From the limited data, while it was difficult to substantiate a strong relationship between the predicted and observed subsidence data and mining characteristics, we found that where mining is less than 220 m below the surface subsidence prediction generally matches well with the observed subsidence and the ratio is closer to 1 than those mined at greater depths. Subsidence prediction analysed for mines in the Gunnedah Basin are found to be generally better than those in the Sydney Basin. The results presented here are based on limited data available in the public domain and while more data is needed to prove or disprove the discussed hypotheses, we consider there to be value in extending EIS requirements. Importantly, publication of both prediction and observed subsidence measurements would provide suitable transparency for improved, future predictions, and to provide social and stakeholder assurances that would meet growing legislative requirements. • Lack of publicly available mine data creates an unintended lack of transparency. • Reporting styles are found to present ambiguity in some of the published mine data. • Compared predicted subsidence data with the mine monitored subsidence data. • Limited analysed data show that there is a value in extending EIS requirements. [ABSTRACT FROM AUTHOR]

Tilapia lake virus (TiLV) has caused mass mortalities in farmed and wild tilapia with serious economic and ecological consequences. Until recently, this virus was the sole member of the Amnoonviridae, a family within the order Articulavirales comprising segmented negative-sense RNA viruses. We sought to identify additional viruses within the Amnoonviridae through total RNA sequencing (meta-transcriptomics) and data mining of published transcriptomes. Accordingly, we sampled marine fish species from both Australia and China and discovered several segments of two new viruses within the Amnoonviridae, tentatively called Flavolineata virus and Piscibus virus, respectively. In addition, by mining vertebrate transcriptome data, we identified nine additional virus transcripts matching to multiple genomic segments of TiLV in both marine and freshwater fish. These new viruses retained sequence conservation with the distantly related Orthomyxoviridae in the RdRp subunit PB1, but formed a distinct and diverse phylogenetic group. These data suggest that the Amnoonviridae have a broad host range within fish and that greater animal sampling will identify additional divergent members of the Articulavirales. [ABSTRACT FROM AUTHOR]

• Empirical modelling is used for mapping soil thickness across Australia at 3 arc second grid cell resolution. • Soil thickness estimates are informed from the data mining of three separate site datasets. • Our model accommodates right-censored data. • Our data mining model derives plausible realities of soil thickness. • From the modelling, one can derive exceedance probabilities of soil thickness given define depth thresholds. Soil thickness is not easily measured in situ, making it also a challenging variable to reliably map. This study improves on previous digital mapping of soil thickness across Australia using an approach suited to the continent's unique pedo-geomorphic history. Leveraging three large, in situ observation datasets and a wide range of spatial environmental variables, we developed three models depicting rock outcrops, intermediate and deep soils respectively. Our modelling approach addressed right-censored data, which is a common attribute of soil thickness data, and we applied an iterative, data re-sampling framework to quantify prediction uncertainties. We integrated the three models to create soil thickness maps and associated products of soil thickness exceedance probabilities. Using data excluded from model calibrations, we achieved an overall accuracy of 99% for the binary outcome rock outcrops model, and 85% for the binary outcome deep soils model. Modelling soil thickness of shallow to deep soils resulted in a concordance coefficient of 0.77. Of all the environmental variables considered in this study, those associated with climate data (including topo-climate) were consistently the most often used and important. We associate this finding with the direct and indirect effects of climate on biota and weathering of parental materials along with other factors driving spatial heterogeneity in soil thickness across Australia. While the products generated by this research are not without error, the overall pattern of soil thickness is consistent with previous observations from historical soil surveys across Australia and the results are demonstrably more skilful than previous digital soil mapping efforts. [ABSTRACT FROM AUTHOR]

The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. The goal of the NBN is to provide Australians with broadband internet access by using a mix of technologies, ranging from fibre and hybrid fibre-coaxial to fixed wireless and satellite platforms. Although the NBN is a public project, one of the more vexing aspects of its evolution is its lack of data transparency. There is virtually no information on platform use or footprint distribution throughout the country. Not only does this drastically limit evidence-based telecommunications policy analysis, it also limits the ability to evaluate equity in the spatial distribution of broadband connectivity and infrastructure quality. The purpose of this paper is to uncover the geographic presence of the various NBN technologies using data mining techniques and census-based socio-spatial data (SEIFA). The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane – with a focus on how the mixed-technology platform adopted divides major Australian cities. • The National Broadband Network (NBN) is the largest public infrastructure project in the history of Australia. • There is limited information on the geographic distribution of the various NBN technologies throughout the country. • This paper aims to uncover the NBN footprint using data mining techniques and census-based socio-spatial data (SEIFA). • The results portray the complexity of the NBN footprint across three metropolitan regions of Sydney, Melbourne, and Brisbane. [ABSTRACT FROM AUTHOR]

Objectives: Health services have an imperative to reduce prolonged patient length of stay (LOS) in ED. Our objective is to develop and validate an accurate prediction model for patient LOS in ED greater than 4 hours using a data mining technique., Methods: Data were collected from a regional Australian public hospital for all ED presentations between 1 January 2016 and 31 December 2017. A decision tree algorithm was built to predict patients with an ED LOS >4 hours. A total of 33 attributes were analysed. The performance of the final model was internally validated. Clinically relevant patterns from the model were analysed., Results: The accuracy of the model was 85%. We identified that patients at our site who were at high risk of ED LOS >4 hours were those who were waiting in ED for a medical consultation, or those who were waiting for a urology, surgical, orthopaedic or paediatric consultation if the request for consultation occurred more than 2 hours after the patient was first seen by an ED doctor., Conclusion: This model performed very well in predicting ED LOS >4 hours for each individual patient and demonstrated a number of clinically relevant patterns. Identifying patterns that influence ED LOS is important for health managers in order to develop and implement interventions targeted at those clinical scenarios. Future work should look at the utility of displaying individual patient risk of ED LOS >4 hours using this model in real-time at the point-of-care., (© 2019 Australasian College for Emergency Medicine.)

The aim of this study was to assess if tactical and technical performance indicators (PIs) could be used in combination to model match outcomes in Australian Football (AF). A database of 101 technical PIs and 14 tactical PIs from every match in the 2009-2016 Australian Football League (AFL) seasons was merged. Two outcome measures Win-loss and Score margin were used as dependent variables. The top 45 ranked technical and tactical PIs from a feature selection process were used to model match outcome using decision tree and Generalised Linear Models (GLMs). Of the top 45 selected features, this included seven tactical PIs. The Win-loss-based Decision tree model achieved a classification accuracy of 89.0% and GLM 93.2%. A Score margin-based GLM achieved a root mean squared error (RMSE) of 6.9 points. A combined approach to the classification of match outcomes provided no improvement in model accuracy compared with previous literature. However, this study has established the relative importance of technical and tactical measures of performance in relation to successful team performance in AF.

Objectives: To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models., Design: Observational., Methods: Ninety-one team PIs from the 2001 to 2016 Australian Football League seasons were used as independent variables. The categorical Win-Loss and continuous Score Margin match outcome measures were used as dependent variables. Decision tree and Generalised Linear Models were created to describe the relationships between the values of the PIs and match outcome., Results: Decision tree models predicted Win-Loss and Score Margin with up to 88.9% and 70.3% accuracy, respectively. The Generalised Linear Models predicted Score Margin to within 6.8 points (RMSE) and Win-Loss with up to 95.1% accuracy. The PIs that are most predictive of match outcome include; Turnovers Forced score, Inside 50s per shot, Metres Gained and Time in Possession, all in their relative (to opposition) form. The decision trees illustrate how combinations of the values of these PIs are associated with match outcome, and they indicate target values for these PIs., Conclusions: This work used a wider range of PIs and more historical data than previous reports and consequently demonstrated higher prediction accuracies and additional insights about important indicators of performance. The methods used in this work can be implemented by other sport analysts to generate further insights that support the strategic decision-making processes of coaches., (Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.)

We applied the indirect approach using anonymised data from an Australian and a Singapore laboratory to derive biological variation data for a group of 10 therapeutic drugs routinely monitored. A series of inclusion and exclusion criteria were applied on the data. The within- (CVi) and between-individual (CVg) biological variation data were then derived as previously described. The corresponding index of individuality and analytical performance specifications were also calculated. The biological variation data were overall very similar between the two study sites. Moreover, the biological variation data were also comparable between males and females, as well as whether the data originated from patients who only had two episodes of measurement during the study period or from the last two results from patients who had more than two episodes of measurement during the study period. The results presented in this study contribute towards the biological variation data for therapeutic drugs, which can be used to inform discussions about the setting of harmonised analytical performance specifications for these measurands., (Copyright © 2019 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.)