Social water assessment protocol : a step towards connecting mining, water and human rights

The human right to water has recently been recognised by both the United Nations General Assembly and the Human Rights Council. As the mining industry interacts with water on multiple levels, it is important that these interactions respect the human right to water. Currently, a disconnect exists between mine site water management practices and the recognition of water from a human rights perspective. The Minerals Council of Australia (MCA) Water Accounting Framework (WAF) has previously been used to strengthen the connection between water management and human rights. This article extends this connection through the use of a Social Water Assessment Protocol (SWAP). The SWAP is scoping tool consisting of a set of questions classified into taxonomic themes under leading topics with suggested sources of data that enable mine sites to better understand the local water context in which they operate. Three of the themes contained in the SWAP – gender, Indigenous peoples and health – are discussed to demonstrate how the protocol may be useful in assisting mining companies to consider their impacts on the human right to water.

Social licence to operate and the coal seam gas industry : lessons from social issues in established mining operations?

The recent growth of the coal seam gas industry has increased pressure on regional communities. Debate surrounding the industry is intense and a social licence to operate has yet to be granted to the industry in its entirety. This article presents an analysis of social issues surrounding the coal seam gas industry, making comparisons between two case studies: the Ranger and Jabiluka mines and the Yandicoogina mine. It presents the results of a desktop study, focussed on three topics: community identity; procedural justice and distributive justice, which provides a means for comparison and draws attention to central concerns. It is found that: power imbalances; changing community identities; potentially inequitable distributions of long term benefits and the process to distribute those benefits and negative perceptions of the industry as a whole serve to undermine the provision of a social licence to operate by communities and has the potential to impose significant negative impacts on companies within the industry.

Mining business process deviance : a quest for accuracy

This paper evaluates the suitability of sequence classification techniques for analyzing deviant business process executions based on event logs. Deviant process executions are those that deviate in a negative or positive way with respect to normative or desirable outcomes, such as non-compliant executions or executions that undershoot or exceed performance targets. We evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions both when deviances are infrequent (unbalanced) and when deviances are as frequent as normal executions (balanced). We also analyze the ability of the discovered rules to explain potential causes and contributing factors of observed deviances. The evaluation results show that feature types extracted using pattern mining techniques only slightly outperform those based on individual activity frequency. The results also suggest that more complex feature types ought to be explored to achieve higher levels of accuracy.

Robotic explosive charging in mining and construction applications

Describes the development and testing of a robotic system for charging blast holes in underground mining. The automation system supports four main tactical functions: detection of blast holes; teleoperated arm pose control; automatic arm pose control; and human-in-the-loop visual servoing. We present the system architecture, and analyse the major components, Hole detection is crucial for automating the process, and we discuss theoretical and practical aspects in detail. The sensors used are laser range finders and cameras installed in the end effector. For automatic insertion, we consider image processing techniques to support visual servoing the tool to the hole. We also discuss issues surrounding the control of heavy-duty mining manipulators, in particular, friction, stiction, and actuator saturation.

A discourse analysis on how the sustainability agenda is defined within the mining industry

The mining industry has positioned itself within the sustainability agenda, particularly since the establishment of the International Council of Mining and Minerals (ICMM). However, some critics have questioned this position, since mining requires the extraction of non-renewable finite resources and commercial mining companies have the specific responsibility to produce profit. Complicating matters is that terms that represent the sustainability such as ‘sustainability’ and ‘sustainable development’ have multiple definitions with varying degrees of sophistication. This work identifies eleven sustainability agenda definitions that are applicable to the mining industry and organises them into three tiers: first, Perpetual Sustainability, that focuses on mining continuing indefinitely with its benefits limited to immediate shareholders; second, Transferable Sustainability, that focuses on how mining can benefit society and the environment and third, Transitional Sustainability, that focuses on the intergenerational benefits to society and the environment even after mining ceases. Using these definitions, a discourse analysis was performed on sustainability reports from member companies of the ICMM and the academic journal Resources Policy. The discourse analysis showed that in both media the definition of the sustainability agenda was focussed on Transferable Sustainability, with the sustainability reports focused on how it can be applied within a business context while the academic journal took a broader view of mining’s social and environmental impacts.

Data mining the relationship between road crash and skid resistance

Road asset managers are seeking analysis of the whole road network to supplement statistical analyses of small subsets of homogeneous roadway. This study outlines the use of data mining capable of analyzing the wide range of situations found on the network, with a focus on the role of skid resistance in the cause of crashes. Results from the analyses show that on non-crash-prone roads with low crash rates, skid resistance contributes only in a minor way, whereas on high-crash roadways, skid resistance often contributes significantly in the calculation of the crash rate. The results provide evidence supporting a causal relationship between skid resistance and crashes and highlight the importance of the role of skid resistance in decision making in road asset management.

The emergent imperatives from a design-led innovation engagement within the mining industry

This research seeks a better and more detailed understanding of the processes of implementing design-led innovation in the mining equipment technology services (METS) sector, in a context where the traditional approach to manufacturing is being challenged. This longitudinal research specifically investigated the barriers to design-led innovation and opportunities that developed throughout this research, to understand how the organisation and culture of a METS company evolved, in order to progress towards design-led change. The significance of these findings is shown in the combined implementation of design imperatives leading towards design-led change at all business levels of an organisational structure.

Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Mining discriminative Itemsets in data streams

This paper presents a single pass algorithm for mining discriminative Itemsets in data streams using a novel data structure and the tilted-time window model. Discriminative Itemsets are defined as Itemsets that are frequent in one data stream and their frequency in that stream is much higher than the rest of the streams in the dataset. In order to deal with the data structure size, we propose a pruning process that results in the compact tree structure containing discriminative Itemsets. Empirical analysis shows the sound time and space complexity of the proposed method.

The civic virtue of developmentalism : on the mining industry’s social licence to develop Western Australia

This paper examines the social licence to operate (SLO) of Western Australia's (WA's) mining industry in the context of the state's ‘developmentalist’ agenda. We draw on the findings of a multi-disciplinary body of new research on the risks and challenges posed byWA's mining industry for environmental, social and economic sustainability. We synthesise the findings of this work against the backdrop of the broader debates on corporate social responsibility (CSR) and resource governance. In light of the data presented, this paper takes issue with the mining sector's SLO and its assessment of social and environmental impacts in WA for three inter-related reasons. A state government ideologically wedded to resource-led growth is seen to offer the resource sector a political licence to operate and to give insufficient attention to its potential social and environmental impacts. As a result, the resource sector can adopt a self-serving CSR agenda built on a limited win–win logic and operate with a ‘quasi social licence’ that is restricted to mere economic legitimacy. Overall, this paper problematises the political-cum-commercial construction and neoliberalisation of the SLO and raises questions about the impact of mining in WA.

BOSTER : an efficient algorithm for mining frequent unordered induced subtrees

Extracting frequent subtrees from the tree structured data has important applications in Web mining. In this paper, we introduce a novel canonical form for rooted labelled unordered trees called the balanced-optimal-search canonical form (BOCF) that can handle the isomorphism problem efficiently. Using BOCF, we define a tree structure guided scheme based enumeration approach that systematically enumerates only the valid subtrees. Finally, we present the balanced optimal search tree miner (BOSTER) algorithm based on BOCF and the proposed enumeration approach, for finding frequent induced subtrees from a database of labelled rooted unordered trees. Experiments on the real datasets compare the efficiency of BOSTER over the two state-of-the-art algorithms for mining induced unordered subtrees, HybridTreeMiner and UNI3. The results are encouraging.

BEST : an efficient algorithm for mining frequent unordered embedded subtrees

This paper presents an algorithm for mining unordered embedded subtrees using the balanced-optimal-search canonical form (BOCF). A tree structure guided scheme based enumeration approach is defined using BOCF for systematically enumerating the valid subtrees only. Based on this canonical form and enumeration technique, the balanced optimal search embedded subtree mining algorithm (BEST) is introduced for mining embedded subtrees from a database of labelled rooted unordered trees. The extensive experiments on both synthetic and real datasets demonstrate the efficiency of BEST over the two state-of-the-art algorithms for mining embedded unordered subtrees, SLEUTH and U3.