The Mining world index of current literature

Subtitle varies slightly

Catalogue of the State Museum of California : being the collections made by the State Mining Bureau.

Mode of access: Internet.

Mining in Northern British Columbia

The history of the settlement of the province is tied to patterns of exploration and min development. In Northern British Columbia the Cariboo goldfields provided the impetus for settlement of the region and the beginning for mining to extend into the watern and northern regions in a series of minor gold rushes. The northern half of the province has a geological diverse mineral base that supports a wide variety of mining, and a gradual improvement of exploration and mining methods due to scientific knowledge and technology provided opportunities for lode gold and base metal mines to be developed. The success of mining is based on world ore prices and competitive markets that impact the economic viability of developing a mine. Mining faces increasing pressures in the northern half of the province due to other resource values, such as tourism or protected areas, that claim and compete for a similar land base.

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Mining trading partners’ preferences for efficient multi-issue bargaining in E‑business

Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e‑marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e‑marketplaces.

Application of text mining in analysing road crashes for road asset management

Traffic safety is a major concern world-wide. It is in both the sociological and economic interests of society that attempts should be made to identify the major and multiple contributory factors to those road crashes. This paper presents a text mining based method to better understand the contextual relationships inherent in road crashes. By examining and analyzing the crash report data in Queensland from year 2004 and year 2005, this paper identifies and reports the major and multiple contributory factors to those crashes. The outcome of this study will support road asset management in reducing road crashes.

Personalised ontology learning and mining for web information gathering

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

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

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

The vernacular terrain : Songzhang Art Museum, Beijing, China

The field was the curation of cross-cultural new media/ digital media practices within large-scale exhibition practices in China. The context was improved understandings of the intertwining of the natural and the artificial with respect to landscape and culture, and their consequent effect on our contemporary globalised society. The research highlighted new languages of media art with respect to landscape and their particular underpinning dialects. The methodology was principally practice-led. --------- The research brought together over 60 practitioners from both local and diasporic Asian, European and Australian cultures for the first time within a Chinese exhibition context. Through pursuing a strong response to both cultural displacement and re-identification the research forged and documented an enduring commonality within difference – an agenda further concentrated through sensitivities surrounding that year’s Beijing’s Olympics. In contrast to the severe threats posed to the local dialects of many of the world’s spoken and written languages the ‘Vernacular Terrain’ project evidenced that many local creative ‘dialects’ of the environment-media art continuum had indeed survived and flourished. --------- The project was co-funded by the Beijing Film Academy, QUT Precincts, IDAProjects and Platform China Art Institute. A broad range of peer-reviewed grants was won including from the Australia China Council and the Australian Embassy in China. Through invitations from external curators much of the work then traveled to other venues including the Block Gallery at QUT and the outdoor screens at Federation Square, Melbourne. The Vernacular Terrain catalogue featured a comprehensive history of the IDA project from 2000 to 2008 alongside several major essays. Due to the reputation IDA Projects had established, the team were invited to curate a major exhibition showcasing fifty new media artists: The Vernacular Terrain, at the prestigious Songzhang Art Museum, Beijing in Dec 07-Jan 2008. The exhibition was designed for an extensive, newly opened gallery owned by one of China's most important art historians Li Xian Ting. This exhibition was not only this gallery’s inaugural non-Chinese curated show but also the Gallery’s first new media exhibition. It included important works by artists such as Peter Greenway, Michael Roulier, Maleonn and Cui Xuiwen. --------- Each artist was chosen both for a focus upon their own local environmental concerns as well as their specific forms of practice - that included virtual world design, interactive design, video art, real time and manipulated multiplayer gaming platforms and web 2.0 practices. This exhibition examined the interconnectivities of cultural dialogue on both a micro and macro scale; incorporating the local and the global, through display methods and design approaches that stitched these diverse practices into a spatial map of meanings and conversations. By examining the contexts of each artist’s practice in relationship to the specificity of their own local place and prevailing global contexts the exhibition sought to uncover a global vernacular. Through pursuing this concentrated anthropological direction the research identified key themes and concerns of a contextual language that was clearly underpinned by distinctive local ‘dialects’ thereby contributing to a profound sense of cross-cultural association. Through augmentation of existing discourse the exhibition confirmed the enduring relevance and influence of both localized and globalised languages of the landscape-technology continuum.

Mining robotics

Mining is the process of extracting mineral resources from the Earth for commercial value. It is an ancient human activity which can be traced back to Palaeolithic times (43 000 years ago), where for example the mineral hematite was mined to produce the red pigment ochre. The importance of many mined minerals is reflected in the names of the major milestones in human civilizations: the stone, copper, bronze, and iron ages. Much later coal provided the energy that was critical to the industrial revolution and still underpins modern society, creating 38% of world energy generation today. Ancient mines used human and later animal labor and broke rock using stone tools, heat, and water, and later iron tools. Today’s mines are heavily mechanized with large diesel and electrically powered vehicles, and rock is broken with explosives or rock cutting machines.

The contemporary librarian : skills, knowledge and attributes required in a world of emerging technologies

Seventy-six librarians participated in a series of focus groups in support of research exploring the skills, knowledge and attributes required by the contemporary library and information professional in a world of every changing technology. The project was funded by the Australian Learning and Teaching Council. Text data mining analysis revealed three main thematic clusters (libraries, people, jobs) and one minor thematic cluster (community). Library 2.0 was broadly viewed by participants as being about change whilst librarian 2.0 was perceived by participants as not a new creation but just about good librarian practices. Participants expressed the general belief that personality traits, not just qualifications, were critical to be a successful librarian or information worker in the future.

Transnational business masculinities : media representations of gender and management in global mining corporations

This paper examines Australian media representations of the male managers of two global mining corporations, Rio Tinto and BHP Billiton. These organizations are transnational (or multinational) corporations with assets and/or operations across national boundaries (Dunning and Lundan, 2008), and indeed their respective Chief Executive Officers, Tom Albanese and Marius Kloppers are two of the most economically (and arguably politically) powerful in the world overseeing 37 000 and 39 000 employees internationally. With a 2008 profit of US$15.962 billion and assets of US$ 75.889 Billion BHP Billiton is the world's largest mining company. In terms of its profits and assets Rio Tinto ranks fourth in the world, but with operations in six countries (mainly Canada and Australia) and a 2008 profit of US$10.3 billion it is also emblematic of the transnational in that its ‘budget is larger than that of all but a few nations’ (Giddens, 2003, p. 62).

Identifying differences in wet and dry road crashes using data mining

It is commonly accepted that wet roads have higher risk of crash than dry roads; however, providing evidence to support this assumption presents some difficulty. This paper presents a data mining case study in which predictive data mining is applied to model the skid resistance and crash relationship to search for discernable differences in the probability of wet and dry road segments having crashes based on skid resistance. The models identify an increased probability of wet road segments having crashes for mid-range skid resistance values.

Using data mining to predict road crash count with a focus on skid resistance values

Road crashes cost world and Australian society a significant proportion of GDP, affecting productivity and causing significant suffering for communities and individuals. This paper presents a case study that generates data mining models that contribute to understanding of road crashes by allowing examination of the role of skid resistance (F60) and other road attributes in road crashes. Predictive data mining algorithms, primarily regression trees, were used to produce road segment crash count models from the road and traffic attributes of crash scenarios. The rules derived from the regression trees provide evidence of the significance of road attributes in contributing to crash, with a focus on the evaluation of skid resistance.