Dynamic group communication for large-scale parallel data mining

Exascale systems are the next frontier in high-performance computing and are expected to deliver a performance of the order of 10^18 operations per second using massive multicore processors. Very large- and extreme-scale parallel systems pose critical algorithmic challenges, especially related to concurrency, locality and the need to avoid global communication patterns. This work investigates a novel protocol for dynamic group communication that can be used to remove the global communication requirement and to reduce the communication cost in parallel formulations of iterative data mining algorithms. The protocol is used to provide a communication-efficient parallel formulation of the k-means algorithm for cluster analysis. The approach is based on a collective communication operation for dynamic groups of processes and exploits non-uniform data distributions. Non-uniform data distributions can be either found in real-world distributed applications or induced by means of multidimensional binary search trees. The analysis of the proposed dynamic group communication protocol has shown that it does not introduce significant communication overhead. The parallel clustering algorithm has also been extended to accommodate an approximation error, which allows a further reduction of the communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing elements.

Nationalised large-scale mining, trade unions and community representation: perspectives from Northern Madagascar

This article critically explores the nature and purpose of relationships and inter-dependencies between stakeholders in the context of a parastatal chromite mining company in the Betsiboka Region of Northern Madagascar. An examination of the institutional arrangements at the interface between the mining company and local communities identified power hierarchies and dependencies in the context of a dominant paternalistic environment. The interactions, inter alia, limited social cohesion and intensified the fragility and weakness of community representation, which was further influenced by ethnic hierarchies between the varied community groups; namely, indigenous communities and migrants to the area from different ethnic groups. Moreover, dependencies and nepotism, which may exist at all institutional levels, can create civil society stakeholder representatives who are unrepresentative of the society they are intended to represent. Similarly, a lack of horizontal and vertical trust and reciprocity inherent in Malagasy society engenders a culture of low expectations regarding transparency and accountability, which further catalyses a cycle of nepotism and elite rent-seeking behaviour. On the other hand, leaders retain power with minimal vertical delegation or decentralisation of authority among levels of government and limit opportunities to benefit the elite, perpetuating rent-seeking behaviour within the privileged minority. Within the union movement, pluralism and the associated politicisation of individual unions restricts solidarity, which impacts on the movement’s capacity to act as a cohesive body of opinion and opposition. Nevertheless, the unions’ drive to improve their social capital has increased expectations of transparency and accountability, resulting in demands for greater engagement in decision-making processes.

Rule Type Identification using TRCM for trend analysis in Twitter

This paper considers the use of Association Rule Mining (ARM) and our proposed Transaction based Rule Change Mining (TRCM) to identify the rule types present in tweet’s hashtags over a specific consecutive period of time and their linkage to real life occurrences. Our novel algorithm was termed TRCM-RTI in reference to Rule Type Identification. We created Time Frame Windows (TFWs) to detect evolvement statuses and calculate the lifespan of hashtags in online tweets. We link RTI to real life events by monitoring and recording rule evolvement patterns in TFWs on the Twitter network.

Site-specific and geographical segmental social, environmental and ethical disclosures by the Ghanaian mining sector

Purpose: This paper explores the extent of site-specific and geographic segmental social, environmental and ethical reporting by mining companies operating in Ghana. We aim to: (i) establish a picture of corporate transparency relating to geographic segmentation of social, environmental and ethical reporting which is specific to operating sites and country of operation, and; (ii) gauge the impact of the introduction of integrated reporting on site-specific social, environmental and ethical reporting. Methodology/Approach: We conducted an interpretive content analysis of the annual/integrated reports of mining companies for the years 2009, 2010 and 2011 in order to extract site-specific social, environmental and ethical information relating to the companies’ mining operations in Ghana. Findings and Implications: We found that site-specific social, environmental and ethical reporting is extremely patchy and inconsistent between the companies’ reports studied. We also found that there was no information relating to certain sites, which were in operation, according to the Ghana Minerals Commission. This could simply be because operations were not in progress. Alternatively it could be that decisions are made concerning which site-specific information is reported according to a certain benchmark. One policy implication arising from this research is that IFRS should require geographic segmental reporting of material social, environmental and ethical information in order to bring IFRS into line with global developments in integrated reporting. Originality: Although there is a wealth of sustainability reporting research and an emergent literature on integrated reporting, there is currently no academic research exploring site-specific social, environmental and ethical reporting

Managing artisanal and small-scale mining in forest areas: perspectives from a poststructural political ecology

Artisanal and small-scale mining (ASM) is an activity intimately associated with social deprivation and environmental degradation, including deforestation. This paper examines ASM and deforestation using a broadly poststructural political ecology framework. Hegemonic discourses are shown to consistently influence policy direction, particularly in emerging approaches such as Corporate Social Responsibility and the Forest Stewardship Council. A review of alternative discourses reveals that the poststructural method is useful for critiquing the international policy arena but does not inform new approaches. Synthesis of the analysis leads to conclusions that echo a growing body of literature advocating for policies to become increasingly sensitive to local contexts, synergistic between actors at difference scales, and to be integrated across sectors.

Decentralising natural resource governance in Ghana: critical reflections on the artisanal and small-scale mining sector

This paper critically examines the impact of decentralization on contemporary and future governance arrangements in Ghana’s artisanal and small-scale mining (ASM) sector. The sector, while providing valuable employment in rural areas, is beleaguered by environmental and social issues. Proponents of decentralization argue that re-distributing decision-making authority leads to more responsive, transparent and efficient natural resource management. The analysis presented here, however, demonstrates how weak decentralization has exacerbated the complex, conflictual and clandestine nature of local resource politics surrounding ASM. If future decentralization reforms are going to reverse this trend and improve the governance of ASM in Ghana, then facilitating the participation of traditional authorities is imperative. It is argued that doing so requires addressing the reticence regarding the role of chiefs in resource governance; simply ironing out existing technical issues with decentralization reforms is unlikely to improve the social and environmental performance of ASM in the country. In light of the chronic resource management deficiencies in Ghana, epitomized in the ASM sector, fostering frank political debates on resource governance is becoming urgent.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

Discriminative pattern mining in software fault detection

We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

A hierarchical distributed approach for mining molecular fragments

Recently, two approaches have been introduced that distribute the molecular fragment mining problem. The first approach applies a master/worker topology, the second approach, a completely distributed peer-to-peer system, solves the scalability problem due to the bottleneck at the master node. However, in many real world scenarios the participating computing nodes cannot communicate directly due to administrative policies such as security restrictions. Thus, potential computing power is not accessible to accelerate the mining run. To solve this shortcoming, this work introduces a hierarchical topology of computing resources, which distributes the management over several levels and adapts to the natural structure of those multi-domain architectures. The most important aspect is the load balancing scheme, which has been designed and optimized for the hierarchical structure. The approach allows dynamic aggregation of heterogenous computing resources and is applied to wide area network scenarios.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Mining, grievance and conflict in rural Tanzania

This paper provides an extended analysis of the tensions that have surfaced between large-scale mine operators and artisanal miners in gold-rich areas of rural Tanzania. The literature on grievance is used to contextualise, these disputes, the underlying cause of which is artisanal miners' mounting frustration over not being able to secure viable concessions to work. Newly implemented legislation has, for the most part, empowered foreign large-scale mine operators, while simultaneously disempowering indigenous small-scale miners. In many cases, the former have addressed mounting security and community problems on their own. Until the country's major mine operators extend assistance to marginalised small-scale mining groups, the likelihood of violent conflict unfolding between these parties will increase.

Abatement of mercury pollution in the small-scale gold mining industry: Restructuring the policy and research agendas

This paper critiques contemporary research and policy approaches taken toward the analysis and abatement of mercury pollution in the small-scale gold mining sector. Unmonitored releases of mercury from gold amalgamation have caused considerable environmental contamination and human health complications in rural reaches of sub-Saharan Africa, Latin America and Asia. Whilst these problems have caught the attention of the scientific community over the past 15-20 years, the research that has since been undertaken has failed to identify appropriate mitigation measures, and has done little to advance understanding of why contamination persists. Moreover, the strategies used to educate operators about the impacts of acute mercury exposure, and the technologies implemented to prevent farther pollution, have been marginally effective at best. The mercury pollution problem will not be resolved until governments and donor agencies commit to carrying out research aimed at improving understanding of the dynamics of small scale gold mining communities. Acquisition of this knowledge is the key to designing and implementing appropriate support and abatement measures. (c) 2005 Elsevier B.V. All rights reserved.