Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Parallel random prism: a computationally efficient ensemble learner for classification

Generally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster.

The "Fair trade nation": market-oriented development in devolved European regions

Scholars have largely ignored the roles played by government and public sector institutions in the fair trade movement. This article addresses the knowledge gap through examining government involvement in fair trade networks in the context of European devolution and the localization of international development action. Proposing a relational view of fair trade networks, and considering the Fair Trade Nation as a social category for development, it highlights how power sources outside the centralized nation-state permit a political community to associate itself with fair trade. Research from Wales demonstrates that government acts in a leadership role rather than as regulator, conferring political voice and finance while enhancing its international credentials and contributing to the politics of nation-building. Our conclusion is cautious; campaigners celebrate political commitment to fair trade embodied within the category of the Fair Trade Nation, but evidence suggests that government reliance on the market as a vehicle for decentralized development action is limited by how the Fair Trade Nation is currently executed.

Reframing 'crisis' in fair trade coffee production: trajectories of agrarian change in Nicaragua

A focus on crisis provides a methodological window to understand how agrarian change shapes producer engagement in fair trade. This orientation challenges a seperation between the market and development, situating fair trade within global processes that incorporate agrarian histories of social change and conflict. Reframing crisis as a condition of agrarian life, rather than emphasizing its cyclical manifestation within the global economy, reveals how market-driven development encompasses the material conditions of peoples' existence in ambiguous and contradictory ways. Drawing on the case of coffee production in Nicaragua, experiences of crisis demonstrate that greater attention needs to be paid to the socioeconomic and political dimensions of development within regional commodity assemblages to address entrenched power relations and unequal access to land and resources. This questions moral certainties when examining the paradox of working in and against the market, and suggests that a better understanding of specific trajectories of development could improve fair trade's objective of enhancing producer livelihoods.

Pushing the boundaries of the social: private agri-food standards and the governance of Fair Trade in European public procurement

The article explores how fair trade and associated private agri-food standards are incorporated into public procurement in Europe. Procurement law is underpinned by principles of equity, non-discrimination and transparency; one consequence is that legal obstacles exist to fair trade being privileged within procurement practice. These obstacles have pragmatic dimensions, concerning whether and how procurement can be used to fulfil wider social policy objectives or to incorporate private standards; they also bring to the fore underlying issues of value. Taking an agency-based approach and incorporating the concept of governability, empirical evidence demonstrates the role played by different actors in negotiating fair trade’s passage into procurement through pre-empting and managing legal risk. This process exposes contestations that arise when contrasting values come together within sustainable procurement. This examination of fair trade in public procurement helps reveal how practices and knowledge on ethical consumption enter into a new governance arena within the global agri-food system.

Fair trade and European public procurement: legal principles and governance dynamics

Purpose The article examines principles of Fair Trade in public procurement in Europe, focusing on legal dimensions related to the European Public Procurement Directives. Design/methodology/approach The article situates public procurement of Fair Trade products in relation to the rise of non-state regulatory initiatives, highlighting how they have entered into new governance dynamics in the public sector and play a part in changing practices in sustainable procurement. A review of legal position on Fair Trade in procurement law is informed by academic research and campaigning experience from the Fair Trade Advocacy Office. Findings Key findings are that the introduction of Fair Trade products into European public procurement has been marked by legal ambiguity, having developed outside comprehensive policy or legal guidelines. Following a 2012 ruling by the Court of Justice of the European Union, it is suggested that the legal position for Fair Trade in procurement has become clearer, and that forthcoming change to the Public Procurement Directives may facilitate the uptake of fair trade products by public authorities. However potential for future expansion of the public sector ‘market’ for Fair Trade is approached with caution: purchasing Fair Trade products as a marker of sustainability, which started to be embedded within procurement practice in the 2000s, is challenged by current European public austerity measures. Research limitations/implications Suggestions for future research include the need for systematic cross-institutional and multi-country comparison of the legal and governance dimensions of procurement practice with regard to Fair Trade. Practical implications A clarification of current state-of-play with regard to legal aspects of fair trade in public procurement of utility for policy and advocacy discussion. Originality/value The article provides needed elaboration on an under researched topic area of value to academia and policy makers.

Introduction: the policy trajectory of Fair Trade

This paper provides a selective review of literature on fair trade and introduces contributions to this Policy Arena. It focuses on policy practice as a dynamic process, highlighting the changing configurations of actors, policy spaces, knowledge, practices and commodities that are shaping the policy trajectory of fair trade. It highlights how recent literature has tackled questions of mainstreaming as part of this trajectory, bringing to the fore dimensions of change associated with the market, state and civil society.

Establishing a reliable Jini infrastructure for parallel applications

Java is becoming an increasingly popular language for developing distributed and parallel scientific and engineering applications. Jini is a Java-based infrastructure developed by Sun that can allegedly provide all the services necessary to support distributed applications. It is the aim of this paper to explore and investigate the services and properties that Jini actually provides and match these against the needs of high performance distributed and parallel applications written in Java. The motivation for this work is the need to develop a distributed infrastructure to support an MPI-like interface to Java known as MPJ. In the first part of the paper we discuss the needs of MPJ, the parallel environment that we wish to support. In particular we look at aspects such as reliability and ease of use. We then move on to sketch out the Jini architecture and review the components and services that Jini provides. In the third part of the paper we critically explore a Jini infrastructure that could be used to support MPJ. Here we are particularly concerned with Jini's ability to support reliably a cocoon of MPJ processes executing in a heterogeneous envirnoment. In the final part of the paper we summarise our findings and report on future work being undertaken on Jini and MPJ.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

Economic commodity or environmental crisis? An interdisciplinary approach to analysing the bushmeat trade in central and west Africa

Bushmeat is a large but largely invisible contributor to the economies of west and central African countries. Yet the trade is currently unsustainable. Hunting is reducing wildlife populations, driving more vulnerable species to local and regional extinction, and threatening biodiversity. This paper uses a commodity chain approach to explore the bushmeat trade and to demonstrate why an interdisciplinary approach is required if the trade is to be sustainable in the future.

Beekeepers in the global 'fair trade' market: a case from Tabora Region, Tanzania

The article examines the production of Tanzanian honey and beeswax for European fair trade markets. It presents a case study of the Tabora Beekeepers Co-operative Society.

Non-uniform data distribution for communication-efficient parallel clustering

Global communicationrequirements andloadimbalanceof someparalleldataminingalgorithms arethe major obstacles to exploitthe computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication costin parallel data mining algorithms and, in particular, in the k-means algorithm for cluster analysis. In the straightforward parallel formulation of the k-means algorithm, data and computation loads are uniformly distributed over the processing nodes. This approach has excellent load balancing characteristics that may suggest it could scale up to large and extreme-scale parallel computing systems. However, at each iteration step the algorithm requires a global reduction operationwhichhinders thescalabilityoftheapproach.Thisworkstudiesadifferentparallelformulation of the algorithm where the requirement of global communication is removed, while maintaining the same deterministic nature ofthe centralised algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real-world distributed applications or can be induced by means ofmulti-dimensional binary searchtrees. The approachcanalso be extended to accommodate an approximation error which allows a further reduction ofthe 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 element