Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.

Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life

Background. The assembly of the tree of life has seen significant progress in recent years but algae and protists have been largely overlooked in this effort. Many groups of algae and protists have ancient roots and it is unclear how much data will be required to resolve their phylogenetic relationships for incorporation in the tree of life. The red algae, a group of primary photosynthetic eukaryotes of more than a billion years old, provide the earliest fossil evidence for eukaryotic multicellularity and sexual reproduction. Despite this evolutionary significance, their phylogenetic relationships are understudied. This study aims to infer a comprehensive red algal tree of life at the family level from a supermatrix containing data mined from GenBank. We aim to locate remaining regions of low support in the topology, evaluate their causes and estimate the amount of data required to resolve them. Results. Phylogenetic analysis of a supermatrix of 14 loci and 98 red algal families yielded the most complete red algal tree of life to date. Visualization of statistical support showed the presence of five poorly supported regions. Causes for low support were identified with statistics about the age of the region, data availability and node density, showing that poor support has different origins in different parts of the tree. Parametric simulation experiments yielded optimistic estimates of how much data will be needed to resolve the poorly supported regions (ca. 103 to ca. 104 nucleotides for the different regions). Nonparametric simulations gave a markedly more pessimistic image, some regions requiring more than 2.8 105 nucleotides or not achieving the desired level of support at all. The discrepancies between parametric and nonparametric simulations are discussed in light of our dataset and known attributes of both approaches. Conclusions. Our study takes the red algae one step closer to meaningful inclusion in the tree of life. In addition to the recovery of stable relationships, the recognition of five regions in need of further study is a significant outcome of this work. Based on our analyses of current availability and future requirements of data, we make clear recommendations for forthcoming research.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

'Mining money': science, legal technologies & working the borderlands of global finance

This paper explores the roles of science and market devices in the commodification of ‘nature’ and the configuration of flows of speculative capital. It focuses on mineral prospecting and the market for shares in ‘junior’ mining companies. In recent years these companies have expanded the reach of their exploration activities overseas, taking advantage of innovations in exploration methodologies and the liberalisation of fiscal and property regimes in ‘emerging’ mineral rich developing countries. Recent literature has explored how the reconfiguration of notions of ‘risk’ has structured the uneven distribution of rents. It is increasingly evident that neoliberal framing of environmental, political, social and economic risks has set in motion overflows that multinational mining capital had not bargained for (e.g. nationalisation, violence and political resistance). However, the role of ‘geological risk’ in animating flows of mining finance is often assumed as a ‘technical’ given. Yet geological knowledge claims, translated locally, designed to travel globally, assemble heterogeneous elements within distanciated regimes of metrology, valuation and commodity production. This paper explores how knowledge of nature is enrolled within systems of property relations, focusing on the genealogy of the knowledge practices that animate contemporary circuits of speculative mining finance. It argues that the financing of mineral prospecting mobilises pragmatic and situated forms of knowledge rather than actuarially driven calculations that promise predictability. A Canadian public enquiry struck in the wake of scandal associated with Bre-X’s prospecting activities in Indonesia is used to glean insights into the ways in which the construction of a system of public warrant to underpin financial speculation is predicated upon particular subjectivities and the outworking of everyday practices and struggles over ‘value’. Reflection on practical investments in processes of standardisation, rituals of verification and systems of accreditation reveal much about how the materiality of things shape the ways in which regional and global financial circuits are integrated, selectively transforming existing social relations and forms of knowledge production.