Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

GTM-based data visualisation with incomplete data

We analyse how the Generative Topographic Mapping (GTM) can be modified to cope with missing values in the training data. Our approach is based on an Expectation -Maximisation (EM) method which estimates the parameters of the mixture components and at the same time deals with the missing values. We incorporate this algorithm into a hierarchical GTM. We verify the method on a toy data set (using a single GTM) and a realistic data set (using a hierarchical GTM). The results show our algorithm can help to construct informative visualisation plots, even when some of the training points are corrupted with missing values.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Data visualisation with missing data: A non-linear approach

Exploratory analysis of data in all sciences seeks to find common patterns to gain insights into the structure and distribution of the data. Typically visualisation methods like principal components analysis are used but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this technical report we discuss a complementary approach based on a non-linear probabilistic model. The generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate far more structure than a two dimensional principal components plot could, and deal at the same time with missing data. We show that using the generative topographic mapping provides us with an optimal method to explore the data while being able to replace missing values in a dataset, particularly where a large proportion of the data is missing.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

Creating and sustaining competitive advantage in collaborative systems:the what and the how

Initially this paper asks two questions: In order to create and sustain competitive advantage through collaborative systems WHAT should be managed? and HOW should it be managed? It introduces the competitive business structure and reviews some of the global trends in manufacturing and business, which leads to focus on manage processes, value propositions and extended business processes. It then goes on to develop a model of the collaborative architecture for extended enterprises and demonstrates the validity of this architecture through a case study. It concludes that, in order to create and sustain competitive advantage, collaborative systems should facilitate the management of: the collaborative architecture of the extended enterprise; the extended business processes and the value proposition for each extended enterprise through a meta level management process. It also identifies areas for further research, such as better understanding of: the exact nature and interaction of multiple strategies within an enterprise; how to manage people/teams working along extended business processes; and the nature and prerequisites of the manage processes.

Creating and managing value in collaborative networks

This is a theoretical paper that examines the interplay between individual and collective capabilities and competencies and value transactions in collaborative environments. The theory behind value creation is examined and two types of value are identified, internal value (Shareholder value) and external value (Value proposition). The literature on collaborative enterprises/network is also examined with particular emphasis on supply chains, extended/virtual enterprises and clusters as representatives of different forms and maturities of collaboration. The interplay of value transactions and competencies and capabilities are examined and discussed in detail. Finally, a model is presented which consists of value transactions and a table which compares the characteristics of different types of collaborative enterprises/networks. It is proposed that this model presents a platform for further research to develop an in-depth understanding into how value may be created and managed in collaborative enterprises/networks.

International technology transfer and collaborative new product development:evidence and a case from the machine tool industry

In recent years, it has become increasingly common for companies to improve their competitiveness and find new markets by extending their operations through international new product development collaborations involving technology transfer. Technology development, cost reduction and market penetration are seen as the foci in such collaborative operations with the aim being to improve the competitive position of both partners. In this paper, the case of technology transfer through collaborative new product development in the machine tool sector is used to provide a typical example of such partnerships. The paper outlines the links between the operational aspects of collaborations and their strategic objectives. It is based on empirical data collected from the machine tool industries in the UK and China. The evidence includes longitudinal case studies and questionnaire surveys of machine tool manufacturers in both countries. The specific case of BSA Tools Ltd and its Chinese partner the Changcheng Machine Tool Works is used to provide an in-depth example of the operational development of a successful collaboration. The paper concludes that a phased coordination of commercial, technical and strategic interactions between the two partners is essential for such collaborations to work.

The importance of collaborative frontloading in automotive supply networks

Purpose – The purpose of this paper is to investigate how research and development (R&D) collaboration takes place for complex new products in the automotive sector. The research aims to give guidelines to increase the effectiveness of such collaborations. Design/methodology/approach – The methodology used to investigate this issue was grounded theory. The empirical data were collected through a mixture of interviews and questionnaires. The resulting inducted conceptual models were subsequently validated in industrial workshops. Findings – The findings show that frontloading of the collaborative members was a major issue in managing successful R&D collaborations. Research limitations/implications – The limitation of this research is that it is only based in the German automotive industry. Practical implications – Practical implications have come out of this research. Models and guidelines are given to help make a success of collaborative projects and their potential impacts on time, cost and quality metrics. Originality/value – Frontloading is not often studied in a collaborative manner; it is normally studied within just one organisation. This study has novel value because it has involved a number of different members throughout the supplier network.

Designing and managing collaborative enterprises in the automotive industry

This paper presents the findings of a recently completed research project. It sheds light upon the appropriate governance of inter-firm relationships, in order to achieve competitive success for the whole partnership and its individual members. An exploratory study in the German automotive industry using inductive Grounded Theory was conducted, in order to form a set of propositions that were then validated. The research has resulted in the consolidation of these propositions into a novel concept termed Collaborative Enterprise Governance, which draws on an inter-disciplinary body of knowledge. The core of the concept is a competence based contingency framework that helps enterprise managers in selecting the most appropriate governance strategy (i.e. enterprise structure) for an inter-firm relationships within automotive supply networks (i.e. enterprises), depending on various exogenous and endogenous factors.

The visualisation of vitreous using surface modified poly(lactic-co-glycolic acid) microparticles

AIMS To demonstrate the potential use of in vitro poly(lactic-co-glycolic acid) (PLGA) microparticles in comparison with triamcinolone suspension to aid visualisation of vitreous during anterior and posterior vitrectomy. METHODS PLGA microparticles (diameter 10-60 microm) were fabricated using single and/or double emulsion technique(s) and used untreated or following the surface adsorption of a protein (transglutaminase). Particle size, shape, morphology and surface topography were assessed using scanning electron microscopy (SEM) and compared with a standard triamcinolone suspension. The efficacy of these microparticles to enhance visualisation of vitreous against the triamcinolone suspension was assessed using an in vitro set-up exploiting porcine vitreous. RESULTS Unmodified PLGA microparticles failed to adequately adhere to porcine vitreous and were readily washed out by irrigation. In contrast, modified transglutaminase-coated PLGA microparticles demonstrated a significant improvement in adhesiveness and were comparable to a triamcinolone suspension in their ability to enhance the visualisation of vitreous. This adhesive behaviour also demonstrated selectivity by not binding to the corneal endothelium. CONCLUSION The use of transglutaminase-modified biodegradable PLGA microparticles represents a novel method of visualising vitreous and aiding vitrectomy. This method may provide a distinct alternative for the visualisation of vitreous whilst eliminating the pharmacological effects of triamcinolone acetonide suspension.

Advanced data driven visualisation for geo-spatial data

Most current 3D landscape visualisation systems either use bespoke hardware solutions, or offer a limited amount of interaction and detail when used in realtime mode. We are developing a modular, data driven 3D visualisation system that can be readily customised to specific requirements. By utilising the latest software engineering methods and bringing a dynamic data driven approach to geo-spatial data visualisation we will deliver an unparalleled level of customisation in near-photo realistic, realtime 3D landscape visualisation. In this paper we show the system framework and describe how this employs data driven techniques. In particular we discuss how data driven approaches are applied to the spatiotemporal management aspect of the application framework, and describe the advantages these convey.

WSRF-based modeling of clinical trial information for collaborative cancer research

The CancerGrid consortium is developing open-standards cancer informatics to address the challenges posed by modern cancer clinical trials. This paper presents the service-oriented software paradigm implemented in CancerGrid to derive clinical trial information management systems for collaborative cancer research across multiple institutions. Our proposal is founded on a combination of a clinical trial (meta)model and WSRF (Web Services Resource Framework), and is currently being evaluated for use in early phase trials. Although primarily targeted at cancer research, our approach is readily applicable to other areas for which a similar information model is available.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.