Complex systems science: expert consultation report

Executive SummaryA new programme of research in Complex Systems Science must be initiated by FETThe science of complex systems (CS) is essential to establish rigorous scientific principles on which to develop the future ICT systems that are critical to the well-being, safety and prosperity of Europe and its citizens. As the “ICT incubator and pathfinder for new ideas and themes for long-term research in the area of information and communication technologies” FET must initiate a significant new programme of research in complex systems science to underpin research and development in ICT. Complex Systems Science is a “blue sky” research laboratory for R&D in ICT and their applications. In July 2009, ASSYST was given a set of probing questions concerning FET funding for ICT-related complex systems research. This document is based on the CS community’s response.Complex systems research has made considerable progress and is delivering new scienceSince FET began supporting CS research, considerable progress has been made. Building on previous understanding of concepts such as emergence from interactions, far-from-equilibrium systems, border of chaos and self-organised criticality, recent CS research is now delivering rigorous theory through methods of statistical physics, network theory, and computer simulation. CS research increasingly demands high-throughput data streams and new ICT-based methods of observing and reconstructing, i.e. modelling, the dynamics from those data in areas as diverse as embryogenesis, neuroscience, transport, epidemics, linguistics, meteorology, and robotics. CS research is also beginning to address the problem of engineering robust systems of systems of systems that can adapt to changing environments, including the perplexing problem that ICT systems are too often fragile and non-adaptive.Recommendation: A Programme of Research in Complex Systems Science to Support ICTFundamental theory in Complex Systems Science is needed, but this can only be achieved through real-world applications involving large, heterogeneous, and messy data sets, including people and organisations. A long-term vision is needed. Realistic targets can be set. Fundamental research can be ensured by requiring that teams include mathematicians, computer scientists, physicists and computational social scientists.One research priority is to develop a formalism for multilevel systems of systems of systems, applicable to all areas including biology, economics, security, transportation, robotics, health, agriculture, ecology, and climate change. Another related research priority is a scientific perspective on the integration of the new science with policy and its implementation, including ethical problems related to privacy and equality.A further priority is the need for education in complex systems science. Conventional education continues to be domain-dominated, producing scientists who are for the most part still lacking fundamental knowledge in core areas of mathematics, computation, statistical physics, and social systems. Therefore:1. We recommend that FET fund a new programme of work in complex systems science as essential research for progress in the development of new kinds of ICT systems.2. We have identified the dynamics of multilevel systems as the area in complex systems science requiring a major paradigm shift, beyond which significant scientific progress cannot be made.3. We propose a call requiring: fundamental research in complex systems science; new mathematical and computational formalisms to be developed; involving a large ‘guinea pig’ organisation; research into policy and its meta-level information dynamics; and that all research staff have interdisciplinary knowledge through an education programme.Tangible outcomes, potential users of the new science, its impact and measures of successUsers include (i) the private and public sectors using ICT to manage complex systems and (ii) researchers in ICT, CSS, and all complex domains. The tangible output of a call will be new knowledge on the nature of complex systems in general, new knowledge of the particular complex system(s) studied, and new knowledge of the fundamental role played by ICT in the research and implementation to create real systems addressing real-world problems. The impact of the call will be seen through new high added-value opportunities in the public and private sectors, new high added-value ICT technologies, and new high added-value science to support innovation in ICT research and development. The measure of success will be through the delivery of these high added-value outcomes, and new science to better understand failures

Dealing with missing data for prognostic purposes

Centrifugal compressors are considered one of the most critical components in oil industry, making the minimization of their downtime and the maximization of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data's presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones

Complementing privacy and utility trade-off with self-organising maps

This research received no external funding.Peer reviewedPublisher PD

Facilitating design learning through faceted classification of in-service information

The maintenance and service records collected and maintained by engineering companies are a useful resource for the ongoing support of products. Such records are typically semi-structured and contain key information such as a description of the issue and the product affected. It is suggested that further value can be realised from the collection of these records for indicating recurrent and systemic issues which may not have been apparent previously. This paper presents a faceted classification approach to organise the information collection that might enhance retrieval and also facilitate learning from in-service experiences. The faceted classification may help to expedite responses to urgent in-service issues as well as to allow for patterns and trends in the records to be analysed, either automatically using suitable data mining algorithms or by manually browsing the classification tree. The paper describes the application of the approach to aerospace in-service records, where the potential for knowledge discovery is demonstrated

Techniques for clustering gene expression data

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Learning from accidents: Investigating the genesis of human errors in multi-attribute settings to improve the organisation of design

Remarkable advances in engineering and system controls in recent times and the consequent development of state-of-the-art technologies are clearly resulting in economic, environmental and safety benefits to the society. Latest disasters, however, put human error in the glare of the media spotlight. The February 2016 train collision in southern Bavaria, Germany, which took 11 lives and left more than 90 people injured, is one of several examples where human errors appear to have played a significant role in a major accident. In this emblematic case, the railway system had multiple safety barriers in place, such as an automatic braking system if a train crosses a stop signal, but the track controller had reportedly disabled it. When he realised the error and tried to warn the drivers, it was too late (BBC, 2016)

Survey Assessment for Decision Support Using Self-Organizing Maps Profile Characterization with an Odds and Cluster Heat Map: Application to Children’s Perception of Urban School Environments

The interpretation of opinion and satisfaction surveys based exclusively on statistical analysis often faces difficulties due to the nature of the information and the requirements of the available statistical methods. These difficulties include the concurrence of categorical information with answers based on Likert scales with only a few levels, or the distancing of the necessary heuristic approach of the decision support system (DSS). The artificial neural network used for data analysis, called Kohonen or self-organizing maps (SOM), although rarely used for survey analysis, has been applied in many fields, facilitating the graphical representation and the simple interpretation of high-dimensionality data. This clustering method, based on unsupervised learning, also allows obtaining profiles of respondents without the need to provide additional information for the creation of these clusters. In this work, we propose the identification of profiles using SOM for evaluating opinion surveys. Subsequently, non-parametric chi-square tests were first conducted to contrast whether answer was independent of each profile found, and in the case of statistical significance (p ≤ 0.05), the odds ratio was evaluated as an indicator of the effect size of such dependence. Finally, all results were displayed in an odds and cluster heat map so that they could be easily interpreted and used to make decisions regarding the survey results. The methodology was applied to the analysis of a survey based on forms administered to children (N = 459) about their perception of the urban environment close to their school, obtaining relevant results, facilitating results interpretation, and providing support to the decision-process.This research was funded by Campus de Excelencia Internacional BIOTIC Granada, University of Granada, grant number V1.2015 and the APC was funded by University of Granada

Improving Subsurface Characterisation with ‘Big Data’ Mining and Machine Learning

Funding: This research was supported by Wood Mackenzie through funding of a Postdoctoral Research Associate position at Heriot Watt University, and through access to data from two basins. Acknowledgments: This work was supported by Wood Mackenzie through funding research collab- oration with Heriot-Watt University. All the data were anonymised and supplied by Wood Mackenzie and authors are thankful for the opportunity to publish the outcomes of this research. Authors also thank Mikhail Kanevski of University of Lausanne for the peer exchange on feature selection and the opportunities opened during his course on Machine Learning hands-on applications. Authors acknowledge the use of Orange Data Mining [27] and ML Office for SOM application [30]. We thank Susan Agar, who reviewed the paper most comprehensively and helped improve it along with two anonymous reviewers.Peer reviewedPublisher PD

A modified kohonen self-organizing map (KSOM) clustering for four categorical data

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified