Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included.

Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included. © 2010 The authors.

Semantic representation for comparison of performance measurement indicators

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Ontology for performance measurement indicators’ comparison

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Exploring the joint compositional variability of major components and trace elements in the Tellus soil geochemistry survey (Northern Ireland)

The complexity of modern geochemical data sets is increasing in several aspects (number of available samples, number of elements measured, number of matrices analysed, geological-environmental variability covered, etc), hence it is becoming increasingly necessary to apply statistical methods to elucidate their structure. This paper presents an exploratory analysis of one such complex data set, the Tellus geochemical soil survey of Northern Ireland (NI). This exploratory analysis is based on one of the most fundamental exploratory tools, principal component analysis (PCA) and its graphical representation as a biplot, albeit in several variations: the set of elements included (only major oxides vs. all observed elements), the prior transformation applied to the data (none, a standardization or a logratio transformation) and the way the covariance matrix between components is estimated (classical estimation vs. robust estimation). Results show that a log-ratio PCA (robust or classical) of all available elements is the most powerful exploratory setting, providing the following insights: the first two processes controlling the whole geochemical variation in NI soils are peat coverage and a contrast between “mafic” and “felsic” background lithologies; peat covered areas are detected as outliers by a robust analysis, and can be then filtered out if required for further modelling; and peat coverage intensity can be quantified with the %Br in the subcomposition (Br, Rb, Ni).

Assisting digital forensic analysis via exploratory information visualisation

Background: Digital forensics is a rapidly expanding field, due to the continuing advances in computer technology and increases in data stage capabilities of devices. However, the tools supporting digital forensics investigations have not kept pace with this evolution, often leaving the investigator to analyse large volumes of textual data and rely heavily on their own intuition and experience. Aim: This research proposes that given the ability of information visualisation to provide an end user with an intuitive way to rapidly analyse large volumes of complex data, such approached could be applied to digital forensics datasets. Such methods will be investigated; supported by a review of literature regarding the use of such techniques in other fields. The hypothesis of this research body is that by utilising exploratory information visualisation techniques in the form of a tool to support digital forensic investigations, gains in investigative effectiveness can be realised. Method:To test the hypothesis, this research examines three different case studies which look at different forms of information visualisation and their implementation with a digital forensic dataset. Two of these case studies take the form of prototype tools developed by the researcher, and one case study utilises a tool created by a third party research group. A pilot study by the researcher is conducted on these cases, with the strengths and weaknesses of each being drawn into the next case study. The culmination of these case studies is a prototype tool which was developed to resemble a timeline visualisation of the user behaviour on a device. This tool was subjected to an experiment involving a class of university digital forensics students who were given a number of questions about a synthetic digital forensic dataset. Approximately half were given the prototype tool, named Insight, to use, and the others given a common open-source tool. The assessed metrics included: how long the participants took to complete all tasks, how accurate their answers to the tasks were, and how easy the participants found the tasks to complete. They were also asked for their feedback at multiple points throughout the task. Results:The results showed that there was a statistically significant increase in accuracy for one of the six tasks for the participants using the Insight prototype tool. Participants also found completing two of the six tasks significantly easier when using the prototype tool. There were no statistically significant different difference between the completion times of both participant groups. There were no statistically significant differences in the accuracy of participant answers for five of the six tasks. Conclusions: The results from this body of research show that there is evidence to suggest that there is the potential for gains in investigative effectiveness when information visualisation techniques are applied to a digital forensic dataset. Specifically, in some scenarios, the investigator can draw conclusions which are more accurate than those drawn when using primarily textual tools. There is also evidence so suggest that the investigators found these conclusions to be reached significantly more easily when using a tool with a visual format. None of the scenarios led to the investigators being at a significant disadvantage in terms of accuracy or usability when using the prototype visual tool over the textual tool. It is noted that this research did not show that the use of information visualisation techniques leads to any statistically significant difference in the time taken to complete a digital forensics investigation.

fuzzySim: Fuzzy similarity in species distributions

fuzzySim is an R package for calculating fuzzy similarity in species occurrence patterns. It includes functions for data preparation, such as converting species lists (long format) to presence-absence tables (wide format), obtaining unique abbreviations of species names, or transposing (parts of) complex data frames; and sample data sets for providing practical examples. It can convert binary presence-absence to fuzzy occurrence data, using e.g. trend surface analysis, inverse distance interpolation or prevalence-independent environmental favourability modelling, for multiple species simultaneously. It then calculates fuzzy similarity among (fuzzy) species distributions and/or among (fuzzy) regional species compositions. Currently available similarity indices are Jaccard, Sørensen, Simpson, and Baroni-Urbani & Buser.

High resolution mass spectrometry for quantitative analysis and untargeted screening of algal toxins in mussels and passive samplers

Measurement of marine algal toxins has traditionally focussed on shellfish monitoring while, over the last decade, passive sampling has been introduced as a complementary tool for exploratory studies. Since 2011, liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been adopted as the EU reference method (No.15/2011) for detection and quantitation of lipophilic toxins. Traditional LC-MS approaches have been based on low-resolution mass spectrometry (LRMS), however, advances in instrument platforms have led to a heightened interest in the use of high-resolution mass spectrometry (HRMS) for toxin detection. This work describes the use of HRMS in combination with passive sampling as a progressive approach to marine algal toxin surveys. Experiments focused on comparison of LRMS and HRMS for determination of a broad range of toxins in shellfish and passive samplers. Matrix effects are an important issue to address in LC-MS; therefore, this phenomenon was evaluated for mussels (Mytilus galloprovincialis) and passive samplers using LRMS (triple quadrupole) and HRMS (quadrupole time-of-flight and Orbitrap) instruments. Matrix-matched calibration solutions containing okadaic acid and dinophysistoxins, pectenotoxin, azaspiracids, yessotoxins, domoic acid, pinnatoxins, gymnodimine A and 13-desmethyl spirolide C were prepared. Similar matrix effects were observed on all instruments types. Most notably, there was ion enhancement for pectenotoxins, okadaic acid/dinophysistoxins on one hand, and ion suppression for yessotoxins on the other. Interestingly, the ion selected for quantitation of PTX2 also influenced the magnitude of matrix effects, with the sodium adduct typically exhibiting less susceptibility to matrix effects than the ammonium adduct. As expected, mussel as a biological matrix, quantitatively produced significantly more matrix effects than passive sampler extracts, irrespective of toxin. Sample dilution was demonstrated as an effective measure to reduce matrix effects for all compounds, and was found to be particularly useful for the non-targeted approach. Limits of detection and method accuracy were comparable between the systems tested, demonstrating the applicability of HRMS as an effective tool for screening and quantitative analysis. HRMS offers the advantage of untargeted analysis, meaning that datasets can be retrospectively analysed. HRMS (full scan) chromatograms of passive samplers yielded significantly less complex data sets than mussels, and were thus more easily screened for unknowns. Consequently, we recommend the use of HRMS in combination with passive sampling for studies investigating emerging or hitherto uncharacterised toxins.

Preparation of environmental monitoring plan pattern for surface water quality in Iran (Case study: Gharehsou River)

Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.

HW implementation of an execution manager for reconfigurable systems

Reconfigurable HW can be used to build a hardware multitasking system where tasks can be assigned to the reconfigurable HW at run-time according to the requirements of the running applications. Normally the execution in this kind of systems is controlled by an embedded processor. In these systems tasks are frequently represented as subtask graphs, where a subtask is the basic scheduling unit that can be assigned to a reconfigurable HW. In order to control the execution of these tasks, the processor must manage at run-time complex data structures, like graphs or linked list, which may generate significant execution-time penalties. In addition, HW/SW communications are frequently a system bottleneck. Hence, it is very interesting to find a way to reduce the run-time SW computations and the HW/SW communications. To this end we have developed a HW execution manager that controls the execution of subtask graphs over a set of reconfigurable units. This manager receives as input a subtask graph coupled to a subtask schedule, and guarantees its proper execution. In addition it includes support to reduce the execution-time overhead due to reconfigurations. With this HW support the execution of task graphs can be managed efficiently generating only very small run-time penalties.

On the analysis of data emerging in non-linear and complex systemscomparison of two X-Ray prony spectra

New arguments proving that successive (repeated) measurements have a memory and actually remember each other are presented. The recognition of this peculiarity can change essentially the existing paradigm associated with conventional observation in behavior of different complex systems and lead towards the application of an intermediate model (IM). This IM can provide a very accurate fit of the measured data in terms of the Prony's decomposition. This decomposition, in turn, contains a small set of the fitting parameters relatively to the number of initial data points and allows comparing the measured data in cases where the “best fit” model based on some specific physical principles is absent. As an example, we consider two X-ray diffractometers (defined in paper as A- (“cheap”) and B- (“expensive”) that are used after their proper calibration for the measuring of the same substance (corundum a-Al2O3). The amplitude-frequency response (AFR) obtained in the frame of the Prony's decomposition can be used for comparison of the spectra recorded from (A) and (B) - X-ray diffractometers (XRDs) for calibration and other practical purposes. We prove also that the Fourier decomposition can be adapted to “ideal” experiment without memory while the Prony's decomposition corresponds to real measurement and can be fitted in the frame of the IM in this case. New statistical parameters describing the properties of experimental equipment (irrespective to their internal “filling”) are found. The suggested approach is rather general and can be used for calibration and comparison of different complex dynamical systems in practical purposes.

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.

Storing Data in the Trial of Complex Technical Products

The article considers the ways of organization of databases for the storage of the results obtained during testing. A new variant of the organization of the data to ensure the ability to write to the database different sets of parameters in the form of chronological series. The required set of parameters depends on the modification of the tested technical installation.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

Tracking Invasion Histories in the Sea: Facing Complex Scenarios Using Multilocus Data

In recent years, new analytical tools have allowed researchers to extract historical information contained in molecular data, which has fundamentally transformed our understanding of processes ruling biological invasions. However, the use of these new analytical tools has been largely restricted to studies of terrestrial organisms despite the growing recognition that the sea contains ecosystems that are amongst the most heavily affected by biological invasions, and that marine invasion histories are often remarkably complex. Here, we studied the routes of invasion and colonisation histories of an invasive marine invertebrate Microcosmus squamiger (Ascidiacea) using microsatellite loci, mitochondrial DNA sequence data and 11 worldwide populations. Discriminant analysis of principal components, clustering methods and approximate Bayesian computation (ABC) methods showed that the most likely source of the introduced populations was a single admixture event that involved populations from two genetically differentiated ancestral regions - the western and eastern coasts of Australia. The ABC analyses revealed that colonisation of the introduced range of M. squamiger consisted of a series of non-independent introductions along the coastlines of Africa, North America and Europe. Furthermore, we inferred that the sequence of colonisation across continents was in line with historical taxonomic records - first the Mediterranean Sea and South Africa from an unsampled ancestral population, followed by sequential introductions in California and, more recently, the NE Atlantic Ocean. We revealed the most likely invasion history for world populations of M. squamiger, which is broadly characterized by the presence of multiple ancestral sources and non-independent introductions within the introduced range. The results presented here illustrate the complexity of marine invasion routes and identify a cause-effect relationship between human-mediated transport and the success of widespread marine non-indigenous species, which benefit from stepping-stone invasions and admixture processes involving different sources for the spread and expansion of their range.