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1. Exploring University-Industry Collaboration Trends in Computer Science: A Study on Hardware and Architecture and Software Engineering.

Author

Hsiang Cheng, Chi-Tung Chen, Ling-fang Wei, Chu-lien Yen, and Mu-Hsuan Huang

Subjects
COMPUTER science, COMPUTER input-output equipment, SOFTWARE engineering, INFORMATION technology industry, DATA analysis
Abstract

This study aims to examine research collaboration between universities and industry in the field of computer science using bibliometric methods. To further understand the impact of individual countries on university-industry (UI) collaboration, data were retrieved from the computer science journal papers included in the Science Citation Index and published during the period 2002--2011. The density, degree centrality, betweenness centrality, and closeness centrality of UI collaborative networks were calculated in order to ascertain the core countries involved in international UI collaboration. This study investigates university-industry collaborative papers (UI papers) at the global and country levels. At the global level, the results show an increasing trend in UI papers and a stable UI collaboration ratio during the last decade. The average number of authors for UI papers is 4.13, which is higher than that for all other papers. Most UI papers result from two-institution collaboration, but we observed an increase in the number of UI papers produced by multiple universities and industrial entities. At the country level, the results also show that international UI paper collaboration has intensified. Meanwhile, the USA is the dominant collaborative partner with many countries and has produced the highest number of UI papers globally. [ABSTRACT FROM AUTHOR]

Published
2017
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2. Interoperable and scalable data analysis with microservices: applications in metabolomics

Author

Namrata Kale, Sven Bergmann, Philippe Rocca-Serra, Kenneth Haug, Kristian Peters, Gianluigi Zanetti, Ola Spjuth, Kim Kultima, Christoph Ruttkies, Etienne A. Thévenot, David Johnson, Marco Capuccini, Carles Foguet, Payam Emami Khoonsari, Rico Rueedi, Anders Larsson, Pedro de Atauri, Vitaly A. Selivanov, Pierrick Roger, Pablo Moreno, Luca Pireddu, Noureddin Sadawi, Christoph Steinbeck, Sijin He, Marta Cascante, Stephanie Herman, Susanna-Assunta Sansone, Michael van Vliet, Daniel Schober, Thomas Hankemeier, Matteo Carone, Joachim Burman, Steffen Neumann, Reza M. Salek, and Alejandra Gonzalez-Beltran

Subjects
Data Analysis, Statistics and Probability, Source code, Programari, Bioinformatics, Computer science, media_common.quotation_subject, Distributed computing, Interoperability, Microservices, kubernetes, Biochemistry, Internetworking (Telecommunication), Field (computer science), Workflow, 03 medical and health sciences, microservices, 0302 clinical medicine, Software, Interoperabilitat en xarxes d'ordinadors, Metabolomics, Computer software, Molecular Biology, e-infrastructure, media_common, 030304 developmental biology, Bioinformatics (Computational Biology), 0303 health sciences, Docker, Mass spectrometry, business.industry, Systems Biology, Computational Biology, container, Original Papers, metabolomics, Computer Science Applications, Computational Mathematics, Espectrometria de masses, Computational Theory and Mathematics, Scalability, Container (abstract data type), Bioinformatik (beräkningsbiologi), Software engineering, business, 030217 neurology & neurosurgery
Abstract

Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. Availability and implementation The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2019

3. A Systematic Study on Factors Impacting GUI Traversal-Based Test Case Generation Techniques for Android Applications.

Author

Jiang, Bo, Zhang, Yaoyue, Chan, Wing Kwong, and Zhang, Zhenyu

Subjects
GRAPHICAL user interfaces, TEST design, SOFTWARE engineering, AMED (Information retrieval system)
Abstract

Many test case generation algorithms have been proposed to test Android apps through their graphical user interfaces. However, no systematic study on the impact of the core design elements in these algorithms on effectiveness and efficiency has been reported. This paper presents the first controlled experiment to examine three key design factors, each of which is popularly used in GUI traversal-based test case generation techniques. These three major factors are definition of GUI state equivalence, state search strategy, and waiting time strategy in between input events. The empirical results on 33 Android apps with real faults revealed interesting results. First, different choices of GUI state equivalence led to significant difference on failure detection rate and extent of code coverage. Second, searching the GUI state hierarchy randomly is as effective as searching it systematically. Last but not the least, the choices on when to fire the next input event to the app under test is immaterial so long as the length of the test session is practically long enough such as 1 h. We also found two new GUI state equivalence definitions that are statistically as effective as the existing best strategy for GUI state equivalence. [ABSTRACT FROM AUTHOR]

Published
2019
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4. RALSA: Design and Implementation

Author

Plamen Vladkov Mirazchiyski

Subjects
large-scale assessments and surveys in education, Computer science, data analysis, 050109 social psychology, complex sampling design, 050105 experimental psychology, R programming, Consistency (database systems), Software, complex assessment design, User experience design, Psychology, 0501 psychology and cognitive sciences, Pharmacology (medical), Graphical user interface, Structure (mathematical logic), code reuse, business.industry, 05 social sciences, Code reuse, R Programming Language, GUI programming, BF1-990, Workflow, Software engineering, business
Abstract

International large-scale assessments (ILSAs) provide invaluable information for researchers and policy makers. Analysis of their data, however, requires methods that go beyond the usual analysis techniques assuming simple random sampling. Several software packages that serve this purpose are available. One such is the R Analyzer for Large-Scale Assessments (RALSA), a newly developed R package. The package can work with data from a large number of ILSAs. It was designed for user experience and is suitable for analysts who lack technical expertise and/or familiarity with the R programming language and statistical software. This paper presents the technical aspects of RALSA—the overall design and structure of the package, its internal organization, and the structure of the analysis and data preparation functions. The use of the data.table package for memory efficiency, speed, and embedded computations is explained through examples. The central aspect of the paper is the utilization of code reuse practices to the achieve consistency, efficiency, and safety of the computations performed by the analysis functions of the package. The comprehensive output system to produce multi-sheet MS Excel workbooks is presented and its workflow explained. The paper also explains how the graphical user interface is constructed and how it is linked to the data preparation and analysis functions available in the package.

Published
2021
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5. Data-driven requirements engineering: A guided tour

Author

Xavier Franch, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, and Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering

Subjects
Monitoring, Computer science, Generalization, Informàtica::Enginyeria del software [Àrees temàtiques de la UPC], 02 engineering and technology, Requirements elicitation, Field (computer science), Data-driven, Feedback, Software analytics, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Enginyeria de requisits, Data-driven requirements engineering, Release planning, Requirements engineering, business.industry, Natural language processing, 020207 software engineering, Data analysis, State (computer science), Software engineering, business, Decision-making
Abstract

Data-driven approaches are becoming dominant in almost every single software engineering activity, and requirements engineering is not the exception. The analysis of data coming from several sources may indeed become an extremely useful input to requirements elicitation and management. However, benefits do not come for free. Techniques such as natural language processing and machine learning are difficult to master and require high-quality data and specific competences from different fields, whilst their generalization remains as a challenge. This paper introduces the main concepts behind data-driven requirements engineering, provides an overview of the state of the art in the field and identifies the main challenges to be addressed. This work is partially supported by the GENESIS project, funded by the Spanish Ministerio de Ciencia e Innovación under contract TIN2016-79269-R.

Published
2020
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6. Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

Author

David Lechevalier, Y. Tina Lee, Sebti Foufou, Sudarsan Rachuri, Anantha Narayanan, Laboratoire Electronique, Informatique et Image ( Le2i ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), University of Maryland [College Park], Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office, CSE Department, College of Engineering, Qatar University, PO BOX 2713, Doha, Qatar, Qatar University, National Institute of Standards and Technology [Gaithersburg] ( NIST ), Ramy Harik, Louis Rivest, Alain Bernard, Benoit Eynard, Abdelaziz Bouras, TC 5, WG 5.1, Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Arts et Métiers (ENSAM), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, University of Maryland System, National Institute of Standards and Technology [Gaithersburg] (NIST), Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM), and HESAM Université (HESAM)-HESAM Université (HESAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Process (engineering), Computer science, neural network, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI]Engineering Sciences [physics], 020901 industrial engineering & automation, Computer-integrated manufacturing, 0202 electrical engineering, electronic engineering, information engineering, [ SPI ] Engineering Sciences [physics], [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Meta-model, Artificial neural network, business.industry, meta-model, Data science, Neural network, Predictive modeling, Metamodeling, Workflow, Analytics, Data analytics, Data analysis, Domain knowledge, manufacturing process, 020201 artificial intelligence & image processing, Manufacturing process, business, Software engineering, predictive modeling
Abstract

To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This paper presents the domain-specific knowledge that the approach should employ, the formal workflow of the approach, and a milling process use case to illustrate the proposed approach. We also discuss potential extensions of the approach. IFIP International Federation for Information Processing 2016. The research in this paper was supported by National Institute of Standards and Technology?s Foreign Guest Researcher Program, and Cooperative Agreement No. 70NANB14H250. Scopus

Published
2016

7. Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn’s disease with a publicly available untargeted metabolomics dataset

Author

Shuchin Aeron, Phillip M. Mach, Erika K. Hussey, Elizabeth S. Dhummakupt, Conor C. Jenkins, Shoaib Bin Masud, and Seth Elkin-Frankston

Subjects
0301 basic medicine, Data Analysis, Computer science, computer.software_genre, 01 natural sciences, Biochemistry, Machine Learning, 010104 statistics & probability, Crohn Disease, Metabolites, Medicine and Health Sciences, Preprocessor, Data processing, Multidisciplinary, Applied Mathematics, Simulation and Modeling, Software Engineering, Colitis, Identification (information), Untargeted metabolomics, Physical Sciences, Medicine, Engineering and Technology, Algorithms, Research Article, Computer and Information Sciences, Science, Feature extraction, Gastroenterology and Hepatology, Machine learning, Research and Analysis Methods, 03 medical and health sciences, Machine Learning Algorithms, Metabolomics, Artificial Intelligence, Ulcerative Colitis, 0101 mathematics, Preprocessing, business.industry, Inflammatory Bowel Disease, Biology and Life Sciences, 030104 developmental biology, Metabolism, Artificial intelligence, business, computer, Biomarkers, Mathematics, Software
Abstract

Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers’ attention for further analysis.

Published
2021

8. Principles for data analysis workflows

Author

Váleri N. Vásquez, Ciera C. Martinez, and Sara Stoudt

Subjects
FOS: Computer and information sciences, Data Analysis, 0301 basic medicine, Science and Technology Workforce, Economics, Computer science, Social Sciences, Design elements and principles, Careers in Research, Workflow, Computer Science - Computers and Society, 0302 clinical medicine, Biology (General), Data Management, Careers, Ecology, Software Development, Software Engineering, Research Assessment, Reproducibility, Professions, Computational Theory and Mathematics, Modeling and Simulation, Engineering and Technology, Raw data, Employment, Computer and Information Sciences, Science Policy, Process (engineering), QH301-705.5, Research and Analysis Methods, Phase (combat), Education, Computer Software, 03 medical and health sciences, Cellular and Molecular Neuroscience, Computers and Society (cs.CY), Genetics, Humans, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Software Tools, business.industry, Data Science, Software development, Computational Biology, Data science, 030104 developmental biology, Labor Economics, People and Places, Scientists, Population Groupings, business, Software, 030217 neurology & neurosurgery
Abstract

A systematic and reproducible “workflow”—the process that moves a scientific investigation from raw data to coherent research question to insightful contribution—should be a fundamental part of academic data-intensive research practice. In this paper, we elaborate basic principles of a reproducible data analysis workflow by defining 3 phases: the Explore, Refine, and Produce Phases. Each phase is roughly centered around the audience to whom research decisions, methodologies, and results are being immediately communicated. Importantly, each phase can also give rise to a number of research products beyond traditional academic publications. Where relevant, we draw analogies between design principles and established practice in software development. The guidance provided here is not intended to be a strict rulebook; rather, the suggestions for practices and tools to advance reproducible, sound data-intensive analysis may furnish support for both students new to research and current researchers who are new to data-intensive work.

Published
2021

9. Empirical study of the relationship between design patterns and code smells

Author

Khalid Aljasser, Mohammad Alshayeb, and Mahmoud Alfadel

Subjects
Data Analysis, Source code, Computer science, Software Validation, Social Sciences, 02 engineering and technology, computer.software_genre, 01 natural sciences, Software Design, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Data Mining, media_common, computer.programming_language, Statistical Data, Multidisciplinary, Data Collection, Statistics, Software Engineering, Smell, Research Design, Physical Sciences, Software design, Engineering and Technology, Medicine, Sensory Perception, Information Technology, Natural language processing, Research Article, Computer and Information Sciences, Java, media_common.quotation_subject, Science, Research and Analysis Methods, God object, Computer Software, Prototypes, Software system, 0101 mathematics, business.industry, Design pattern, 010102 general mathematics, Code smell, Biology and Life Sciences, 020207 software engineering, Source Code, Technology Development, Software design pattern, Programming Languages, Artificial intelligence, business, computer, Mathematics, Software, Neuroscience
Abstract

Software systems are often developed in such a way that good practices in the object-oriented paradigm are not met, causing the occurrence of specific disharmonies which are sometimes called code smells. Design patterns catalogue best practices for developing object-oriented software systems. Although code smells and design patterns are widely divergent, there might be a co-occurrence relation between them. The objective of this paper is to empirically evaluate if the presence of design patterns is related to the presence of code smells at different granularity levels. We performed an empirical study using 20 design patterns and 13 code smells in ten small-size to medium-size, open source Java-based systems. We applied statistical analysis and association rules. Results confirm that classes participating in design patterns have less smell-proneness and smell frequency than classes not participating in design patterns. We also noticed that every design pattern category act in the same way in terms of smell-proneness in the subject systems. However, we observed, based on the association rules learning and the proposed validation technique, that some patterns may be associated with certain smells in some cases. For instance, Command patterns can co-occur with God Class, Blob and External Duplication smell.

Published
2020

10. Modely procesních anti-vzorů založené na SPEM pro detekci v projektových datech

Author

Premek Brada, Petr Picha, and Lenka Simeckova

Subjects
anti-pattern, Computer science, Process (engineering), softwarový proces, software proces, data analysis, 02 engineering and technology, transformace, Eclipse Process Framework, Software development process, Software, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Project management, anti-vzor, model, analýza dat, business.industry, transformation, 05 social sciences, 020206 networking & telecommunications, Metamodeling, Anti-pattern, SPEM, 050211 marketing, business, Software engineering, Software project management
Abstract

Detekce potíží projektů a nedostatků v jejich řízení je častou potřebou v oblasti projektového řízení a zlepšování procesu. Ideálně je taková detekce prováděna na základě dat dostupných v nástrojích řízení projektů. Procesní anti-vzory popisují právě tyty potíže a nedostatky, jsou ale zachyceny typicky ve formě určené k použití lidmi v projektovém řízení což vede k nejednoznačné interpretaci. Formalizované modely by přitom napomohly datově orientované detekci anti-vzorů. V tomto příspěvku ukazujeme, jak je možné anti-vzory modelovat na základě Software & System Process Engineering Metamodel (SPEM) a následně, jak je možné takovéto modely transformovat do podoby dotazů nad projektovými daty. Tento přístup je implementován v podobě rozšiřujícího modulu nástroje Eclipse Process Framework Composer. A common need in software project management and process improvement is to detect the occurrence of project mishaps and management mistakes, if possible based on data available from project management and development tools. Process anti-patterns describe such re-occurring problems in textual form intended for human consumption, and the lack of their unambiguous, formalized models hinders their detection in project data. This paper discusses how process anti-patterns can be modeled using Software & System Process Engineering Metamodel (SPEM) and describes an approach to transform such models into queries over data of actual software projects. An implementation of the approach in the form of Eclipse Process Framework Composer plug-in is also presented.

Published
2020

11. DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems

Author

Daniele Tafani, Alessio Netti, Micha Müller, Gence Ozer, Carla Guillen, Michael Ott, and Martin Schulz

Subjects
010302 applied physics, Flexibility (engineering), FOS: Computer and information sciences, 020203 distributed computing, Computer Science - Machine Learning, Computer science, business.industry, Scale (chemistry), 02 engineering and technology, Supercomputer, computer.software_genre, 01 natural sciences, Variety (cybernetics), Machine Learning (cs.LG), Resource (project management), Computer Science - Distributed, Parallel, and Cluster Computing, Systems management, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Data analysis, Distributed, Parallel, and Cluster Computing (cs.DC), Literature survey, Software engineering, business, computer
Abstract

As we approach the exascale era, the size and complexity of HPC systems continues to increase, raising concerns about their manageability and sustainability. For this reason, more and more HPC centers are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to optimize efficiency and effectiveness of system operations. However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA. This leads to insular ad-hoc solutions, each addressing only specific aspects of the problem. In this paper we propose Wintermute, a novel generic framework to enable online ODA on large-scale HPC installations. Its design is based on the results of a literature survey of common operational requirements. We implement Wintermute on top of the holistic DCDB monitoring system, offering a large variety of configuration options to accommodate the varying requirements of ODA applications. Moreover, Wintermute is based on a set of logical abstractions to ease the configuration of models at a large scale and maximize code re-use. We highlight Wintermute's flexibility through a series of practical case studies, each targeting a different aspect of the management of HPC systems, and then demonstrate the small resource footprint of our implementation., Accepted for publication at the 29th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020)

Published
2019

12. UDAO: A Next-Generation Unified Data Analytics Optimizer

Author

Chenghao Lyu, Yanlei Diao, Arnab Sinha, Prashant Shenoy, Fei Song, Khaled Zaouk, Rich Data Analytics at Cloud Scale (CEDAR), Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Massachusetts [Amherst] (UMass Amherst), University of Massachusetts System (UMASS), and Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Inria Saclay - Ile de France

Subjects
business.industry, Computer science, Dataflow, Big data, General Engineering, Pareto principle, 02 engineering and technology, Set (abstract data type), Runtime system, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Data analysis, Key (cryptography), 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Software engineering, business, Budget constraint
Abstract

Big data analytics systems today still lack the ability to take user performance goals and budgetary constraints, collectively referred to as "objectives", and automatically configure an analytic job to achieve the objectives. This paper presents UDAO, a unified data analytics optimizer that can automatically determine the parameters of the runtime system, collectively called a job configuration, for general dataflow programs based on user objectives. UDAO embodies key techniques including in-situ modeling , which learns a model for each user objective in the same computing environment as the job is run, and multi-objective optimization , which computes a Pareto optimal set of job configurations to reveal tradeoffs between different objectives. Using benchmarks developed based on industry needs, our demonstration will allow the user to explore (1) learned models to gain insights into how various parameters affect user objectives; (2) Pareto frontiers to understand interesting tradeoffs between different objectives and how a configuration recommended by the optimizer explores these tradeoffs; (3) end-to-end benefits that UDAO can provide over default configurations or those manually tuned by engineers.

Published
2019

13. Open source software DASOS: efficient accumulation, analysis, and visualisation of full-waveform lidar

Author

Diofantos G. Hadjimitsis, Milto Miltiadou, Mike Grant, Mark Warren, Neill D. F. Campbell, and Daniel Clewley

Subjects
GIS file format, Pixel, business.industry, Computer science, Feature vector, Data analysis, Hyperspectral imaging, Software Engineering, Pattern recognition, Forestry, computer.file_format, Agriculture Forestry and Fisheries, Visualization, Random forest, Remote Sensing, Full-waveform LiDAR, Software, Lidar, Engineering and Technology, Artificial intelligence, business, computer
Abstract

Full-waveform (FW) LiDAR have been available for 20 years, but compared to discrete LiDAR, there are very few researchers exploiting these data due to the increased complexity. DASOS is an open source command-line software developed for improving the adoption of FW LiDAR in Earth Observation related applications. It uses voxelisation for interpreting the data, which is fundamentally different from the state-of-art tools interpreting FW LiDAR. There are four key features of DASOS: (1) Generation of polygonal meshes by extracting an iso-surface from the voxelised data. (2) the 2D FW LiDAR metrics exported in standard GIS format; each pixel corresponds to a column from the voxelised space and contains information about the spread of the non-open voxels, (3) efficient alignment with hyperspectral imagery using a hashed table with buckets of geolocated hyperspectral pixels. The outputs of the alignment are coloured polygonal meshes, and aligned metrics. (4) The extraction of 3D raw or composite features into vectors using 3D-windows; these feature vectors can be used in machine learning for describing objects, such as trees. Machine learning approaches (e.g. random forest) could be used for classifying trees in the 3D-voxelised space. This research was funded by EPSRC Engineering and Physical Sciences grant number EP/G037736/1 (Centre for Digital Entertainment) and the NERC Airborne Research Facility Data Analysis Node, which is based at Plymouth Marine Laboratory. The continuation of this research and the preparation of the paper is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research Promotion Foundation (project ”FOREST”: OPPORTUNITY/0916/MSCA/0005).

Published
2019

14. Investigations into the Development of a Knowledge Transfer Platform for Business Productivity

Author

David Tully, Nathan Shone, and William Hurst

Subjects
Internet of things, business.industry, Computer science, Best practice, Knowledge management, Smart homes, 020206 networking & telecommunications, Cloud computing, 02 engineering and technology, Construct (python library), Virtual reality, Modular design, Industry 4.0, Education, 0202 electrical engineering, electronic engineering, information engineering, Data analysis, 020201 artificial intelligence & image processing, Software engineering, business, Productivity, Knowledge transfer
Abstract

There is a lack of access to training tools., best practice guides and knowledge repositories to help with the digital switch to Industry 4.0. Consequently., in this paper., the ProAccel (Productivity Accelerator) platform design is outlined. The system is a modular cloud-based multimedia platform that employs advanced data analytics and gamification techniques., such as Virtual Reality (VR)., to revolutionise the way productivity information is shared to support businesses in their uptake of digital technologies in the Industry 4.0 environment. We present our findings from a 4 month case study., involving over 100 UK-based companies. The resulting research was used to construct a prototype of the ProAccel platform. As an evaluation., a simulated user evaluation of the platform using a guestimate model derived from a KLM analysis is conducted as an analysis of the platform”s functionality.

Published
2019

15. Standardizing Process-Data Exploitation by Means of a Process-Instance Metamodel

Author

Antonio Cancela, Antonia María Reina Quintero, Alejandro García-García, María Teresa Gómez-López, Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Sevilla. TIC-258: Data-centric Computing Research Hub, Ministerio de Ciencia Y Tecnología (MCYT). España, Universidad de Sevilla, Paolo Ceravolo, Maurice van Keulen, María Teresa Gómez-López, TC 2, and WG 2.6

Subjects
Model mapping, Data Model, Computer science, Process (engineering), Interface (Java), business.industry, Process Instance Metamodel, Domain Knowledge, Process Data, Business-process data exploitation, Process mining, Model Mapping, 020207 software engineering, 02 engineering and technology, Data structure, Metamodeling, Data model, Proof of concept, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Data analysis, [INFO]Computer Science [cs], Software engineering, business, Process-Instance Metamodel
Abstract

The analysis of data produced by the enterprises during pro cesses execution is key to know how these processes are working and how they can be improved. These data may be consumed to make different types of analysis, for example, data could be used as input for process dis covery, decision-making and even process querying tools. However, each type of analysis needs data in different formats because they use different techniques and tackle the problem from a different point of view. Fortu nately, if we look at the data exploitation problem from a higher level of abstraction perspective, we can realize that all the points of view share a common ground: the business process model and its instantiation are in the kernel of all of them. In this paper, we propose the use of a Busi ness Process Instance Metamodel, which serves as a common interface to make independent the applications producing business process data from those applications that consume and exploit it. A tool has been implemented as a proof of concept to facilitate the matching between data from different data sources and the metamodel. Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-R Ministerio de Ciencia y Tecnología TIN2016-75394-R

Published
2018

16. A Computational Intelligence Decision-Support Environment for Architectural and Building Design: CIDEA

Author

Ioannis Chatzikonstantinou

Subjects
Data Analysis, Optimization, Flexibility (engineering), Decision support system, Knowledge management, business.industry, Computer science, Decision Support, 0211 other engineering and technologies, Surrogate Modeling, Computational intelligence, 02 engineering and technology, Energy consumption, Building design, Architectural Design, Environmentally friendly, Workflow, Software, 021105 building & construction, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Architecture, Software engineering, business, Abstraction (linguistics)
Abstract

Environmentally friendly and comfortable buildings are a much sought after goal in today's architectural practice. In order to improve energy consumption of buildings without sacrificing indoor comfort, careful consideration of design decisions is needed. Simulation tools provide a solution to one aspect arising from this need, namely the requirement for accurate quantitative results. On the other hand, the complexity of the real-world design problems in question calls for decision support tools that integrate, in addition to simulation, optimization, analysis, and modeling. The aim of the paper is to present ongoing work on the development of such a tool. The focus of the tool is on abstraction of the technical complexity, while maintaining a sufficient level of flexibility. The tool is designed according to an integrated workflow beginning from sampling, data analysis, model creation and testing, up until the final analysis of the optimization results. We present the architecture of the platform, as well as its application in two case studies, one focusing on the design of an office tower, and one on the design of a sustainable facade. Results from qualitative usage cases indicate favorable performance in supporting decision-making.

Published
2016

17. Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics

Author

Petera, Mélanie, Le Corguille, Gildas, Landi, Marion, Monsoor, Misharl, Tremblay-Franco, Marie, Martin, Jean-Francois, Duperier, Christophe, Jacob, Daniel, Guitton, Yann, Lefebvre, Marie, Pujos-Guillot, Estelle, Giacomoni, Franck, Thévenot, Etienne, Caron, Christophe, Unité de Nutrition Humaine (UNH), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Université Pierre et Marie Curie - Paris 6 (UPMC), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (FR2424), Station biologique de Roscoff (SBR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biologie du fruit et pathologie (BFP), Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA), Platetorme Métabolome Bordeaux, Institut National de la Recherche Agronomique (INRA), Dynamics, Logics and Inference for biological Systems and Sequences (Dyliss), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Laboratoire d'Informatique de Nantes Atlantique (LINA), Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN), Plateforme Metabolome Bordeaux, Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, MetaboHUB, Centre National de la Recherche Scientifique (CNRS), Université d'Auvergne - Clermont-Ferrand I (UdA). Clermont-Ferrand, FRA., Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Toxicologie Alimentaire (UTA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1, Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), IDEALG [ANR-10-BTBR-04], IFB [ANR-11- INBS-0013], MetaboHUB [ANR-11-INBS-0010], ANR-10-BTBR-04-01/10-BTBR-0004,IDEALG,IDEALG(2010), ANR-11-INBS-0013/11-INBS-0013,ReNaBi-IFB,Institut français de bioinformatique(2011), ANR-11-INBS-0010/11-INBS-0010,METABOHUB,Développement d’une infrastructure française distribuée pour la métabolomique dédiée à l’innovation(2011), MetaToul AXIOM (E20), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Proteomic Facility [Bordeaux] (Functional Genomic Center), Université de Bordeaux (UB), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), ANR-10-BTBR-0004,IDEALG,Biotechnologies pour la valorisation des macroalgues(2010), ANR-11-INBS-0013,IFB (ex Renabi-IFB),Institut français de bioinformatique(2011), ANR-11-INBS-0010,METABOHUB,Développement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation(2011), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science (ABIMS), Fédération de recherche de Roscoff (FR2424), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Station biologique de Roscoff (SBR), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1 (UB), Plateforme Bordeaux Metabolome, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Bordeaux, MetaboHUB-MetaboHUB, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Outils d'Analyse des Données (LOAD), Plateforme Exploration du Métabolisme (PFEM), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-MetaboHUB-Clermont, This work was supported by Biogenouest R©, Lifegrid (Auvergne), and by the IDEALG project [ANR-10-BTBR-04], IFB [ANR-11-INBS-0013] and MetaboHUB [ANR-11-INBS-0010] grants., Université Sciences et Technologies - Bordeaux 1-Institut National de la Recherche Agronomique (INRA)-Université Bordeaux Segalen - Bordeaux 2, Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Unité de Nutrition Humaine ( UNH ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Auvergne - Clermont-Ferrand I ( UdA ) -Clermont Université, Université Pierre et Marie Curie - Paris 6 ( UPMC ), ABiMS - Informatique et bioinformatique = Analysis and Bioinformatics for Marine Science ( FR2424 ), Station Biologique de Roscoff, Toxicologie Alimentaire ( UTA ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, MetaToul-AXIOM, Plateforme de Métabolomique et de Chimie Analytique en Toxicologie, Institut National de la Recherche Agronomique ( INRA ), Biologie du fruit et pathologie ( BFP ), Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique ( INRA ) -Université Sciences et Technologies - Bordeaux 1, Dynamics, Logics and Inference for biological Systems and Sequences ( Dyliss ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -GESTION DES DONNÉES ET DE LA CONNAISSANCE ( IRISA-D7 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), CentraleSupélec-Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Télécom Bretagne-Institut National des Sciences Appliquées ( INSA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -École normale supérieure - Rennes ( ENS Rennes ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bretagne Sud ( UBS ) -CentraleSupélec-Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Télécom Bretagne-Institut National des Sciences Appliquées ( INSA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -École normale supérieure - Rennes ( ENS Rennes ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bretagne Sud ( UBS ) -Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Télécom Bretagne-Institut National des Sciences Appliquées ( INSA ) -École normale supérieure - Rennes ( ENS Rennes ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bretagne Sud ( UBS ), UMR 6241, Laboratoire d'Informatique de Nantes-Atlantique ( LINA ), Laboratoire d’Intégration des Systèmes et Technologies ( LIST/LADIS ), Commissariat à l'Energie Atomique et aux Energies Alternatives ( CEA ), Centre National de la Recherche Scientifique ( CNRS ), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Station biologique de Roscoff [Roscoff] ( SBR ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Centre National de la Recherche Scientifique ( CNRS ), LADIS, Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Computer science, [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, data analysis, Gene Expression, SOFTWARE, computer.software_genre, Biochemistry, Field (computer science), Workflow, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, plateforme collaborative, analyse statistique, collaborative online platform, bioinformatics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], metabolomics, Computer Science Applications, Computational Mathematics, Identification (information), Computational Theory and Mathematics, annotation, Statistical analysis, statistics, WEB SERVER, Alimentation et Nutrition, Algorithms, data processing, Statistics and Probability, Signal processing, [ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR], analyse métabolomique, World Wide Web, Metabolomics, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Food and Nutrition, Molecular Biology, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], IDENTIFICATION, business.industry, expertise collective, Computational Biology, Applications Notes, MASS-SPECTROMETRY DATA, Virtual machine, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Data pre-processing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software engineering, business, computer, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

Paper reference: Giacomoni et al. (2014) Workflow4Metabolomics: A collaborative research infrastructure for com- putational metabolomics. Bioinformatics http://dx.doi.org/10.1093/bioinformatics/btu813Paper reference: Giacomoni et al. (2014) Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics http://dx.doi.org/10.1093/bioinformatics/btu813; In the context of an emergent and fast evolving science, the development of various tools dedicated to metabolomic data processing and data analysis increased. Because metabolomic analyses require a variety of steps involving various disciplines from analytical chemistry to statistics and bioinformatics, it requires many skills and expertise. However, despite this abundance of tools, standardization is lacking in these diversity of programs, as well as infrastructure to handle and link the different steps of metabolomic analyses. We recently implemented Workflow4Metabolomics (W4M), a collaborative online platform hosting and providing a full pipelinefor metabolomics from data preprocessing to annotation including statistical analysis. It is not designed to respond to only one specific type of metabolomic analysis, but to cover a maximum range of possible approaches - as metabolomics is a complex science that can be studied through various complementary analytical techniques. Thus, more than just gathering programs, W4M provides relevant combinations of generic and specific tools, a large part of which being developed and sustained by the partners providing this virtual research environment (VRE). Moreover, using Galaxy, a web-based platform technology, W4M provides modules from various sources and of various types. This platform allows hosted tools to be run and linked together via an instinctive and ergonomic interface, which is beneficial for both beginners and experts in metabolomics. W4M gets its strength from the collaboration of complementary teams from bioinformatics and metabolomics environment. Initiated by the collaboration between two platforms, it gathers today six research teams and platforms with a higher diversity in skills and expertise. It allows a continuous enrichment in the service provided, with addition of new modules and new possible workflows dedicated to cover a large scope of the increasing needs of the metabolomic community. Moreover,the ‘open-source’ aspect of this platform allows to open it to new collaborators bringing specific expertise that can be highlighted and disseminated in the metabolomic community

Published
2015