High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management

(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Smart urban transportation management can be considered as a multifaceted big data challenge. It strongly relies on the information collected into multiple, widespread, and heterogeneous data sources as well as on the ability to extract actionable insights from them. Besides data, full stack (from platform to services and applications) Information and Communications Technology (ICT) solutions need to be specifically adopted to address smart cities challenges. Smart urban transportation management is one of the key use cases addressed in the context of the EUBra-BIGSEA (Europe-Brazil Collaboration of Big Data Scientific Research through Cloud-Centric Applications) project. This paper specifically focuses on the City Administration Dashboard, a public transport analytics application that has been developed on top of the EUBra-BIGSEA platform and used by the Municipality stakeholders of Curitiba, Brazil, to tackle urban traffic data analysis and planning challenges. The solution proposed in this paper joins together a scalable big and fast data analytics platform, a flexible and dynamic cloud infrastructure, data quality and entity matching algorithms as well as security and privacy techniques. By exploiting an interoperable programming framework based on Python Application Programming Interface (API), it allows an easy, rapid and transparent development of smart cities applications.This work was supported by the European Commission through the Cooperation Programme under EUBra-BIGSEA Horizon 2020 Grant [Este projeto e resultante da 3a Chamada Coordenada BR-UE em Tecnologias da Informacao e Comunicacao (TIC), anunciada pelo Ministerio de Ciencia, Tecnologia e Inovacao (MCTI)] under Grant 690116.Fiore, S.; Elia, D.; Pires, CE.; Mestre, DG.; Cappiello, C.; Vitali, M.; Andrade, N.... (2019). An Integrated Big and Fast Data Analytics Platform for Smart Urban Transportation Management. IEEE Access. 7:117652-117677. https://doi.org/10.1109/ACCESS.2019.2936941S117652117677

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Acceleration of Bayesian model based data analysis

Inverse problems for parameter estimation often face a choice between the use of a real-time scheme with strong approximations or rigorous post-processing with explicit uncertainty handling. Plasma physics experiments set a particularly high demand of both and a solution that meets all of these requirements is missing. Standard Bayesian analysis is an excellent tool for the case at hand, with the disadvantage of extensive processing times. This work therefore presents a solution that satisfies the scientific requirements while reducing the need for a speed vs. rigorosity trade-off.Die Bestimmung von Parametern bei inversen Problemen beinhaltet eine Abwägung zwischen vereinfachenden Annahmen für Echtzeitverfahren und rigoroser Datenanalyse mit Fehlerbetrachtung. Experimente in der Plasmaphysik stellen besonders hohe Anforderungen an beide, und eine Lösung, die diese Anforderungen erfüllt, fehlt. Die Bayessche Analyse ist ein exzellentes Werkzeug für diese Problemstellung, mit dem Nachteil langer Laufzeiten. Diese Arbeit stellt eine Lösung dar, die den Anforderungen entspricht und die Notwendigkeit der Abwägung zwischen Geschwindigkeit und Rigorosität reduziert

Design and management of image processing pipelines within CPS : Acquired experience towards the end of the FitOptiVis ECSEL Project

Cyber-Physical Systems (CPSs) are dynamic and reactive systems interacting with processes, environment and, sometimes, humans. They are often distributed with sensors and actuators, characterized for being smart, adaptive, predictive and react in real-time. Indeed, image- and video-processing pipelines are a prime source for environmental information for systems allowing them to take better decisions according to what they see. Therefore, in FitOptiVis, we are developing novel methods and tools to integrate complex image- and video-processing pipelines. FitOptiVis aims to deliver a reference architecture for describing and optimizing quality and resource management for imaging and video pipelines in CPSs both at design- and run-time. The architecture is concretized in low-power, high-performance, smart components, and in methods and tools for combined design-time and run-time multi-objective optimization and adaptation within system and environment constraints.Peer reviewe

Development of laser sources and interferometric approaches for polarization-based label-free microscopy

The project developed in this thesis describes the design and the experimental realization of optical methods which can probe the anisotropy of semitransparent media. The ability to manipulate polarized light enables a label-free imaging approach that can retrieve fundamental information about the sample structure without introducing any alteration within it. Such a potential is of great importance and methods like the ones based on polarization analysis are gaining more and more popularity in the biomedical and biophysical fields. Moreover, when they are coupled with fluorescence microscopy and nanoscopy, they may provide an invaluable tool for researchers. The optical method I developed mainly exploits the laser radiation emitted from tailored optical oscillators to dynamically generate polarization states. The realization of such states does not comprise any external active device. The resulting time-evolving polarization state once properly coupled to an optical system enables probing a sample to retrieve its anisotropies at a fast rate. The development of two different laser sources is presented together with the characterizations of their optical properties. One of them consists of a Helium-Neon laser modified by applying an external magnetic field to trigger the Zeeman effect in its active medium. The other one is a Dual-Comb source, that is a mode-locked (ML) laser generating a pair of mutually coherent twin beams. Moreover, the thesis delivers the theoretical model and the experimental realization of the optical method to probe the optical anisotropies of specimens. Finally, the technical realization of a custom laser scanning optical microscope and its imaging results obtained with such methods are reported

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems