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

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

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

Data-Intensive Modelling and Simulation in Life Sciences and Socio-economical and Physical Sciences

First paragraph: This special issue of the journal Data Science and Engineering is a result of the work fostered by the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), an EU-funded research network across 30+ European countries and overseas partners (http://chipset-cost.eu/).Output Type: Editoria

Unsupervised tracking of time-evolving data streams and an application to short-term urban traffic flow forecasting

I am indebted to many people for their help and support I receive during my Ph.D. study and research at DIBRIS-University of Genoa. First and foremost, I would like to express my sincere thanks to my supervisors Prof.Dr. Masulli, and Prof.Dr. Rovetta for the invaluable guidance, frequent meetings, and discussions, and the encouragement and support on my way of research. I thanks all the members of the DIBRIS for their support and kindness during my 4 years Ph.D. I would like also to acknowledge the contribution of the projects Piattaforma per la mobili\ue0 Urbana con Gestione delle INformazioni da sorgenti eterogenee (PLUG-IN) and COST Action IC1406 High Performance Modelling and Simulation for Big Data Applications (cHiPSet). Last and most importantly, I wish to thanks my family: my wife Shaimaa who stays with me through the joys and pains; my daughter and son whom gives me happiness every-day; and my parents for their constant love and encouragement

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

This chapter presents the authors’ work for the Case Study entitled “Delivering Social Media with Scalability” within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with potential solutions addressing the problems of the area and described how these fit in the general ecosystem

Spiral extrusion die design using modified differential evolution algorithm

In this work, a spiral extrusion die for industrial production of plastic foil has been designed using a modified differential evolution algorithm. The proposed method managed to provide a die design that was compliant with all demands of the foil manufacturer and lowered the production cost. Third-Party software is used to compute the die characteristics from the geometry designed by modified differential evolution. © 2019, Brno University of Technology. All rights reserved

Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.info:eu-repo/semantics/publishedVersio