2012 IMSAloquium, Student Investigation Showcase

Through SIR and its partnerships, IMSA students engage in rich opportunities to pursue compelling questions of interest, conduct investigations, engage with extraordinary advisors, communicate findings, and ultimately impact society.https://digitalcommons.imsa.edu/archives_sir/1004/thumbnail.jp

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

2014 Annual Research Symposium Abstract Book

2014 annual volume of abstracts for science research projects conducted by students at Trinity College

Fair vaccination strategies with influence maximization : a case study on COVID-19

Pendant la pandémie de Covid-19, les minorités raciales et les groupes économiquement défavorisés ont connu des taux accrus d’infection, d’hospitalisation et de décès dans les zones urbaines. Cette disparité témoigne de l’oppression systématique à laquelle sont confrontées les minorités raciales et la classe ouvrière, qui s’étend évidemment aux services de santé. Les inégalités flagrantes en matière de santé étaient évidentes avant que les vaccins ne soient disponibles, nous ne pouvons donc pas simplement les attribuer à des attitudes culturelles d’hésitation à la vaccination. Dans ce travail, nous présentons des solutions pour optimiser la distribution équitable des vaccins pour différents groupes démographiques, afin de promouvoir un accès équitable aux vaccins lors du premier cycle d’attribution. Nous nous appuyons sur des travaux antérieurs pour construire des réseaux de mobilité de trois zones métropolitaines américaines en utilisant des données de visites réelles dans des lieux publics au cours des premières semaines de la pandémie. Nous proposons une nouvelle méthode utilisant la maximisation de l’influence pour détecter les quartiers les plus influents de la zone urbaine en termes d’efficacité dans la propagation de la maladie. Nous modélisons ensuite la propagation ultérieure de la maladie avec ces quartiers sélectionnés vaccinés. De plus, nous introduisons des considérations d’équité afin de mettre en œuvre un accès équitable aux vaccins pour les groupes raciaux et les groupes de revenus du réseau. Pour fusionner nos solutions avec les stratégies actuelles, nous combinons nos stratégies équitables avec une méthode de priorisation pour les groupes plus âgés du réseau.During the Covid-19 pandemic, racial minorities and economically-disadvantaged groups experienced heightened rates of infection, hospitalization and death in urban areas. This disparity speaks to the systematic oppression faced by racial minorities and the working classes, which evidently extends to healthcare provisions. The stark inequalities in health outcomes were clear before vaccines became available, so we cannot simply attribute this to cultural attitudes of vaccine hesitancy. In this work, we present solutions to optimize the fair distribution of vaccines for different demographic groups, in order to promote equitable vaccine access in the first round of allocation. We build on previous work to construct mobility networks of three US metropolitan areas using data of real visits to public places during the first weeks of the pandemic. We propose a novel method using influence maximization (IM) to detect the most influential neighborhoods in the urban area in terms of efficacy in spreading the disease. We then model the subsequent disease spread with these selected neighborhoods vaccinated. Additionally, we introduce fairness considerations, to implement equitable vaccine access for racial groups and income groups in the network. To merge our solutions with current strategies, we combine our fair strategies with a prioritization method for older-age groups in the network

Temporal graph mining and distributed processing

With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction. We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data. Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of distributed graph processing is to correctly partition the graph data between different machine. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.Con el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacción humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo también están en alza. Todas estas interacciones no son más que una representación de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades únicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos información y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo estático G=(V, E). Las redes de interacción se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacción sobre una arista representa una interacción entre dos usuarios (correo electrónico, llamada, retweet), o en el caso de una red financiera una interacción entre dos cuentas para representar una transacción. Analizamos las redes de interacción bajo múltiples escenarios. En el primero, estudiamos las redes de interacción bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar información a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario añadimos restricciones adicionales respecto como la información fluye entre nodos. Asumimos que un nodo puede mandar información a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales también asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximización de influencia restringido temporalmente. Analizando los datos de la red de interacción bajo nuestro modelo intentamos descubrir los k nodos más influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicación-a-ubicación usando datos de redes sociales basades en localización (LBSNs). En el mismo escenario también minamos camínos cíclicos temporales para entender los patrones de comunicación en una red. Existen múltiples aplicaciones para cíclos temporales y aparecen naturalmente en redes de comunicación donde una persona envía un mensaje y después de un tiempo reacciona a una cadena de reacciones de compañeros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros análisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, también estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas máquinas. Gran cantidad de investigación se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partición no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento actúa dado un grafo y un algoritmo

Temporal graph mining and distributed processing

Cotutela Universitat Politècnica de Catalunya i Université Libre de BruxellesWith the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamics of the network. Understanding the dynamics of the network is very important because it encapsulates the way we communicate, socialize, consume information and get influenced. To this end, in this PhD thesis, we focus on analyzing an interaction network to understand how the underlying network is being used. We define interaction network as a sequence of time-stamped interactions E over edges of a static graph G=(V, E). Interaction networks can be used to model many real-world networks for example, in a social network or a communication network, each interaction over an edge represents an interaction between two users, e.g., emailing, making a call, re-tweeting, or in case of the financial network an interaction between two accounts to represent a transaction. We analyze interaction network under two settings. In the first setting, we study interaction network under a sliding window model. We assume a node could pass information to other nodes if they are connected to them using edges present in a time window. In this model, we study how the importance or centrality of a node evolves over time. In the second setting, we put additional constraints on how information flows between nodes. We assume a node could pass information to other nodes only if there is a temporal path between them. To restrict the length of the temporal paths we consider a time window in this approach as well. We apply this model to solve the time-constrained influence maximization problem. By analyzing the interaction network data under our model we find the top-k most influential nodes. We test our model both on human-human interaction using social network data as well as on location-location interaction using location-based social network(LBSNs) data. In the same setting, we also mine temporal cyclic paths to understand the communication patterns in a network. Temporal cycles have many applications and appear naturally in communication networks where one person posts a message and after a while reacts to a thread of reactions from peers on the post. In financial networks, on the other hand, the presence of a temporal cycle could be indicative of certain types of fraud. We provide efficient algorithms for all our analysis and test their efficiency and effectiveness on real-world data. Finally, given that many of the algorithms we study have huge computational demands, we also studied distributed graph processing algorithms. An important aspect of distributed graph processing is to correctly partition the graph data between different machine. A lot of research has been done on efficient graph partitioning strategies but there is no one good partitioning strategy for all kind of graphs and algorithms. Choosing the best partitioning strategy is nontrivial and is mostly a trial and error exercise. To address this problem we provide a cost model based approach to give a better understanding of how a given partitioning strategy is performing for a given graph and algorithm.Con el reciente crecimiento de las redes sociales y el deseo humano de interactuar con el mundo digital, una gran cantidad de datos de interacción humano-a-humano o humano-a-dispositivo se generan cada segundo. Con el auge de los dispositivos IoT, las interacciones dispositivo-a-dispositivo también están en alza. Todas estas interacciones no son más que una representación de como la red subyacente conecta distintas entidades en el tiempo. Modelar estas interacciones en forma de red de interacciones presenta una gran cantidad de oportunidades únicas para descubrir patrones interesantes y entender la dinamicidad de la red. Entender la dinamicidad de la red es clave ya que encapsula la forma en la que nos comunicamos, socializamos, consumimos información y somos influenciados. Para ello, en esta tesis doctoral, nos centramos en analizar una red de interacciones para entender como la red subyacente es usada. Definimos una red de interacciones como una sequencia de interacciones grabadas en el tiempo E sobre aristas de un grafo estático G=(V, E). Las redes de interacción se pueden usar para modelar gran cantidad de aplicaciones reales, por ejemplo en una red social o de comunicaciones cada interacción sobre una arista representa una interacción entre dos usuarios (correo electrónico, llamada, retweet), o en el caso de una red financiera una interacción entre dos cuentas para representar una transacción. Analizamos las redes de interacción bajo múltiples escenarios. En el primero, estudiamos las redes de interacción bajo un modelo de ventana deslizante. Asumimos que un nodo puede mandar información a otros nodos si estan conectados utilizando aristas presentes en una ventana temporal. En este modelo, estudiamos como la importancia o centralidad de un nodo evoluciona en el tiempo. En el segundo escenario añadimos restricciones adicionales respecto como la información fluye entre nodos. Asumimos que un nodo puede mandar información a otros nodos solo si existe un camino temporal entre ellos. Para restringir la longitud de los caminos temporales también asumimos una ventana temporal. Aplicamos este modelo para resolver este problema de maximización de influencia restringido temporalmente. Analizando los datos de la red de interacción bajo nuestro modelo intentamos descubrir los k nodos más influyentes. Examinamos nuestro modelo en interacciones humano-a-humano, usando datos de redes sociales, como en ubicación-a-ubicación usando datos de redes sociales basades en localización (LBSNs). En el mismo escenario también minamos camínos cíclicos temporales para entender los patrones de comunicación en una red. Existen múltiples aplicaciones para cíclos temporales y aparecen naturalmente en redes de comunicación donde una persona envía un mensaje y después de un tiempo reacciona a una cadena de reacciones de compañeros en el mensaje. En redes financieras, por otro lado, la presencia de un ciclo temporal puede indicar ciertos tipos de fraude. Proponemos algoritmos eficientes para todos nuestros análisis y evaluamos su eficiencia y efectividad en datos reales. Finalmente, dado que muchos de los algoritmos estudiados tienen una gran demanda computacional, también estudiamos los algoritmos de procesado distribuido de grafos. Un aspecto importante de procesado distribuido de grafos es el de correctamente particionar los datos del grafo entre distintas máquinas. Gran cantidad de investigación se ha realizado en estrategias para particionar eficientemente un grafo, pero no existe un particionamento bueno para todos los tipos de grafos y algoritmos. Escoger la mejor estrategia de partición no es trivial y es mayoritariamente un ejercicio de prueba y error. Con tal de abordar este problema, proporcionamos un modelo de costes para dar un mejor entendimiento en como una estrategia de particionamiento actúa dado un grafo y un algoritmo.Postprint (published version

Efektivní algoritmy pro problémy se sociálním vlivem u velkých sítí

In recent years, the dizzying explosion of data and information results from social networks with millions to billions of users, such as Facebook, YouTube, Twitter, and LinkedIn. Users can use online social networks (OSNs) to quickly trade information, communicate with other users, and keep their information up-to-date. The challenge of spreading information on social networks that arises in practice requires effective information management solutions, such as disseminating useful information, maximizing the influence of information transmission, and preventing disinformation, rumors, and viruses from being disseminated. Motivated by the above issues, we investigate the problem of information diffusion on OSNs. We study this problem based on two models, Independent Cascade (IC) and Linear Threshold (LT), and classical Influence Maximization (IM) in online social networks. In addition, we investigate various aspects of IM problems, such as budget variations, topics of interest, multiple competitors, and others. Moreover, we also investigate and apply the theory of combinatorial optimization problems to solve one of the current concerns in social networks, maximizing the influence on the groups and topics in social networks. In general, the main goals of the Ph.D thesis proposal are as follows. 1. We investigate the Multi-Threshold problem for IM, which is a variant of the IM problem with threshold constraints. We propose an efficient algorithm that IM for multiple thresholds in the social network. In particular, we develop a novel algorithmic framework that can use the solution to a smaller threshold to find that of larger ones. 2. We study the Group Influence Maximization problem and introduce an efficient group influence maximization algorithm with more advantages than each node’s influence in networks, using a novel sampling technique to estimate the epsilon group function. We also devised an approximation algorithm to estimate multiple candidate solutions with theoretical guarantee. 3. We investigate an approach for Influence Maximization problem with k-topic under constraints in social network. More specifically, we also study a streaming algorithm that combines an optimization algorithm to improve the approximation algorithm and theoretical guarantee in terms of solution quality and running time.V posledních letech je závratná exploze dat a informací výsledkem sociálních sítí s miliony až miliardami uživatelů, jako jsou Facebook, YouTube, Twitter a LinkedIn. Uživatelé mohou využívat online sociální sítě (OSNs) k rychlému obchodování s informacemi, komunikaci s ostatními uživateli a udržování jejich informací v aktuálním stavu. Výzva šíření informací na sociálních sítích, která se v praxi objevuje, vyžaduje efektivní řešení správy informací, jako je šíření užitečných informací, maximalizace vlivu přenosu informací a zabránění šíření dezinformací, fám a virů. Motivováni výše uvedenými problémy zkoumáme problém šíření informací na OSN. Tento problém studujeme na základě dvou modelů, Independent Cascade (IC) a Linear Threshold (LT) a klasické Influence Maximization (IM) v online sociálních sítích. Kromě toho zkoumáme různé aspekty problémů s rychlým zasíláním zpráv, jako jsou změny rozpočtu, témata zájmu, více konkurentů a další. Kromě toho také zkoumáme a aplikujeme teorii kombinatorických optimalizačních problémů k vyřešení jednoho ze současných problémů v sociálních sítích, maximalizujeme vliv na skupiny a témata v sociálních sítích. Obecně lze říci, že hlavní cíle Ph.D. návrh diplomové práce je následující. 1. Zkoumáme problém Multi-Threshold pro IM, což je varianta problému IM s prahovými omezeními. Navrhujeme účinný algoritmus, který IM pro více prahů v sociální síti. Zejména vyvíjíme nový algoritmický rámec, který může použít řešení pro menší práh k nalezení prahu většího. 2. Studujeme problém maximalizace vlivu skupiny a zavádíme účinný algoritmus maxima- lizace vlivu skupiny s více výhodami, než je vliv každého uzlu v sítích, pomocí nové vzorkovací techniky k odhadu funkce skupiny epsilon. Navrhujeme také aproximační algoritmus pro odhad více kandidátních řešení s teoretickou zárukou. 3. Zkoumáme přístup pro maximalizaci vlivu s k-téma pod omezeními v rozsáhlé síti. Konkrétněji budeme studovat novou metriku, která kombinuje optimalizační algoritmus pro zlepšení aproximačního algoritmu z hlediska kvality řešení a doby běhu na základě kliky a komunity v komplexních sítích.460 - Katedra informatikyvyhově

Born Digital / Grown Digital: Assessing the Future Competitiveness of the EU Video Games Software Industry

This report reflects the findings of the JRC-IPTS study on the Video games Industry, with a focus on two specific activities: online and mobile video games. The report starts by introducing the technologies, their characteristics, market diffusion and barriers to take up, and their potential economic impact, before moving to an analysis of their contribution to the competitiveness of the European ICT industry. The research is based on internal and external expertise, literature reviews and desk research, several workshops and syntheses of the current state of the knowledge. The results were reviewed by experts and in dedicated workshops. The report concludes that the general expectations for the next years foresee a speeded up migration of contents and services to digital, in a scenario of rapidly increasing convergence of digital technologies and integration of media services taking advantage of improved and permanent network connections. The role of the so-called creative content industry is expected to increase accordingly. Communication services and media industry will co-evolve on the playground of the Internet of services, along with a product to service transformation of the software market in general. In this general context the Video games Software industry plays and is expected to play a major role. The games industry may become a major driver of the development of networks as it has been in the past for the development of computer hardware.JRC.DDG.J.4-Information Societ