217 research outputs found
Clustering in Geo-Social Networks
The rapid growth of Geo-Social Networks (GeoSNs) provides a new and rich form of data. Users of
GeoSNs can capture their geographic locations and share them with other users via an operation named checkin. Thus, GeoSNs can track the connections (and the time of these connections) of geographic data to their users. In addition, the users are organized in a social network, which can be extended to a heterogeneous network if the connections to places via checkins are also considered. The goal of this paper is to analyze the opportunities in clustering this rich form of data. We first present a model for clustering geographic locations, based on GeoSN data. Then, we discuss how this model
can be extended to consider temporal information from checkins. Finally, we study how the accuracy
of community detection approaches can be improved by taking into account the checkins of users in a
GeoSN.published_or_final_versio
Visual Analytics Methods for Exploring Geographically Networked Phenomena
abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.
Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201
At the crossroads of big science, open science, and technology transfer
Les grans infraestructures cientĂfiques s’enfronten a demandes creixents de responsabilitat pĂşblica, no nomĂ©s per la seva contribuciĂł al descobriment cientĂfic, sinĂł tambĂ© per la seva capacitat de generar valor econòmic secundari. Per construir i operar les seves infraestructures sofisticades, sovint generen tecnologies frontereres dissenyant i construint solucions tècniques per a problemes d’enginyeria complexos i sense precedents. En paral·lel, la dècada anterior ha presenciat la rĂ pida irrupciĂł de canvis tecnològics que han afectat la manera com es fa i es comparteix la ciència, cosa que ha comportat l’emergència del concepte d’Open Science (OS). Els governs avancen rĂ pidament vers aquest paradigma de OS i demanen a les grans infraestructures cientĂfiques que "obrin" els seus processos cientĂfics. No obstant, aquestes dues forces s'oposen, ja que la comercialitzaciĂł de tecnologies i resultats cientĂfics requereixen normalment d’inversions financeres importants i les empreses nomĂ©s estan disposades a assumir aquest cost si poden protegir la innovaciĂł de la imitaciĂł o de la competència deslleial. Aquesta tesi doctoral tĂ© com a objectiu comprendre com les noves aplicacions de les TIC afecten els resultats de la recerca i la transferència de tecnologia resultant en el context de les grans infraestructures cientĂfiques. La tesis pretĂ©n descobrir les tensions entre aquests dos vectors normatius, aixĂ com identificar els mecanismes que s’utilitzen per superar-les. La tesis es compon de quatre estudis: 1) Un estudi que aplica un mètode de recerca mixt que combina dades de dues enquestes d’escala global realitzades online (2016, 2018), amb dos cas d’estudi de dues comunitats cientĂfiques en fĂsica d’alta energia i biologia molecular que avaluen els factors explicatius darrere les prĂ ctiques de compartir dades per part dels cientĂfics; 2) Un estudi de cas d’Open Targets, una infraestructura d’informaciĂł basada en dades considerades bens comuns, on el Laboratori Europeu de Biologia Molecular-EBI i empreses farmacèutiques col·laboren i comparteixen dades cientĂfiques i eines tecnològiques per accelerar el descobriment de medicaments; 3) Un estudi d’un conjunt de dades Ăşnic de 170 projectes finançats en el marc d’ATTRACT (un nou instrument de la ComissiĂł Europea liderat per les grans infraestructures cientĂfiques europees) que tĂ© com a objectiu comprendre la naturalesa del procĂ©s de serendipitat que hi ha darrere de la transiciĂł de tecnologies de grans infraestructures cientĂfiques a aplicacions comercials abans no anticipades. ; i 4) un cas d’estudi sobre la tecnologia White Rabbit, un hardware sofisticat de codi obert desenvolupat al Consell Europeu per a la Recerca Nuclear (CERN) en col·laboraciĂł amb un extens ecosistema d’empreses.Las grandes infraestructuras cientĂficas se enfrentan a crecientes demandas de responsabilidad pĂşblica, no solo por su contribuciĂłn al descubrimiento cientĂfico sino tambiĂ©n por su capacidad de generar valor econĂłmico para la sociedad. Para construir y operar sus sofisticadas infraestructuras, a menudo generan tecnologĂas de vanguardia al diseñar y construir soluciones tĂ©cnicas para problemas de ingenierĂa complejos y sin precedentes. Paralelamente, la dĂ©cada anterior ha visto la irrupciĂłn de rápidos cambios tecnolĂłgicos que afectan la forma en que se genera y comparte la ciencia, lo que ha llevado a acuñar el concepto de Open Science (OS). Los gobiernos se están moviendo rápidamente hacia este nuevo paradigma y están pidiendo a las grandes infraestructuras cientĂficas que "abran" el proceso cientĂfico. Sin embargo, estas dos fuerzas se oponen, ya que la comercializaciĂłn de tecnologĂa y productos cientĂficos generalmente requiere importantes inversiones financieras y las empresas están dispuestas a asumir este coste solo si pueden proteger la innovaciĂłn de la imitaciĂłn o la competencia desleal. Esta tesis doctoral tiene como objetivo comprender cĂłmo las nuevas aplicaciones de las TIC están afectando los resultados cientĂficos y la transferencia de tecnologĂa resultante en el contexto de las grandes infraestructuras cientĂficas. La tesis pretende descubrir las tensiones entre estas dos fuerzas normativas e identificar los mecanismos que se emplean para superarlas. La tesis se compone de cuatro estudios: 1) Un estudio que emplea un mĂ©todo mixto de investigaciĂłn que combina datos de dos encuestas de escala global realizadas online (2016, 2018), con dos caso de estudio sobre dos comunidades cientĂficas distintas -fĂsica de alta energĂa y biologĂa molecular- que evalĂşan los factores explicativos detrás de las prácticas de intercambio de datos cientĂficos; 2) Un caso de estudio sobre Open Targets, una infraestructura de informaciĂłn basada en datos considerados como bienes comunes, donde el Laboratorio Europeo de BiologĂa Molecular-EBI y compañĂas farmacĂ©uticas colaboran y comparten datos cientĂficos y herramientas tecnolĂłgicas para acelerar el descubrimiento de fármacos; 3) Un estudio de un conjunto de datos Ăşnico de 170 proyectos financiados bajo ATTRACT, un nuevo instrumento de la ComisiĂłn Europea liderado por grandes infraestructuras cientĂficas europeas, que tiene como objetivo comprender la naturaleza del proceso fortuito detrás de la transiciĂłn de las tecnologĂas de grandes infraestructuras cientĂficas a aplicaciones comerciales previamente no anticipadas ; y 4) un estudio de caso de la tecnologĂa White Rabbit, un sofisticado hardware de cĂłdigo abierto desarrollado en el Consejo Europeo de InvestigaciĂłn Nuclear (CERN) en colaboraciĂłn con un extenso ecosistema de empresas.Big science infrastructures are confronting increasing demands for public accountability, not only within scientific discovery but also their capacity to generate secondary economic value. To build and operate their sophisticated infrastructures, big science often generates frontier technologies by designing and building technical solutions to complex and unprecedented engineering problems. In parallel, the previous decade has seen the disruption of rapid technological changes impacting the way science is done and shared, which has led to the coining of the concept of Open Science (OS). Governments are quickly moving towards the OS paradigm and asking big science centres to "open up” the scientific process. Yet these two forces run in opposition as the commercialization of scientific outputs usually requires significant financial investments and companies are willing to bear this cost only if they can protect the innovation from imitation or unfair competition. This PhD dissertation aims at understanding how new applications of ICT are affecting primary research outcomes and the resultant technology transfer in the context of big and OS. It attempts to uncover the tensions in these two normative forces and identify the mechanisms that are employed to overcome them. The dissertation is comprised of four separate studies: 1) A mixed-method study combining two large-scale global online surveys to research scientists (2016, 2018), with two case studies in high energy physics and molecular biology scientific communities that assess explanatory factors behind scientific data-sharing practices; 2) A case study of Open Targets, an information infrastructure based upon data commons, where European Molecular Biology Laboratory-EBI and pharmaceutical companies collaborate and share scientific data and technological tools to accelerate drug discovery; 3) A study of a unique dataset of 170 projects funded under ATTRACT -a novel policy instrument of the European Commission lead by European big science infrastructures- which aims to understand the nature of the serendipitous process behind transitioning big science technologies to previously unanticipated commercial applications; and 4) a case study of White Rabbit technology, a sophisticated open-source hardware developed at the European Council for Nuclear Research (CERN) in collaboration with an extensive ecosystem of companies
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