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We propose an approach for supporting attribute exploration by web information retrieval, in particular by posing appropriate queries to search engines, crowd sourcing systems, and the linked open data cloud. We discuss underlying general assumptions for this to work and the degree to which these can be taken for granted.

Until recently, transit data lacked a common data format that could be used to share and integrate information among multiple agencies. In 2005, however, Google worked with Tri-Met in Oregon to create the General Transit Feed Specification (GTFS), an open data format now used by all transit agencies that participate in Google Maps. GTFS feeds contain data for scheduled transit service including stop and route locations, schedules and fare information. The broad adoption of GTFS by transit agencies has made it a de facto standard. Those agencies using it are able to participate in a host of traveler services designed for GTFS, most notably transit trip planners. Still, analysts have not widely used GTFS as a data source for transit planning because of the newness of the technology. The objectives of this project are to demonstrate that GTFS feeds are an efficient data source for calculating key transit service metrics and to evaluate the validity of GTFS feeds as a data source. To demonstrate GTFS feeds’ analytic potential, the author created a tool called GTFS Reader, which imports GTFS feeds into a database using open-source products. GTFS Reader also includes a series of queries that calculate metrics like headways, route lengths and stop-spacing. To evaluate the validity of GTFS feeds, annual vehicle revenue miles and hours from the National Transit Database (NTD) are compared to the calculated values from agencies whose GTFS feeds are available. The key finding of this work is that well-formed GTFS feeds are an accurate representation of transit networks and that the method of aggregation presented in this research can be used to effectively and efficiently calculate metrics for transit agencies. The daily aggregation method is more accurate than the weekly aggregation method, both introduced in this thesis, but practical limitations on processing time favor the weekly method. The reliability of GTFS feed data for smaller agencies is less conclusive than that of larger agencies because of discrepancies found in smaller agencies when their GTFS-generated metrics were compared to those in the NTD. This research will be of particular interest to transit and policy analysts, researchers and transit planners.

Das kulturelle Gedächtnis speichert immer gewaltigere Mengen von Informationen und Daten. Doch nur ein verschwindend geringer Teil der Inhalte ist derzeit über digitale Kanäle recherchierbar und verfügbar. Die Projekte mediaglobe und yovisto ermöglichen, den wachsenden Bestand an audiovisuellen Dokumenten auffindbar und nutzbar zu machen und begleiten Medienarchive in die digitale Zukunft. mediaglobe hat das Ziel, durch automatisierte und semantische Verfahren audiovisuelle Dokumente zur deutschen Zeitgeschichte zu erschließen und verfügbar zu machen. Die Vision von mediaglobe ist ein web-basierter Zugang zu umfassenden digitalen AV-Inhalten in Medienarchiven. Dazu bietet mediaglobe zahlreiche automatisierte Verfahren zur Analyse von audiovisuellen Daten, wie z.B. strukturelle Analyse, Texterkennung im Video, Sprachanalyse oder Genreanalyse. Der Einsatz semantischer Technologien verknüpft die Ergebnisse der AV-Analyse und verbessert qualitativ und quantitativ die Ergebnisse der Multimedia-Suche. Ein Tool zum Rechtemanagement liefert Informationen über die Verfügbarkeit der Inhalte. Innovative und intuitiv bedienbare Benutzeroberflächen machen den Zugang zu kulturellem Erbe aktiv erlebbar. mediaglobe vereinigt die Projektpartner Hasso-Plattner Institut für Softwaresystemtechnik (HPI), Medien-Bildungsgesellschaft Babelsberg, FlowWorks und das Archiv der defa Spektrum. mediaglobe wird im Rahmen des Forschungsprogramms »THESEUS – Neue Technologien für das Internet der Dienste« durch das Bundesministerium für Wirtschaft und Technologie gefördert. Die Videosuchmaschine yovisto hingegen ist spezialisiert auf Aufzeichnungen akademischer Lehrveranstaltungen und implementiert explorative und semantische Suchstrategien. yovisto unterstützt einen mehrstufigen 'explorativen' Suchprozess, in dem der Suchende die Möglichkeit erhält, den Bestand des zugrundeliegenden Medienarchivs über vielfältige Pfade entsprechend seines jeweiligen Interesses zu erkunden, so dass am Ende dieses Suchprozesses Informationen entdeckt werden, von deren Existenz der Suchende bislang nichts wusste. Um dies zu ermöglichen vereinigt yovisto automatisierte semantische Medienanalyse mit benutzergenerierten Metadaten zur inhaltlichen Erschließung von AV-Daten und ermöglicht dadurch eine punktgenaue inhaltsbasierte Suche in Videoarchiven.

Nous entendons beaucoup parler de Big Data, Open Data, Social Data, Scientific Data, etc. L’importance qui est apportée aux données en général est très élevée. L’analyse de ces données est importante si l’objectif est de réussir à en extraire de la valeur pour pouvoir les utiliser. Les travaux présentés dans cette thèse concernent la compréhension, l’évaluation, la correction/modification, la gestion et finalement l’intégration de données, pour permettre leur exploitation. Notre recherche étudie exclusivement les données ouvertes (DOs - Open Data) et plus précisément celles structurées sous format tabulaire (CSV). Le terme Open Data est apparu pour la première fois en 1995. Il a été utilisé par le groupe GCDIS (Global Change Data and Information System) (États-Unis) pour encourager les entités, possédant les mêmes intérêts et préoccupations, à partager leurs données [Data et System, 1995]. Le mouvement des données ouvertes étant récent, il s’agit d’un champ qui est actuellement en grande croissance. Son importance est actuellement très forte. L’encouragement donné par les gouvernements et institutions publiques à ce que leurs données soient publiées a sans doute un rôle important à ce niveau. / We hear a lot about Big Data, Open Data, Social Data, Scientific Data, etc. The importance currently given to data is, in general, very high. We are living in the era of massive data. The analysis of these data is important if the objective is to successfully extract value from it so that they can be used. The work presented in this thesis project is related with the understanding, assessment, correction/modification, management and finally the integration of the data, in order to allow their respective exploitation and reuse. Our research is exclusively focused on Open Data and, more precisely, Open Data organized in tabular form (CSV - being one of the most widely used formats in the Open Data domain). The first time that the term Open Data appeared was in 1995 when the group GCDIS (Global Change Data and Information System) (from United States) used this expression to encourage entities, having the same interests and concerns, to share their data [Data et System, 1995]. However, the Open Data movement has only recently undergone a sharp increase. It has become a popular phenomenon all over the world. Being the Open Data movement recent, it is a field that is currently growing and its importance is very strong. The encouragement given by governments and public institutions to have their data published openly has an important role at this level.

Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-08-30T17:25:22Z No. of bitstreams: 1 CAIO LIBÂNIO MELO JERÔNIMO – DISSERTAÇÃO (PPGCC) 2017.pdf: 4821943 bytes, checksum: 615dc29730ed480c902a5496dce5492f (MD5) / Made available in DSpace on 2018-08-30T17:25:22Z (GMT). No. of bitstreams: 1 CAIO LIBÂNIO MELO JERÔNIMO – DISSERTAÇÃO (PPGCC) 2017.pdf: 4821943 bytes, checksum: 615dc29730ed480c902a5496dce5492f (MD5) Previous issue date: 2017-07-07 / Capes / A demanda constante por melhorias na qualidade de vida dos habitantes das grandes cidades, somado à crescente urbanização desses centros, torna imprescindível a utilização de meios tecnológicos para um melhor entendimento da dinâmica dos centros urbanos e como seus habitantes interagem nesses ambientes. Nesse sentido, o aumento na utilização de dispositivos eletrônicos equipados com sistemas GPS e o constante anseio da humanidade por comunicação e, mais atualmente, por conexão à internet, vem criando novas oportunidades de estudo e também grandes desafios, especialmente no que tange a grande quantidade de dados gerados pelas redes sociais. Diversas pesquisas vêm utilizando esses dados para realizar estudos que buscam compreender traços do comportamento humano, especialmente no que diz respeito à mobilidade urbana e trajetórias. Porém, grande parte das pesquisas que utilizam dados georreferenciados se restringem às dimensões espaciais e temporais, desconsiderando outros aspectos que podem influenciar na mobilidade humana. Este trabalho propõe um método computacional capaz de extrair padrões de mobilidade oriundos de mensagens georreferenciadas de redes sociais e correlacioná-los com indicadores sociais, econômicos e demográficos fornecidos por órgãos governamentais, buscando assim, analisar quais possíveis fatores poderiam exercer alguma influência sobre a mobilidade dos moradores de uma grande cidade. Para validar o método proposto, foram utilizadas mensagens postadas no Twitter e um conjunto de indicadores sociais, ambos oriundos da cidade de Londres. Os resultados mostraram a existência de correlações entre padrões de mobilidade e indicadores sociais, especialmente os relacionados com condições de emprego e renda, como também com características étnico-religiosas dos indivíduos em estudo. / The constant need for improvements in life quality of inhabitants of big cities, together with the increasing urbanization of these centers, demands the use of technological means for a better understanding of the dynamics of urban centers and how their inhabitants interact in these environments. In this sense, the adoption of electronic devices equipped with GPS systems, the human need for communication and, more recently, for Internet connection, have brought new research opportunities and great challenges, especially due to the huge amount of data generated by social networks. Several studies have used this data to carry out research that seek to understand traces of human behavior, especially with respect to urban mobility and trajectories. However, much of the research that uses georeferenced data are restricted to spatial and temporal dimensions, disregarding other aspects that may influence human mobility. This work proposes a model capable of extracting mobility patterns from georeferenced messages of social networks and correlating them with social, economic and demographic indicators provided by government agencies, seeking to analyze which factors may impact in urban mobility. To evaluate the model, we used messages posted on Twitter and a set of social indicators, both related to the city of London. The results revealed the existence of correlations between mobility patterns and social indicators, especially those related to employment and income conditions, as well as ethnic and religious characteristics of the individuals under study.

This study examines how Borlänge municipality can initiate their work with open data. To open data means that the organisation has in the past kept their datasets closed from public, the point with the project is to publicize datasets to encourage opportunities and innovation in society.Since 2016 Riksarkivet has had a mission to make sure that Swedish governmental bodies have opened a dataset of some type, this mission will finish at the end of 2018. The mission itself has originated from an EU directive and been fitted to Swedish law.This study has collected data and experiences from other municipalities but also from stakeholders and an expert in open data. The study has also collected information online, such as guides, frameworks and processes. All of the collected data served as a foundation for the startplan we composed for Borlänge municipality. / Denna studie undersöker hur Borlänge kommun kan inleda sitt arbete med öppna data. Att öppna sina data betyder att verksamheten tidigare har hållit sina datamängder stängda från allmänheten, syftet med arbetet är då att offentliggöra datamängder för att främja möjligheter och innovation i samhället.Sedan 2016 har Riksarkivet haft i uppdrag att se till att samtliga svenska myndigheter har öppnat någon mängd och typ av data, detta ska vara klart till slutet av 2018. Uppdraget i sig har härstammat från ett EU direktiv och anpassats till svensk lag.Denna studie har samlat in data och erfarenheter från andra kommuner men också från intressenter och en expert inom öppna data. Studien har även samlat in information som finns online, såsom guider, ramverk och processer. Dessa insamlade data låg till grund för den startplan vi sammanställde för Borlänge kommun.

Submitted by Priscilla Araujo (priscilla@ibict.br) on 2015-11-27T16:32:51Z No. of bitstreams: 2 license_rdf: 21267 bytes, checksum: 73e23c2acaaf13389e092bd813e3223d (MD5) Mestrado_Diogo_Luiz_Jesus_Moreira_2015.pdf: 5362309 bytes, checksum: 85d98fdb041855122f99025e6e918d5f (MD5) / Made available in DSpace on 2015-11-27T16:32:51Z (GMT). No. of bitstreams: 2 license_rdf: 21267 bytes, checksum: 73e23c2acaaf13389e092bd813e3223d (MD5) Mestrado_Diogo_Luiz_Jesus_Moreira_2015.pdf: 5362309 bytes, checksum: 85d98fdb041855122f99025e6e918d5f (MD5) Previous issue date: 2015-03-31 / Esta dissertação tem como objetivo principal traçar um panorama, a partir do estudo dos aplicativos gerados, sobre como a sociedade brasileira está utilizando Dados Governamentais Abertos. Dados Governamentais Abertos correspondem à disponibilização de dados da esfera governamental em formatos predeterminados (formatos abertos e não proprietários) para a livre utilização, reutilização e redistribuição pela sociedade. A identificação dos aplicativos que utilizam Dados Governamentais Abertos no Brasil é realizada a partir de informações oficiais oriundas do Portal Brasileiro de Dados Abertos e de informações extraídas de notícias sobre Dados Governamentais Abertos no Brasil arquivadas no Observatório de Gestão da Informação a partir do Alerta Google. Desta forma, este estudo pretende desenvolver os seguintes objetivos específicos: identificar e categorizar os temas e os autores dos aplicativos que utilizam Dados Governamentais Abertos no Brasil, assim como os conjuntos de dados utilizados e suas respectivas licenças, e avaliar o grau de atualização dos aplicativos disponibilizados no Portal Brasileiro de Dados Abertos em relação às aplicações identificadas através das notícias sobre Dados Governamentais Abertos arquivadas no Observatório de Gestão da Informação. / This paper aims to give an overview, from the study of the generated applications, about as Brazilian society is using Open Government Data. Open Government Data consist of the provision of data of governmental sphere at predetermined formats (open and non-proprietary standards) for free use, reuse and redistribution by society. The identification of applications that use Open Government Data in Brazil is performed based on official information extracted from the Portal Brasileiro de Dados Abertos and information extracted from news on Open Government Data in Brazil archived on Observatório de Gestão da Informação as from Google Alerts. Thus, this study aims to develop the following specific objectives: identify and categorize the themes and the authors of the applications that use Open Government Data in Brazil, as well as datasets used and their licenses, and assess the degree of update of the applications available in Portal Brasileiro de Dados Abertos relative to applications identified through the news on Open Government Data archived on Observatório de Gestão da Informação.

The transnational traffic of narcotics has had undeniable impacts on international development, for instance, stagnant economic growth in Myanmar (Chin, 2009), unsustainable agricultural practices in Yemen (Robins, 2016), and human security threats in Columbia (Thoumi, 2013). Furthermore, globalization is a catalyst for the transnational narcotics traffic (Robins, 2016; Aas, 2007; Kelly, Maghan & Serio, 2005). Several qualitative studies exist on the transnational narcotics traffic, yet few quantitative studies examine the issue. There is thus an opportunity for novel quantitative studies on the general question: “what are the main economic factors that influence the transnational traffic of narcotics between countries?” This study looked at the specific question: “are distance and economic size correlated with the volume of narcotics traffic between countries?” This study chose the gravity model as it centres on bilateral trade (Tinbergen, 1962), accounts for trade barriers (Kalirajan, 2008) and is empirically robust (Anderson 2011). This study defined a basic functional gravity model relating a proxy of the narcotics traffic to distance and economic size. Four augmented functional gravity models were also advanced to address omitted variable bias. The research was limited conceptually to cross sectional and pooled time series data. In addition, the data was also limited practically to a convenience sample of secondary data drawn from: the United Nations Office on Drugs and Crime’s (UNODC) (2016a) Individual Drug Seizures (IDS); the World Bank’s (2016) World Development Indicators; and the CEPII’s GeoDist (2016) datasets. This study used a novel “dosage” approach to unit standardization to overcome the challenge posed by the many measures and forms of narcotics. The study used the Poisson pseudo maximum likelihood (PPML) estimator as its estimations of the gravity model are consistent (Gourieroux et al., 1984), allow heteroscedasticity (Silva & Tenreyro, 2006) and avoid back transformation bias (Cox et al., 2008). The evidence analyzed in this study seem to indicate that the gravity model may not be applicable in its current form to the transnational narcotics traffic among countries that report drug seizures to the UNODC. However, the sampling method and the choice of proxy are likely to influence these findings. Moreover, the low explanatory power of the gravity model for the narcotics traffic, reflected in the values of the pseudo-R-squared coefficient of determination, indicates that other factors are at play. For instance, authors such as Asad and Harris (2003) and Thoumi (2003) argue that institutions could be a key factor in the narcotics traffic. Future empirical research into this topic could build on the theses findings to introduce new proxies and to explore alternate theoretical frameworks.

近年來，在資通訊快速發展與公民意識崛起的環境下，開放資料開始成為各國關注的議題之一。運用開放資料致力於資訊透明、達至公眾參與與協同合作的三個目標，不但將政府資訊透明化、節省資訊流通成本，同時能夠藉由開放資料提供民眾更便捷的生活，並運用於經濟發展。 本研究運用公私協作模型，探討台灣在開放資料的發展過程中，政府、企業與公民間的互動關係，並藉此檢驗三方之間是否平衡運作。研究過程，透過相關文獻的檢閱分析三方的協作分析過程，並以運用開放資料進行商業開發的「公車通」、「KNY台灣天氣」、「觀天氣」與「天氣風險管理開發」作為本研究的個案，補足公私協作上的企業方的實務觀點。並先以企業的角度探討企業成功運用開放資料進行商業開放的關鍵因素，及現階段運用開放資料進行商業開發的發展情況。再將這企業個案放置於公私協作架構中，探討政府、企業與公民三方之間的互動關係。 本研究發現，先進者優勢、因開發者背景所取得之資源，以及對於政府開放資料的依賴程度為企業成功運用開放資料進行商業開發的關鍵因素。另外，政府、企業與公民在公私協作上，並非單向而為雙向的互動關係，且「政府」為公私民協作是否能夠平衡運作的關鍵角色。並經由此研究分析給予建議：1.企業若降低對政府開放資料的依賴程度，並運用組織人才及其關鍵資源，將能夠增加商業模式的差異性。2.政府不應干涉企業與民間於運用開放資料進行商業發展的市場。3.政府須提出完備的獎勵機制，亦或黑客公務員的加入，突破政府的穀倉效應。 / With the rise of civic consciousness, and the rapid development of information and communication technologies, open data become the important issue around the world. The purpose of the study uses the Public-Private-People-Partnership theory to investigate the interactional relationship within government, enterprises and citizen in the process of promoting open data in Taiwan. In addition, using the business model to investigate the key factors in developing business with open data successfully from the perspective of enterprises. The research adopts case study and in-depth interviews as the research tools to investigate. Furthermore, this research finds out that first-mover advantage, the resources enterprises have and the degree of dependence on open government data are the key factors in business development. In addition, the interactions within government, enterprises and citizen are not only unidirectional but bidirectional. And the operation of government will be the key factor to the balance in Public-Private-People-Partnership model. This study suggests that (a) If enterprise reduce the dependence on the open government data and use resources properly, it will increase the difference in its business model. (b) The government in Taiwan should not interfere the market in the business development with open data. (c) The government in Taiwan should set the performance incentives completely, and let more hacker civil servants join into public sector to break through silo effect.