京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23313号 / 情博第749号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 吉川 正俊 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The purpose of this study was (1) to identify visitors’ behaviors in and perceptions of linear parks, (2) to identify social media users’ behaviors in and perceptions of linear parks, and (3) to compare small data with big data. This chapter discusses the main findings and their implications for practitioners such as landscape architects and urban planners. It has three sections. The first addresses the main findings in the order of the research questions at the center of the study. The second describes implications and recommendations for practitioners. The final section discusses the limitations of the study and suggests directions for future work. This study compares two methods of data collection, focused on activities and benefits. The survey asked respondents to check all the activities they did in the park. Social media users’ activities were detected by term frequency in social media data. Both results ordered the activities similarly. For example social interaction and art viewing were most popular on the High Line, then the 606, then the High Bridge according to both methods. Both methods also reported that High Line visitors engaged in viewing from overlooks the most. As for benefits, according to both methods vistors to the 606 were more satisfied than High Line visitors with the parks’ social and natural benefits. These results suggest social media analytics can replace surveys when the textual information is sufficient for analysis. Social media analytics also differ from surveys in accuracy of results. For example, social media revealed that 606 users were interested in events and worried about housing prices and crimes, but the pre-designed survey could not capture those facts. Social media analytics can also catch hidden and more general information: through cluster analysis, we found possible reasons for the High Line’s success in the arts and in the New York City itself. These results involve general information that would be hard to identify through a survey. On the other hand, surveys provide specific information and can describe visitors’ demographics, motivations, travel information, and specific benefits. For example, 606 users tend to be young, high-income, well educated, white, and female. These data cannot be collected through social media. / Doctor of Philosophy / Turning unused infrastructure into green infrastructure, such as linear parks, is not a new approach to managing brownfields. In the last few decades, changes in the industrial structure and the development of transportation have had a profound effect on urban spatial structure. As the need for infrastructure, which played an important role in the development of past industry, has decreased, many industrial sites, power plants, and military bases have become unused. This study identifies new ways of collecting information about a new type of park, linear parks, using a new method, social media analytics. The results are then compared with survey results to establish the credibility of social media analytics. Lastly, shortcomings of social media analytics are identified. This study is meaningful in helping us understand the users of new types of parks and suggesting design and planning strategies. Regarding methodology, this study also involves evaluating the use of social media analytics and its advantages, disadvantages, and reliability.

Across a wide variety of digital devices, users create, consume, and disseminate large quantities of information. While data sometimes look like a spreadsheet or network diagram, more often for everyday users their data look more like an Amazon search page, the line-up for a fantasy football team, or a set of Yelp reviews. However, interpreting these kinds of data remains a difficult task even for experts since they often feature soft or unknown constraints (e.g. ”I want some Thai food, but I also want a good bargain”) across highly multidimensional data (i.e. rating, reviews, popularity, proximity). Existing technology is largely optimized for users with hard criteria and satisfiable constraints, and consumer systems often use representations better suited for browsing than sensemaking. In this thesis I explore ways to support soft constraint decision-making and exploratory data analysis by giving users tools that show fine-grained features of the data while at the same time displaying useful contextual information. I describe approaches for representing collaborative content history and working behavior that reveal both individual and group/dataset level features. Using these approaches, I investigate general visualizations that utilize physics to help even inexperienced users find small and large trends in multivariate data. I describe the transition of physicsbased visualization from the research space into the commercial space through a startup company, and the insights that emerged both from interviews with experts in a wide variety of industries during commercialization and from a comparative lab study. Taking one core use case from commercialization, consumer search, I develop a prototype, Fractal, which helps users explore and apply constraints to Yelp data at a variety of scales by curating and representing individual-, group-, and dataset-level features. Through a user study and theoretical model I consider how the prototype can best aide users throughout the sensemaking process. My dissertation further investigates physics-based approaches for represent multivariate data, and explores how the user’s exploration process itself can help dynamically to refine the search process and visual representation. I demonstrate that selectively representing points using clusters can extend physics-based visualizations across a variety of data scales, and help users make sense of data at scales that might otherwise overload them. My model provides a framework for stitching together a model of user interest and data features, unsupervised clustering, and visual representations for exploratory data visualization. The implications from commercialization are more broad, giving insight into why research in the visualization space is/isn’t adopted by industry, a variety of real-world use cases for multivariate exploratory data analysis, and an index of common data visualization needs in industry.

Organizations own data sources that contain millions, billions or even trillions of rows and these data are usually highly dimensional in nature. Typically, these raw repositories are comprised of numerous independent data sources that are too big to be copied or joined, with the consequence that aggregations become highly problematic. Data cubes play an essential role in facilitating fast Online Analytical Processing (OLAP) in many multi-dimensional data warehouses. Current data cube computation techniques have had some success in addressing the above-mentioned aggregation problem. However, the combined problem of reducing data cube size for very large and highly dimensional databases, while guaranteeing fast query response times, has received less attention. Another issue is that most OLAP tools often causes users to be lost in the ocean of data while performing data analysis. Often, most users are interested in only a subset of the data. For example, consider in such a scenario, a business manager who wants to answer the crucial location-related business question. "Why are my sales declining at location X"? This manager wants fast, unambiguous location-aware answers to his queries. He requires access to only the relevant ltered information, as found from the attributes that are directly correlated with his current needs. Therefore, it is important to determine and to extract, only that small data subset that is highly relevant from a particular user's location and perspective. In this thesis, we present the Personalized Smart Cube approach to address the abovementioned scenario. Our approach consists of two main parts. Firstly, we combine vertical partitioning, partial materialization and dynamic computation to drastically reduce the size of the computed data cube while guaranteeing fast query response times. Secondly, our personalization algorithm dynamically monitors user query pattern and creates a personalized data cube for each user. This ensures that users utilize only that small subset of data that is most relevant to them. Our experimental evaluation of our Personalized Smart Cube approach showed that our work compared favorably with other state-of-the-art methods. We evaluated our work focusing on three main criteria, namely the storage space used, query response time and the cost savings ratio of using a personalized cube. The results showed that our algorithm materializes a relatively smaller number of views than other techniques and it also compared favourable in terms of query response time. Further, our personalization algorithm is superior to the state-of-the art Virtual Cube algorithm, when evaluated in terms of the number of user queries that were successfully answered when using a personalized cube, instead of the base cube.

Organizations own data sources that contain millions, billions or even trillions of rows and these data are usually highly dimensional in nature. Typically, these raw repositories are comprised of numerous independent data sources that are too big to be copied or joined, with the consequence that aggregations become highly problematic. Data cubes play an essential role in facilitating fast Online Analytical Processing (OLAP) in many multi-dimensional data warehouses. Current data cube computation techniques have had some success in addressing the above-mentioned aggregation problem. However, the combined problem of reducing data cube size for very large and highly dimensional databases, while guaranteeing fast query response times, has received less attention. Another issue is that most OLAP tools often causes users to be lost in the ocean of data while performing data analysis. Often, most users are interested in only a subset of the data. For example, consider in such a scenario, a business manager who wants to answer the crucial location-related business question. "Why are my sales declining at location X"? This manager wants fast, unambiguous location-aware answers to his queries. He requires access to only the relevant ltered information, as found from the attributes that are directly correlated with his current needs. Therefore, it is important to determine and to extract, only that small data subset that is highly relevant from a particular user's location and perspective. In this thesis, we present the Personalized Smart Cube approach to address the abovementioned scenario. Our approach consists of two main parts. Firstly, we combine vertical partitioning, partial materialization and dynamic computation to drastically reduce the size of the computed data cube while guaranteeing fast query response times. Secondly, our personalization algorithm dynamically monitors user query pattern and creates a personalized data cube for each user. This ensures that users utilize only that small subset of data that is most relevant to them. Our experimental evaluation of our Personalized Smart Cube approach showed that our work compared favorably with other state-of-the-art methods. We evaluated our work focusing on three main criteria, namely the storage space used, query response time and the cost savings ratio of using a personalized cube. The results showed that our algorithm materializes a relatively smaller number of views than other techniques and it also compared favourable in terms of query response time. Further, our personalization algorithm is superior to the state-of-the art Virtual Cube algorithm, when evaluated in terms of the number of user queries that were successfully answered when using a personalized cube, instead of the base cube.

Did you stand at a door with a bunch of keys and tried to find the right one to unlock the door? Did you hold a flower and wonder the name of it? A need of object recognition could rise anytime and any where in our daily lives. With the development of mobile devices object recognition applications become possible to provide immediate assistance. However, performing complex tasks in even the most advanced mobile platforms still faces great challenges due to the limited computing resources and computing power. In this thesis, we present an object recognition system that resides and executes within a mobile device, which can efficiently extract image features and perform learning and classification. To account for the computing constraint, a novel feature extraction method that minimizes the data size and maintains data consistency is proposed. This system leverages principal component analysis method and is able to update the trained classifier when new examples become available . Our system relieves users from creating a lot of examples and makes it user friendly. The experimental results demonstrate that a learning method trained with a very small number of examples can achieve recognition accuracy above 90% in various acquisition conditions. In addition, the system is able to perform learning efficiently.

Neste trabalho estudamos a equação de ondas do tipo p-Laplaciano \'u IND. tt\' - \'DELTA\' IND.p u + \'(- \'DELTA\' POT. alpha\' u IND. t\' = \' [u] POT.q - 2 u, definida num domínio limitado limitado do \'R POT. n\', com 2 \' > ou = \' p < q e 0 < \' alpha\' < 1. Utilizando o método de Faedo-Galerkin provamos a existência de soluções fracas globais para dados iniciais pequenos. Para essas soluções estudamos também o decaimento polinomial da energia associada. A questão da não existência de soluções globais é considerada para o caso em que a energia inicial do sistema é negativa / In this work we study the p-Laplacian wave equation \'u IND. tt\' - \' DELTA\' IND p u + \'(- \'DELTA\' POT. \'alpha\' \' u IND. t\' = \'[u] POT. q - 2 u, defined in a bounded domain of \'R POT n\', with 2 \'> or =\' p < q and 0 < \' alpha\' < 1. By using the Faedo-Galerkin method we prove the existence of weak global solutions for small initial data. We also study the polynomial decay of the associate energy. The blow-up of solutions in finite time is considered for negative initial energy

<p>Za potrebe evaluacije kvaliteta vizuelizacije u svrhu prakse inkluzivnog dizajna razvijen je namenski jezik koji hendikepe vida opisuje kroz formalizam baziran na vizuelnim promenljivama. Namenski jezik se može koristiti kao dokumentacija, ili kao specifikacija hendikepa za posebno napisan simulator vizuelnih hendikepa koji se može koristiti za potrebe testiranja. Kao pandan ovoj tehnologiji razvijen je novi mehanizam za evaluaciju vizuelizacija koji kroz inovacije u metodologiji i statistici omogućava da se merodavni zaključci izvedu iz relativno malih uzoraka podataka.</p> / <p>For purposes of visualization quality evaluation as part of the practice of<br />inclusive design, a domain specific language was developed such that it<br />describes visual handicaps through a formalism based on visual variables.<br />This domain specific language can be used as documentation or as a handicap<br />specification for a specially constructed visual handicap simulator usable for<br />testing. Alongside this technology, a novel mechanism for visualization<br />evalution was developed. This mechanism allows, through innovations in<br />metholodoly and statistics, the production of trustworthy results from relatively<br />small data sets.</p>

Neste trabalho estudamos a equação de ondas do tipo p-Laplaciano \'u IND. tt\' - \'DELTA\' IND.p u + \'(- \'DELTA\' POT. alpha\' u IND. t\' = \' [u] POT.q - 2 u, definida num domínio limitado limitado do \'R POT. n\', com 2 \' > ou = \' p < q e 0 < \' alpha\' < 1. Utilizando o método de Faedo-Galerkin provamos a existência de soluções fracas globais para dados iniciais pequenos. Para essas soluções estudamos também o decaimento polinomial da energia associada. A questão da não existência de soluções globais é considerada para o caso em que a energia inicial do sistema é negativa / In this work we study the p-Laplacian wave equation \'u IND. tt\' - \' DELTA\' IND p u + \'(- \'DELTA\' POT. \'alpha\' \' u IND. t\' = \'[u] POT. q - 2 u, defined in a bounded domain of \'R POT n\', with 2 \'> or =\' p < q and 0 < \' alpha\' < 1. By using the Faedo-Galerkin method we prove the existence of weak global solutions for small initial data. We also study the polynomial decay of the associate energy. The blow-up of solutions in finite time is considered for negative initial energy

In this thesis we study the global existence of small data solutions to the Cauchy problem for semilinear damped wave equations with an effective dissipation term, where the data are supposed to belong to different classes of regularity. We apply these results to the Cauchy problem for weakly coupled systems of semilinear effectively damped waves with respect to the defined classes of regularity for different power nonlinearities. We also presented blow-up results for semi-linear systems with weakly coupled damped waves.