Dissertationen zum Thema „Online data visualization“

As a result of the rapid evolution of smart mobile devices and the wide application of satellite-based positioning devices, the moving object database (MOD) has become a hot research topic in recent years. The moving objects generate a large amount of geo-referenced data in different types, such as videos, audios, images and sensor logs. In order to better analyze and utilize the data, it is useful and necessary to visualize the data on a map. With the rise of web mapping, visualizing the moving object and geo-referenced data has never been so easy. While displaying the trajectory of a moving object is a mature technology, there is little research on visualizing both the location and data of the moving objects in a synchronized manner. This dissertation proposes a general moving object visualization model to address the above problem. This model divides the spatial data visualization systems into four categories. Another contribution of this dissertation is to provide a framework, which deals with all these visualization tasks with synchronization control in mind. This platform relies on the TerraFly web mapping system. To evaluate the universality and effectiveness of the proposed framework, this dissertation presents four visualization systems to deal with a variety of situations and different data types.

OpenDSA is a collection of open source tutorials for teaching data structures and algorithms. It was created with the goals of visualizing complex, abstract topics; increasing the amount of practice material available to students; and providing immediate feedback and incremental assessment. In this thesis, I first describe aspects of the OpenDSA architecture relevant to collecting user interaction data. I then present an analysis of the interaction log data gathered from three classes during Spring 2013. The analysis focuses on determining the time distribution of student activity, determining the time required for assignment completion, and exploring \credit-seeking" behaviors and behavior related to non-required exercises. We identified clusters of students based on when they completed exercises, verified the reliability of estimated time requirements for exercises, provided evidence that a majority of students do not read the text, discovered a measurement that could be used to identify exercises that require additional development, and found evidence that students complete exercises after obtaining credit. Furthermore, we determined that slideshow usage was fairly high (even when credit was not ordered), and skipping to the end of slideshows was more common when credit was offered but also occurred when it was not.
Master of Science

The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters, although this is challenging because: (1) it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams; (2) it is difficult to derive accurate relationships between interval based events, where multiple appliance usage persist; (3) continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. To overcome these challenges, we propose an unsupervised progressive incremental data mining technique using frequent pattern mining (appliance-appliance associations) and cluster analysis (appliance-time associations) coupled with a Bayesian network based prediction model. The proposed technique addresses the need to analyze temporal energy consumption patterns at the appliance level, which directly reflect consumers' behaviors and provide a basis for generalizing household energy models. Extensive experiments were performed on the model with real-world datasets and strong associations were discovered. The accuracy of the proposed model for predicting multiple appliances usage outperformed support vector machine during every stage while attaining accuracy of 81.65\%, 85.90\%, 89.58\% for 25\%, 50\% and 75\% of the training dataset size respectively. Moreover, accuracy results of 81.89\%, 75.88\%, 79.23\%, 74.74\%, and 72.81\% were obtained for short-term (hours), and long-term (day, week, month, and season) energy consumption forecasts, respectively.

[Truncated abstract] Many databases in scientific and business applications have grown exponentially in size in recent years. Accessing and using databases is no longer a specialized activity as more and more ordinary users without any specialized knowledge are trying to gain information from databases. Both expert and ordinary users face significant challenges in understanding the information stored in databases. The databases are so large in most cases that it is impossible to gain useful information by inspecting data tables, which are the most common form of storing data in relational databases. Visualization has emerged as one of the most important techniques for exploring data stored in large databases. Appropriate visualization techniques can reveal trends, correlations and associations in data that are very difficult to understand from a textual representation of the data. This thesis presents several new frameworks for data visualization and visual data mining. The first technique, VisEx, is useful for visual exploration of large multi-attribute datasets and especially for exploring the correlations among the attributes in such datasets. Most previous visualization techniques can display correlations among two or three attributes at a time without excessive screen clutter. ... Although many algorithms for mining association rules have been researched extensively, they do not incorporate users in the process and most of them generate a large number of association rules. It is quite often difficult for the user to analyze a large number of rules to identify a small subset of rules that is of importance to the user. In this thesis I present a framework for the user to interactively mine association rules visually. Another challenging task in data mining is to understand the correlations among the mined association rules. It is often difficult to identify a relevant subset of association rules from a large number of mined rules. A further contribution of this thesis is a simple framework in the VisAR system that allows the user to explore a large number of association rules visually. A variety of businesses have adopted new technologies for storing large amounts of data. Analysis of historical data quite often offers new insights into business processes that may increase productivity and profit. On-line analytical processing (OLAP) has become a powerful tool for business analysts to explore historical data. Effective visualization techniques are very important for supporting OLAP technology. A new technique for the visual exploration of OLAP data cubes is also presented in this thesis.

Work-related musculoskeletal disorders constitutes a substantial burden for society, generating individual suffering and financial costs. Quantifying the musculoskeletal stress and establishing exposure-response relationships is an important step in facing this problem. Observational methods for assessing exposure in the field of ergonomics have shown poor results, and the technical measurement methods that exists are often complicated to use which limits their scope to scientific purposes. This work describes the development of a prototype measurement system aimed to simplify ambulatory measurements of musculoskeletal load, specifically aimed at the wrist and hand. Wearable sensors including Inertial Measurement Units (IMU:s) and Electromyography (EMG) were connected to a smartphone and used for measuring wrist movement and forearm muscle activity. Data sampled in the smartphone was stored online in a cloud database, and a webapplication was developed to visualize work-load exposure. Testing under controlled conditions indicated that muscular rest can be measured and classified according to suggested risk thresholds. Accurate angular measurements were difficult to implement because of lacking inter-sensor alignment in the horizontal plane, as well as uncertainties in the Bluetooth protocol. Future work should focus on the IMU:s and look to further develop a method of correcting the relative angle error, as well as investigating accurate time synchronization of the two sensors.Alternatively, deriving angular velocities directly from the IMU gyroscopes could be investigated.

A new phenomenon on the Internet is the online audience response. Mentimeter is a web-based tool where you can create online polls. The users can then cast a vote and the result is shown in real time. We want to test the information visualization in Mentimeter and we base our hypothesis, that a better graph design is possible, on the work of Edward Tufte and Stephen Few. Edward Tufte is an emeritus at Yale University, where he held courses in statistical evidence and information design and has created theories about how visual information should be designed. Stephen Few is a known information designer and he has based a lot of his theories on the work of Tufte. We compare Tufte and Fews design principles against the Mentimeter tool with user testing. We can see that Tufte and Fews theories still stand strong when it comes to the comparisment with Mentimeter and that a redesign of their graphs could raise the quality and the user experience of the tool. But Mentimeter is also a tool that functions the way it is supposed to.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
A colaboração de usuários em sites jornalísticos é um fenômeno crescente. Cada vez mais, a evolução tecnológica abre espaço para uma maior participação dos usuários no processo de construção da narrativa noticiosa. Nesse contexto, um olhar do design sobre os modelos colaborativos dos sites jornalísticos fornece subsídios para o entendimento deste fenômeno e para o aprofundamento em cada uma das etapas que compõe o processo colaborativo. Dessa forma, essa dissertação apresenta a análise teórica e prática dessas diferentes etapas, bem como das soluções de design aplicáveis aos modelos colaborativos, de maneira a estabelecer conceitos e diretrizes para a construção de modelos que otimizem o aproveitamento do conteúdo enviado por usuários e sua relação com o conteúdo editorial dos sites noticiosos.
The collaboration on news sites is a growing phenomenon. Increasingly, technological change makes room for greater involvement of users in the process of building the news narrative. In this context, a design look on the models of collaborative news sites provide insights for understanding this phenomenon and to deepen in each of the steps that make up the collaborative process. Thus, this dissertation presents the theoretical analysis and practice of these different stages, as well as design solutions applicable to collaborative models in order to establish concepts and guidelines for the construction of models that optimize the use of the content posted by users and its relationship with the editorial content of news sites.

Digital technology is developing rapidly, and educators are currently in the process of exploring what opportunities new technology can have for learning and teaching. This applies not least to the field of education in programming, as   learning to program is a great challenge for many students. Therefore, educators and researchers have sought to find educationally effective digital learning resources to facilitate the programming learning process. In this thesis, multimodal online learning environments, providing accessible and learner-focused learning activities, and multimodal software visualizations, striving at concretizing the abstract building blocks in programming languages, were exploratory investigated. The aim was to expand the understanding of how digital learning resources can be used to support students’ understanding of basic programming concepts and dynamics. Empirical studies were conducted at the Department of Computer and Systems Sciences (DSV) at Stockholm University, Sweden. The data were collected during introductory programming courses in which the author had an active role as designer of the software visualizations, teacher and researcher. In these courses, the digital learning resources were explored as additional course resources. The research was conducted both through field experiments and case studies and data was collected using questionnaires, interviews and group discussions. Results strengthened the claims that abstract phenomena like programming can be made visible and understandable through visualization, in particular program dynamics.  When visualizations illustrated more conceptual elements, further guidance was required, to facilitate students’ interpretation and understanding. Regarding the online learning environments, guiding to the right learning pathways was the most critical factor found from the results. When adequate instructions and feedback were lacking in the learning environments, users became cognitively overloaded and disoriented. Finally, findings in all compiled studies indicate that teacher planning and orchestration of the learning situations should be conducted with more reflection.

This thesis project presents a research through design process, that has aimed to investigate and challenge internet users’ perception and awareness around the theme of online privacy and third-party trackers. This has been done by designing a critical design artifact called PrivacyLamp which takes form as a classic lamp, that through a secondary (dis)functionality is designed to work as an mediation of potential third-par- ty-trackers activity on the user’s local network. PrivacyLamp has been developed through an iterative design process, guid- ed by relevant literature and works within the eld of critical design, physical data visualization, and design for re ection, which all have worked as a foundation for the design of such an artefact. The prototype has been evaluated together with six participants, who all adopted the prototype into their domestic settings to experience it as a part of their everyday life for a few days. The aim of this qualitative study has been to investigate how a defamiliarized domestic object can work as an ambient display to question the invisible ow of connec- tivity and its complication within online privacy, as well as the narratives and experiences users develops in relation to this.

The diploma thesis deals with automated measurement of humidity and CO2 concentration inside buildings. Results of this measurement form the input parameters for the effective management of technologies reducing the energy performance of buildings. In the introduction, the issue of indoor air quality of buildings and indicators characterizing this quality are approached. The technical part of the thesis consists of making a measuring device which contains two prototype sensors provided by Teco Inc. with online access to measured data. The measurement of relative humidity, CO2 concentration and temperature in the interior of the building with the made device is compared with commercially available devices for measuring selected parameters. For unambiguous interpretation of online data, the virtual machine with an online database is configured for the created measuring device. The possibilities of using the prepared measuring device to achieve a reduction in the energy performance of buildings are discussed in the final part.

Data Structures and Algorithms (DSA) courses are critical for any computer science curriculum. DSA courses emphasize concepts related to procedural dynamics and Algorithm Analysis (AA). These concepts are hard for students to grasp when conveyed using traditional textbook material relying on text and static images. Algorithm Visualizations (AVs) emerged as a technique for conveying DSA concepts using interactive visual representations. Historically, AVs have dealt with portraying algorithm dynamics, and the AV developer community has decades of successful experience with this. But there exist few visualizations to present algorithm analysis concepts. This content is typically still conveyed using text and static images. We have devised an approach that we term Algorithm Analysis Visualizations (AAVs), capable of conveying AA concepts visually. In AAVs, analysis is presented as a series of slides where each statement of the explanation is connected to visuals that support the sentence. We developed a pool of AAVs targeting the basic concepts of AA. We also developed AAVs for basic sorting algorithms, providing a concrete depiction about how the running time analysis of these algorithms can be calculated. To evaluate AAVs, we conducted a quasi-experiment across two offerings of CS3114 at Virginia Tech. By analyzing OpenDSA student interaction logs, we found that intervention group students spent significantly more time viewing the material as compared to control group students who used traditional textual content. Intervention group students gave positive feedback regarding the usefulness of AAVs to help them understand the AA concepts presented in the course. In addition, intervention group students demonstrated better performance than control group students on the AA part of the final exam. The final exam taken by both the control and intervention groups was based on a pilot version of the Algorithm Analysis Concept Inventory (AACI) that was developed to target fundamental AA concepts and probe students' misconceptions about these concepts. The pilot AACI was developed using a Delphi process involving a group of DSA instructors, and was shown to be a valid and reliable instrument to gauge students' understanding of the basic AA topics.
Ph. D.

This thesis examines the usage of and attitudes toward data visualization and analytic tools in three genres of online games. Using an online survey, this research analyzes responses from participants regarding their play habits and attitudes online. Several scales are generated identifying different player demographics such as emotional attitudes, competitive attitudes, technological attitudes, spectator involvement, and overall attitudes toward information customization. In addition, several genre specific scales are created for massive multiplayer online games (MMO), real time strategy (RTS) and first person shooting (FPS) games. This research concludes that competitive attitudes are moderately correlated with information customization and implementation of data visualization tools. Additionally, the relationship between the usage of data visualization tools are strongest with the MMO genre compared to the RTS or FPS genres. In addition, my research shows a strong preference between the responses for the usage of data visualization tools amongst those who report higher levels of spectator involvement with online games. Finally, my research concludes that there is a strong relationship between the amount of time players spend playing online games and the attitudes toward and usage of data visualization tools.
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Sharing sensor data between multiple devices and users can be^challenging for naive users, and requires knowledge of programming and use of different communication channels and/or development tools, leading to non uniform solutions. This thesis proposes a system that allows users to access sensors, share sensor data and manage sensors. With this system we intent to manage devices, share sensor data, compare sensor data, and set policies to act based on rules. This thesis presents the design and implementation of the system, as well as three case studies of its use.
Universidade da Madeira

Um dos claros benefícios da visualização da informação é poder ajudar as pessoas a ver tendências e anomalias em dados, que podem ser particularmente valiosas em tempo real. Técnicas visuais, como os mapas de calor e grafos, ajudam a revelar padrões contidos em dados homogéneos. Estas técnicas eram “virtualmente” desconhecidas há 10 anos, mas nos dias de hoje têm uma grande aplicação prática, pois auxiliam significativamente na visualização de dados disponíveis em sítios públicos na Web a aplicações avançadas para suporte a negociação. Os sítios web que disponibilizam comentários sobre um produto ou um serviço têm atraído a atenção de milhões de pessoas por todo o mundo. O TripAdvisor é disto um exemplo, recebe mais de 60 milhões de visitantes únicos mensais e mais de 75 milhões de comentários e opiniões (TripAdvisor, 2012). Além disso, os comentários on-line estão disponíveis numa variedade de domínios, como por exemplo na restauração (por exemplo zagat.com e opentable.com), na comercialização de vinhos (adegga.com) e cruzeiros (cruisecritic.com). Apesar de oferecerem alguma visualização, sobre a informação que disponibilizam, este aspeto necessita de ser melhor explorado. Algumas das questões em aberto são: Como representar visualmente opiniões online para apoiar uma melhor tomada de decisão? Quais são as técnicas de visualização de informação mais adequadas para representar o conhecimento nas avaliações online? Que tipos de gráficos devem ser utilizados para representar o conhecimento (informação integrada) em comentários online? Se considerarmos que os potenciais utilizadores finais podem ser não só o público em geral, mas também gestores de negócios, haverá alguma diferença cognitiva visual que justifique a utilização de técnicas visuais diferentes? Em última análise, a própria variedade do perfil de amostra de utilizadores (idade, género, nacionalidade, etc.) poderá implicar nessa diferença? O cenário apresentado, onde foi identificada a necessidade de melhorar a visualização dos dados resultantes das opiniões online disponíveis nos websites, é a motivação para a proposta deste trabalho. De uma forma mais abrangente, pretende-se conhecer melhor o modelo cognitivo visual dos potenciais utilizadores desses websites e propor uma ou mais novas formas de representação visual que permitam o mapeamento visual desses dados e uma mais fácil análise. De uma forma mais específica, este trabalho pretende focar o domínio das opiniões online do sector hoteleiro, tendo especial atenção no enfoque diferenciado a ser considerado para uma visão de gestor e de público em geral sobre a informação.
One of the most obvious benefits of visualization is helping people see trends and anomalies in data, which can be particularly valuable in real-time environments. Visual techniques such as heat maps and tree maps, which help reveal patterns in homogenous data, were virtually unknown 10 years ago, but are used today in many places ranging from public Web sites to advanced trading applications. Online reviews, e.g. review about a product or a service, is pervasive on the web and the richness of this data have attracted attention from millions of people around the world. For example, TripAdvisor receives more than 60 million unique monthly visitors, and over 75 million reviews and opinions (TripAdvisor, 2012) - and the pace is accelerating. Moreover, online reviews are available in a range of domains such as restaurants (e.g. zagat.com and opentable.com), wines (e.g. adegga.com) and cruisers (e.g. cruisecritic.com) which needs to be better explored, mainly in the visualization aspect. Some of the open questions are: How to visually represent online reviews to support better decision making? Which are the more adequate/suitable information visualization techniques to represent the knowledge in online reviews? What kinds of graphics should be used to represent the knowledge (integrated information) in online reviews? Are both textual and graphical visualizations useful for customers and managers? The goal of this work is to have a better knowledge of the multilingual online business reviewers’ visual and cognitive model. The domain of the reviews will be the hotel sector.

Geo-based Information Visualizations (GIV) allow people to analyze data points based on their related geographic locations. This approach is usually adopted where a large scale geo-referenced dataset is present and users are trying to find a way to examine hidden patterns within this data. One of the emerging trends in GIV is to visualize social media data to show how information flows between users of popular social networking sites. Due to its public nature and the large number of users, most of the visualizations in this area rely on conversational data from Twitter (Twitter.com). In this thesis, we design and implement a web-based interactive GIV system, AcademiaMap-GIV, to visualize online conversations among scholars on Twitter. A formal exploratory user study was also conducted on the target users. The study results demonstrated that most of the study respondents found the features of AcademiaMap-GIV effective in regards to visualizing information of their interests.

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like cyclone tracking and earthquake modeling require high-fidelity and high- performance simulations involving large-scale computations and generate huge amounts of data. Faster simulations and simultaneous online data analysis and visualization enable scientists provide real-time guidance to policy makers. In this thesis, we present a set of techniques for efficient high-fidelity simulations, online data analysis and visualization in environments with varying resource configurations. First, we present a strategy for improving throughput of weather simulations with multiple regions of interest. We propose parallel execution of these nested simulations based on partitioning the 2D process grid into disjoint rectangular regions associated with each subdomain. The process grid partitioning is obtained from a Huffman tree which is constructed from the relative execution times of the subdomains. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. We observe up to 33% gain over the default strategy in weather models. Second, we propose a processor reallocation heuristic that minimizes data redistribution cost while reallocating processors in the case of dynamic regions of interest. This algorithm is based on hierarchical diffusion approach that uses a novel tree reorganization strategy. We have also developed a parallel data analysis algorithm to detect regions of interest within a domain. This helps improve performance of detailed simulations of multiple weather phenomena like depressions and clouds, thereby in- creasing the lead time to severe weather phenomena like tornadoes and storm surges. Our method is able to reduce the redistribution time by 25% over a simple partition from scratch method. We also show that it is important to consider resource constraints like I/O bandwidth, disk space and network bandwidth for continuous simulation and smooth visualization. High simulation rates on modern-day processors combined with high I/O bandwidth can lead to rapid accumulation of data at the simulation site and eventual stalling of simulations. We show that formulating the problem as an optimization problem can deter- mine optimal execution parameters for enabling smooth simulation and visualization. This approach proves beneficial for resource-constrained environments, whereas a naive greedy strategy leads to stalling and disk overflow. Our optimization method provides about 30% higher simulation rate and consumes about 25-50% lesser storage space than a naive greedy approach. We have then developed an integrated adaptive steering framework, InSt, that analyzes the combined e ect of user-driven steering with automatic tuning of application parameters based on resource constraints and the criticality needs of the application to determine the final parameters for the simulations. It is important to allow the climate scientists to steer the ongoing simulation, specially in the case of critical applications. InSt takes into account both the steering inputs of the scientists and the criticality needs of the application. Finally, we have developed algorithms to minimize the lag between the time when the simulation produces an output frame and the time when the frame is visualized. It is important to reduce the lag so that the scientists can get on-the- y view of the simulation, and concurrently visualize important events in the simulation. We present most-recent, auto-clustering and adaptive algorithms for reducing lag. The lag-reduction algorithms adapt to the available resource parameters and the number of pending frames to be sent to the visualization site by transferring a representative subset of frames. Our adaptive algorithm reduces lag by 72% and provides 37% larger representativeness than the most-recent for slow networks.

碩士
國立交通大學
教育研究所
105
The purpose of this study was to explore how personalized learning integrated with data visualization and self-regulatory strategies influences online learner’s self-regulation and sequential behavioral patterns. Participants were 182 graduate students, who were randomly assigned into four groups: control group, self-regulation group, data visualization group, and self-regulation with data visualization group. The instruments include the Online Self-regulated Learning Questionnaire and prior-knowledge test; additionally, the behavioral logs were recorded to explore learner’s sequential behavioral patterns. The experimental procedure is as follows. First, the Online Self-regulated Learning Questionnaire and prior-knowledge test were administered. Second, learners engaged in the personalized learning system where they perused the learning materials about research ethics education and completed the tests. Finally, they completed the post-test of the Online Self-regulated Learning Questionnaire. The results demonstrated that the use of data visualization function improves goal setting and help-seeking dimensions of self-regulation. In addition, self-regulation integrated with data visualization improved learner’s self-evaluation, so that after witnessing their performance, learners perused the learning materials again, and reviewed learning materials after receiving their test scores. Therefore, self-regulation integrated with data visualization effectively enhanced students’ self-regulation. This study suggests that course developers should incorporate data visualization functions into the designs of online learning courses. Furthermore, strategies of self-regulation (e.g., goal setting, time management) should be provided so as to guide data visualization and improve students’ self-learning. The findings of the study can serve as the references for online course designers and educators.