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Effect of color properties in multiple time series graph comprehension

APPLIED ERGONOMICS 2022年 103卷 103808页

作者： Seva, Rosemary R. Wu, Judy Ann G. Chinjen, Katrina K. Estoista, Nicole Anne Therese P. Salle Univ Ind & Syst Engn Dept Manila Philippines

Multiple time series graphs are used prevalently in representing business and research data, but the use of color properties to visualize them to enhance comprehension is limited. This study explored the effect of hue and lightness in representing 4-time series data in relation to response time (RT) and accuracy. Two types of palettes were developed for each experiment: monochrome and multi-hue. The three sets of monochrome palettes created were red, green, and blue, while four equidistant hues in the color wheel were used in the multi-hue palette: red, blue, green, and purple. A total of forty people participated in the two experiments. Participants performed two tasks for both experiments: maximum and discrimination tasks. The monochrome experiment showed the primacy of green in terms of RT and accuracy in the discrimination task. RT and accuracy were significantly affected by lightness in the multi-hue experiment. For both tasks, RT was longer for 20% lightness and lowest at 60% lightness. Accuracy results were also consistent with RT. In the discrimination task, participants made more errors in 20% lightness and the highest accuracy for 60% and 80%.

关键词： Color data and knowledge visualization Graphics recognition and interpretation Time series analysis

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visualization of Real-Time Heterogeneous Smart City data Using Virtual Reality 5

Visualization of Real-Time Heterogeneous Smart City Data Usi...

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5th IEEE Annual International Smart Cities Conference (ISC2)

作者： Vanden Broucke, Steven Deligiannis, Nikos Vrije Univ Brussel Pl Laan 2 B-1050 Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9781728108469

As the size and dimensionality of big heterogeneous data increases, finding patterns and anomalies with existing visualization methods and tools poses a significant challenge. The majority of open data platforms that offer smart city data visualizations use browser-based two-dimensional (2D) visualizations as 2D displays are widely adopted. These displays are however ineffective in depicting multi-dimensional heterogeneous data. The recent growth in the virtual reality (VR) consumer market resulted in an affordable alternative for 3D visualizations. In this paper, we propose a VR system capable of visualizing real-time smart city data concerning the city of Brussels. A subset of external data sources that is already visualized in existing web platforms is incorporated in the VR application. A user study is conducted to assess perceived workloads and data immersion parameters for a set of data exploration tasks in three existing web platforms and in the proposed VR system. Results indicate significantly lower levels of perceived frustration and significantly higher levels of data intuitivity, immersion, overview in data, and intuitive interaction. However, no significant difference in total perceived workload is observed.

关键词： data and knowledge visualization visualization Applications Big data Human-computer interaction Artificial augmented, and virtual realities

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knowledge discovery and visualisation framework using machine learning for music information retrieval from broadcast radio data

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115236-115236页

作者： Furner, Michael Islam, Md Zahidul Li, Chang-Tsun Charles Sturt Univ Sch Comp & Math Panorama Ave Bathurst NSW 2795 Australia Deakin Univ Fac Sci Engn & Built Environm Geelong Vic Australia

Music radio data is currently underutilised in radio program management. Software tools that listen to and analyse music airplay are in many markets nonexistent, limited, or unaffordable. In this paper we present a novel knowledge discovery and visualisation framework for broadcast radio, ZeitMetric. The ZeitMetric framework uses machine learning and music information retrieval techniques to label radio audio automatically for knowledge discovery. The framework incorporates a novel music dataset collection technique (MusiGrab) to leverage online music services for ground-truth data, as well as a novel knowledge visualisation and presentation technique based on self-organizing maps (ZeitViz). The framework is compared to what little literature relating to this topic exists, and a set of requirements for a high-quality broadcast radio knowledge discovery is developed. MusiGrab specifically is compared to an existing static music information retrieval dataset and shown to offer superior results in this context. Future research directions using and extending the framework are also discussed. On acceptance of the paper, code for a use-case of the MusiGrab dataset collection technique will be released on GitHub.

关键词： data mining Machine learning Sound and music computing Signal processing systems Software Architectures data and knowledge visualization Record classification

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StreamStory: Exploring Multivariate Time Series on Multiple Scales

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2019年第4期25卷 1788-1802页

作者： Stopar, Luka Skraba, Primoz Grobelnik, Marko Mladenic, Dunja Jozef Stefan Inst Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia Jozef Stefan Inst Ljubljana 1000 Slovenia

This paper presents an approach for the interactive visualization, exploration and interpretation of large multivariate time series. Interesting patterns in such datasets usually appear as periodic or recurrent behavior often caused by the interaction between variables. To identify such patterns, we summarize the data as conceptual states, modeling temporal dynamics as transitions between the states. This representation can visualize large datasets with potentially billions of examples. We extend the representation to multiple spatial granularities allowing the user to find patterns on multiple scales. The result is an interactive web-based tool called StreamStory. StreamStory couples the abstraction with several tools that map the abstractions back to domain-specific concepts using techniques from statistics and machine learning. It is aimed at users who are not experts in data analytics, minimizing the number of parameters to configure out-of-the-box. We use three real-world datasets to demonstrate how StreamStory can be used to perform three main visual analytics tasks: identify the main states of a complex system and map them back to data-specific concepts, find high-level and long-term periodic behavior and traverse the scales to identify which scales exhibit interesting phenomena. We find and interpret several known, as well as previously unknown patterns in these datasets.

关键词： Time series analysis visualization systems and software data and knowledge visualization markov processes multivariate visualization data mining

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An ontology-based approach to knowledge representation for Computer-Aided Control System Design

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data & knowledge ENGINEERING 2018年 118卷 107-125页

作者： Benavides, Carmen Garcia, Isaias Alaiz, Hector Quesada, Luis Univ Leon Escuela Ingn Dept Elect & Syst Engn Campus Vegazana E-24071 Leon Spain Univ Coll Cork Insight Ctr Data Analyt Cork Ireland

Different approaches have been used in order to represent and build control engineering concepts for the computer. Software applications for these fields are becoming more and more demanding each day, and new representation schemas are continuously being developed. This paper describes a study of the use of knowledge models represented in ontologies for building Computer Aided Control Systems Design (CACSD) tools. The use of this approach allows the construction of formal conceptual structures that can be stated independently of any software application and be used in many different ones. In order to show the advantages of this approach, an ontology and an application have been built for the domain of design of lead/lag controllers with the root locus method, presenting the results and benefits found.

关键词： Conceptual modeling data and knowledge visualization Ontologies Computer-Aided Control System Design

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Mapping and Visualizing Deep-Learning Urban Beautification

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2018年第5期38卷 70-83页

作者： Kauer, Tobias Joglekar, Sagar Redi, Miriam Aiello, Luca Maria Quercia, Daniele Univ Appl Sci Potsdam Potsdam Germany Kings Coll London Dept Informat London England Nokia Bell Labs Cambridge Cambridge England Nokia Bell Labs Cambridge Social Dynam Cambridge England

Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (***/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience.

关键词： computer graphics data and knowledge visualization deep learning information visualization urban informatics

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Cardinality constraints and functional dependencies over possibilistic data

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data & knowledge ENGINEERING 2018年 117卷 339-358页

作者： Roblot, Tania Link, Sebastian Univ Auckland Dept Comp Sci Auckland New Zealand

Modern applications require advanced techniques and tools to process large volumes of uncertain data. For that purpose we study cardinality constraints and functional dependencies as a declarative mechanism to control the occurrences and interrelationships of uncertain data. Uncertainty is modeled qualitatively by assigning to each object a degree of possibility by which the object occurs in an uncertain instance. Cardinality constraints and functional dependencies are assigned a degree of certainty that stipulates on which objects they hold. Our framework empowers users to model uncertainty in an intuitive way, without the requirement to put a precise value on it. Our class of cardinality constraints and functional dependencies enjoys a natural possible world semantics, which is exploited to establish several tools to reason about them. We characterize the associated implication problem axiomatically and algorithmically in linear input time. Furthermore, we show how to visualize any given set of our cardinality constraints and functional dependencies in the form of an Armstrong sketch. Even though the problem of finding an Armstrong sketch is precisely exponential, our algorithm computes a sketch with conservative use of time and space. data engineers may therefore compute Armstrong sketches that they can jointly inspect with domain experts in order to consolidate the set of cardinality constraints and functional dependencies meaningful for a given application domain.

关键词： data and knowledge visualization data models database semantics Management of integrity constraints Requirements engineering

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Review of Research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2017年第4期10卷 405-418页

作者： Bodily, Robert Verbert, Katrien Brigham Young Univ Instruct Psychol & Technol Dept 150 MCKB Provo UT 84604 USA Katholieke Univ Leuven Dept Comp Sci Human & Comp Interact Grp Celestijnenlaan 200A B-3001 Heverlee Belgium

This article is a comprehensive literature review of student-facing learning analytics reporting systems that track learning analytics data and report it directly to students. This literature review builds on four previously conducted literature reviews in similar domains. Out of the 945 articles retrieved from databases and journals, 93 articles were included in the analysis. Articles were coded based on the following five categories: functionality, data sources, design analysis, student perceptions, and measured effects. Based on this review, we need research on learning analytics reporting systems that targets the design and development process of reporting systems, not only the final products. This design and development process includes needs analyses, visual design analyses, information selection justifications, and student perception surveys. In addition, experiments to determine the effect of these systems on student behavior, achievement, and skills are needed to add to the small existing body of evidence. Furthermore, experimental studies should include usability tests and methodologies to examine student use of these systems, as these factors may affect experimental findings. Finally, observational study methods, such as propensity score matching, should be used to increase student access to these systems but still rigorously measure experimental effects.

关键词： data mining data and knowledge visualization self-assessment technologies homework support systems adaptive and intelligent educational systems literature review

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D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第2期14卷 484-490页

作者： Athanasiadis, Emmanouil Bourdakou, Marilena Spyrou, George Acad Athens Syst Biol Ctr Biomed Res Fdn 4 Soranou Ephessiou Athens 11527 Greece Univ Athens Dept Informat & Telecommun Ilissia 15784 Greece

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases.

关键词： data and knowledge visualization graphs and networks inference networks random walks

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A natural language interface to a graph-based bibliographic information retrieval system

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data & knowledge ENGINEERING 2017年 111卷 73-89页

作者： Zhu, Yongjun Yan, Erjia Song, Il-Yeol Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities.

关键词： Information retrieval Natural language interface Graph database data and knowledge visualization Digital libraries

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