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Concepts of Visual and Interactive Clustering
Published in Charu C. Aggarwal, Chandan K. Reddy, Data Clustering, 2018
An alternative metaphor is the ThemeRiver [21]. It shows the relative sizes of a series of clusters with the same label as stacked layers of different widths. See Figure 19.8 for an example. The TIARA System [48] uses this visualization technique to visualize dynamic documents . The clusters/topics are indicated by different colors. In contrast to Topic Table, color here has the function to discriminate between distinct clusters/topics. The top words of the clusters/topics are inserted into the layers at time points when a cluster/topic is strongly present, and therefore, the respective time series is represented by a wide layer. The user can interactively explore the visualization. Less dominant clusters/topics that have no space to insert top-words could be zoomed and annotated with words after a certain minimum width is reached. Search functionality is included to easily retrieve documents related to clusters/topics.
Data Modeling for Systems Integration
Published in Adedeji B. Badiru, Systems Engineering Using the DEJI Systems Model®, 2023
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, technology can be used to drill down into charts and graphs for more detail, interactively changing what data is seen and how it’s processed. With big data there’s potential for great opportunity, but many retail banks are challenged when it comes to finding value in their big data investment. For example, how can they use big data to improve customer relationships? How—and to what extent—should they invest in big data?
Business modeling and forecasting
Published in Adedeji B. Badiru, Ibidapo-Obe Oye, Babatunde J. Ayeni, Manufacturing and Enterprise, 2018
Adedeji B. Badiru, Ibidapo-Obe Oye, Babatunde J. Ayeni
Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, technology can be used to drill down into charts and graphs for more detail, interactively changing what data is seen and how it’s processed. With Big Data there’s potential for great opportunity, but many retail banks are challenged when it comes to finding value in their Big Data investment. For example, how can they use Big Data to improve customer relationships? How – and to what extent – should they invest in Big Data ?
Enhancing the order-to-delivery process with real-time performance measurement based on digital visualization
Published in Production & Manufacturing Research, 2023
Mira Holopainen, Minna Saunila, Juhani Ukko
Though visualization is not a new concept, technological development has made its implementation more efficient – for example, real-time monitoring can be done via smartphones through graphical dashboards (Zhong et al., 2017). Visualization refers to the presentation of information, data, and knowledge in a graphical form that facilitates the provision of insights, the recreation of lively images, the evolution of comprehension, and the transmission of experiences (Lengler & Eppler, 2007). It also refers to a visible expression that describes a group rather than an individual. (Greif, 1991). Visualization’s benefits have been studied by many researchers (e.g. Eppler & Platts, 2009). As part of the management activities of companies, visualization supports decision-making (Al-Kassab et al., 2014), promotes communication (Bititci et al., 2016; Eaidgah et al., 2016; Larsson et al., 2017), enhances information flow (Eaidgah et al., 2016), and supports continuous improvement (Bititci et al., 2016; Eaidgah et al., 2016; van Assen & de Mast, 2019).
Using Chained Views and Follow-Up Queries to Assist the Visual Exploration of the Web of Big Linked Data
Published in International Journal of Human–Computer Interaction, 2022
Aline Menin, Minh Nhat Do, Carla Dal Sasso Freitas, Olivier Corby, Catherine Faron, Alain Giboin, Marco Winckler
Information visualization techniques are useful to discover patterns and causal relationships within LOD datasets (Bikakis, 2019). However, since the discovery process is often exploratory [i.e., users have no predefined goal and do not expect a particular outcome (Leng, 2011)] when users find something interesting, they should be able to (i) retrace their exploratory path to explain how results are found, and (ii) branch out the exploratory path to compare data observed in different views or found in different datasets. Furthermore, as most of LOD datasets are very specialized, users often need to explore multiple datasets to obtain the knowledge required to support decision-making processes. Thus, the design of visualization tools is confronted with two main challenges: the visualization system should provide multiple views to enable the exploration of different or complementary perspectives to the data; and the system should support the combination of diverse data sources during the exploration process.
A systematic meta-Review and analysis of learning analytics research
Published in Behaviour & Information Technology, 2021
Xu Du, Juan Yang, Brett E. Shelton, Jui-Long Hung, Mingyan Zhang
Schwendimann et al. (2017) examined educational dashboards in order to categorise learning contexts, data sources, visualisations and analysis types in both LA and EDM by reviewing 55 journal papers between 2010–2015. The authors found that most of the learning dashboards were designed for students’ self-monitoring and for instructors to monitor students in formal higher education settings. The data sources of dashboards were heavily relied on behaviour logs from a single LMS platform. The visualisation types, which were similar to that of traditional dashboards, utilised bar chart, line graph, table, pie chart, and network graph. In terms of analysis type, the authors revealed that most of studies in their review were exploratory or proof-of-concept without authentic evaluations, therefore, it was difficult to evaluate the actual impacts of learning dashboard on learning effects.