A Heatmap-Based Time-Varying Multi-variate Data Visualization Unifying Numeric and Categorical Variables

Suematsu, Haruka; Yagi, Sayaka; Itoh, Takayuki; Motohashi, Yosuke; Aoki, Kenji; Morinaga, Soichiro

doi:10.1109/iv.2014.25

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Examples of cluster heatmaps can be found in biology (e.g., Perez‐Llamas & Lopez‐Bigas, ), social sciences (e.g., Breuer, Schlegel, Kauf, & Rupf, ), and many other fields. Heatmaps without clustering are applied in various areas of research, recently being very useful in marketing (e.g., Suematsu et al, ).…”

Section: Methodsmentioning

confidence: 99%

Identifying Crisis Characteristics: Cross‐Case Relevant Crisis Character Variables for Public Administrations

Kalbassi¹,

Kauf²

2017

Risk Hazard & Crisis Pub Pol

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In the crisis management literature, only few approaches exist that focus on cross‐case relevant crisis character variables for public administrations. In order to overcome this shortage, this paper identifies and operationalizes cross‐case relevant crisis character variables derived from crisis management literature. A quantitative analysis (clustering) of past crises in Switzerland makes the relevance of the identified variables for the characterization of crises plausible. With the determination of a range of cross‐case relevant crisis character variables and the analysis of a number of crises by quantitative methods, we chose rather unfamiliar approaches in crisis management research. Therefore, the presented research aims to stimulate a discussion among crisis management scholars about both relevant variables and suitable methods for the identification of crisis characteristics.

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Section: Methodsmentioning

confidence: 99%

Identifying Crisis Characteristics: Cross‐Case Relevant Crisis Character Variables for Public Administrations

Kalbassi¹,

Kauf²

2017

Risk Hazard & Crisis Pub Pol

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“…Regarding the representation of nominal variables, they only contain ordinal, selective, or associative properties such that they can be represented with visual variables that do not enhance quantitative aspect, mainly color hue or shape [30,42]. Many 2D charts rely on these visual variables to represent categorical variables [39].…”

Section: Visualization For Multivariate Datamentioning

confidence: 99%

Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes

Cantu

Micó-Amigo

Din

et al. 2023

2023 IEEE 16th Pacific Visualization Symposium (PacificVis)

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“…The preprocessed trace output in Step 2 is used to produce a heatmap structure in Step 3 . The heatmap is a compact two-dimensional graphical representation of measured values of numerical data using a chosen color scheme, with one end of the color scheme representing the high values and the other end representing the low values [19]. The variation in color may be by hue or intensity, giving visual insights to the reader about how a phenomenon is clustered or varies over space and time.…”

Section: 21mentioning

confidence: 99%

A trace-driven methodology to evaluate and optimize memory management services of distributed operating systems for lightweight manycores

Podestá

Penna

Uller

et al. 2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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Lightweight manycores belong to a new class of emerging lowpower processors for the Exascale era. These processors present several challenges for the development of applications, such as distributed memory architecture, limited amount of on-chip memory and no cache coherence. Recently, distributed Operating Systems (OSs) have been proposed to address these challenges in a transparent way. In these systems, different OS services are deployed across the processor cores, being the memory management service one of the most important ones. However, the intrinsic characteristics and memory limitations of lightweight manycores bring several challenges to the design, implementation and future optimizations of memory management services. In this work, we propose a tracedriven methodology to evaluate and optimize features of a memory management service of distributed OSs for lightweight manycores. By using a compact representation of the page access pattern of the applications, our methodology is capable of mimicking the memory access pattern of the original applications on the target distributed OS running on a lightweight manycore. We integrated our methodology in a distributed OS (Nanvix) and validated it using three applications from a specific benchmark for lightweight manycores (CAP Bench). Then, we applied our methodology to carry out a case study using a software-managed cache implementation available in Nanvix. Our methodology enabled us to evaluate different page replacement policies on Kalray MPPA-256, even without the required support from the architecture to implement them. CCS CONCEPTS• Computer systems organization → System on a chip; Very long instruction word; Multiple instruction, multiple data; Parallel architectures;

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A Heatmap-Based Time-Varying Multi-variate Data Visualization Unifying Numeric and Categorical Variables

Cited by 4 publications

References 11 publications

Identifying Crisis Characteristics: Cross‐Case Relevant Crisis Character Variables for Public Administrations

Identifying Crisis Characteristics: Cross‐Case Relevant Crisis Character Variables for Public Administrations

Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes

A trace-driven methodology to evaluate and optimize memory management services of distributed operating systems for lightweight manycores

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