Multi-Source Data Analysis Challenges

Uselton,; Ahrens,; Treinish,

doi:10.1109/visual.1998.745353

Cited by 9 publications

(4 citation statements)

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“…In what follows, we provide a brief introduction to some of the work relevant to our contribution. One aspect of heterogeneity that has been well studied before is the challenges related to analyzing and visualizing data from multiple sources [34]. Various visualization techniques have targeted such heterogeneity.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Heterogeneous Data Using Robust Similarity

Mirzargar¹,

Whitaker²,

Kirby³

2017

Preprint

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Heterogeneous data pose serious challenges to data analysis tasks, including exploration and visualization. Current techniques often utilize dimensionality reductions, aggregation, or conversion to numerical values to analyze heterogeneous data. However, the effectiveness of such techniques to find subtle structures such as the presence of multiple modes or detection of outliers is hindered by the challenge to find the proper subspaces or prior knowledge to reveal the structures. In this paper, we propose a generic similaritybased exploration technique that is applicable to a wide variety of datatypes and their combinations, including heterogeneous ensembles. The proposed concept of similarity has a close connection to statistical analysis and can be deployed for summarization, revealing fine structures such as the presence of multiple modes, and detection of anomalies or outliers. We then propose a visual encoding framework that enables the exploration of a heterogeneous dataset in different levels of detail and provides insightful information about both global and local structures. We demonstrate the utility of the proposed technique using various real datasets, including ensemble data.

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Section: Related Workmentioning

confidence: 99%

Exploration of Heterogeneous Data Using Robust Similarity

Mirzargar¹,

Whitaker²,

Kirby³

2017

Preprint

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“…Data stemming from different acquisition modalities are common in many physical sciences including climate research, meteorology, physics, and astronomy [138,238]. A simulation model can, for instance, be validated by comparing it to the output of another model or measurement data from weather stations or satellite observations.…”

Section: Visualization and Analysis Of Multi-modal Datamentioning

confidence: 99%

Hypothesis Generation in Climate Research with Interactive Visual Data Exploration

Kehrer

Ladstädter

Muigg

et al. 2008

IEEE Trans. Visual. Comput. Graphics

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One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology--in the context of a coordinated multiple views framework--allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.

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“…Many data are collected from peoples' daily life, including daily travel, weather, and industries, which contain lots of information [1][2][3]. Multi-source spatio-temporal information are the basic data sources for predicting urban population activity flow and urban transportation planning.…”

Section: Introductionmentioning

confidence: 99%

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

et al. 2020

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

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Multi-Source Data Analysis Challenges

Cited by 9 publications

References 0 publications

Exploration of Heterogeneous Data Using Robust Similarity

Exploration of Heterogeneous Data Using Robust Similarity

Hypothesis Generation in Climate Research with Interactive Visual Data Exploration

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

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