Visualization System Requirements for Data Processing Pipeline Design and Optimization

Landesberger, Tatiana von; Fellner, Dieter W.; Ruddle, Roy A.

doi:10.1109/tvcg.2016.2603178

Cited by 13 publications

(11 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The incorporation, characterization, and evaluation of uncertainty in visualization methodlogies and applications [BHJ∗14, Mac15, SSK∗16, BPHE17, HQC∗18] is a widely accepted subject to research. However, the analysis of uncertainty produced by processing algorithms along a pipeline is considered an open challenge [vLFR17]. Methodlogies for multiple types of uncertainties for processing steps exist [WYM12], just like uncertainty visualization approaches for PP and quality assessment [BBB∗18, BBGM17, CCM09].…”

Section: Related Workmentioning

confidence: 99%

“…At a glance, we support analysts in the creation of pipelines, as well as in the interactive analysis of the output [vLFR17]. With the order of tasks, we adhere to the natural way of creating pipelines: from single routines to complex pipelines.…”

Section: Approachmentioning

confidence: 99%

“…Requirements met by existing Toolkits We adopt important basic requirements from previous conceptual works and surveys [SHB∗14, vLFR17, BSS∗18]. Toolkits should allow analysts to combine different processing steps, build branches of data flows, and steer parameter values in order to explore the underlying data set and processing options.…”

Section: Approachmentioning

confidence: 99%

See 2 more Smart Citations

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre‐processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre‐processing pipelines, human‐in‐the‐loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in‐depth research in visual analytics. We present a visual‐interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre‐processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty‐aware pre‐processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre‐processing in general and for uncertainty‐aware pre‐processing of multivariate time series in particular.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

See 1 more Smart Citation

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Bernard

Hutter

Reinemuth

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Overplotting was not a major issue because PETMiner allowed users to toggle the images on and off, present image-based and conventional plots in adjacent columns, and adjust individual images. Image-based data points help users to combine subjective and objective information during analysis (this was one of six output requirements in [39]), and were particularly effective on a 4k display (see Challenge 5 in [39]). As our evaluation showed, for U1 image data points allowed the user to generate substantially more accurate reservoir flow models, which would improve companies' ability to make predictions about new resource deposits.…”

Section: Resultsmentioning

confidence: 99%

PETMiner—A Visual Analysis Tool for Petrophysical Properties of Core Sample Data

Harrison

Efford

Fisher

et al. 2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Abstract-The aim of the PETMiner software is to reduce the time and monetary cost of analysing petrophysical data that is obtained from reservoir sample cores. Analysis of these data requires tacit knowledge to fill 'gaps' so that predictions can be made for incomplete data. Through discussions with 30 industry and academic specialists, we identified three analysis use cases that exemplified the limitations of current petrophysics analysis tools. We used those use cases to develop nine core requirements for PETMiner, which is innovative because of its ability to display detailed images of the samples as data points, directly plot multiple sample properties and derived measures for comparison, and substantially reduce interaction cost. An 11-month evaluation demonstrated benefits across all three use cases by allowing a consultant to: (1) generate more accurate reservoir flow models, (2) discover a previously unknown relationship between one easy-to-measure property and another that is costly, and (3) make a 100-fold reduction in the time required to produce plots for a report.

show abstract

“…Scholtz et al further developed them into an evaluation methodology [SPWG13]. von Landesberger et al considered a categorization of system requirements for data processing in visualization [vFR17]. Lam et al analyzed the relationships between goals and tasks in 20 visualization design studies [LTM18].…”

Section: Design and Evaluation Methods For Visualization And Vamentioning

confidence: 99%

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Chen

Ebert

2019

Computer Graphics Forum

View full text Add to dashboard Cite

Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the cost‐benefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect “symptoms”, “causes”, “remedies”, and “side‐effects”, and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time‐efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial‐and‐error in the lifecycle of VA system development.

show abstract

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Cited by 13 publications

References 76 publications

Visual‐Interactive Preprocessing of Multivariate Time Series Data

Visual‐Interactive Preprocessing of Multivariate Time Series Data

PETMiner—A Visual Analysis Tool for Petrophysical Properties of Core Sample Data

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

Contact Info

Product

Resources

About