Visual analytics of cyber physical data streams using spatio-temporal radial pixel visualization

2013 ◽  
Author(s):  
M. Hao ◽  
M. Marwah ◽  
S. Mittelstaedt ◽  
H. Janetzko ◽  
D. Keim ◽  
...  
Keyword(s):  
Data Streams ◽  
Visual Analytics ◽  
Physical Data ◽  
Spatio Temporal

scholarly journals A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams

2018 ◽  
Vol 7 (12) ◽  
pp. 475 ◽  
Author(s):  
Bolelang H Sibolla ◽  
Serena Coetzee ◽  
Terence L Van Zyl
Keyword(s):  
Data Streams ◽  
High Frequency ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
User Model ◽  
Streaming Data ◽  
Design Model ◽  
Observation Data ◽  
Spatio Temporal ◽  
Frequency Changes

Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes.

Spatial modeling of air pollution based on data streams from geosensor networks

2018 ◽  
Vol 930 (12) ◽  
pp. 39-43 ◽  
Author(s):  
V.P. Savinikh ◽  
A.A. Maiorov ◽  
A.V. Materuhin
Keyword(s):  
Air Pollution ◽  
Data Streams ◽  
Spatial Modeling ◽  
Spatial Model ◽  
Large City ◽  
Field Tests ◽  
Processing Unit ◽  
Geosensor Network ◽  
Spatio Temporal ◽  
Different Sources

The article is a brief summary of current research results of the authors in the field of spatial modeling of air pollution based on spatio-temporal data streams from geosensor networks. The urban environment is characterized by the presence of a large number of different sources of emissions and rapidly proceeding processes of contamination spread. So for the development of an adequate spatial model is required to make measurements with a large spatial and temporal resolution. It is shown that geosensor network provide researchers with the opportunity to obtain data with the necessary spatio-temporal detail. The article describes a prototype of a geosensor network to build a detailed spatial model of air pollution in a large city. To create a geosensor in the prototype of the system, calibrated gas sensors for a nitrogen dioxide and carbon monoxide concentrations measurement were interfaced to the module, which consist of processing unit and communication unit. At present, the authors of the article conduct field tests of the prototype developed.

scholarly journals E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

2016 ◽  
Author(s):  
Maia A. Smith ◽  
Cydney Nielsen ◽  
Fong Chun Chan ◽  
Andrew McPherson ◽  
Andrew Roth ◽  
...  
Keyword(s):  
Single Cell ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Treatment Resistance ◽  
Cancer Evolution ◽  
Imaging Data ◽  
Web Based ◽  
Clinical Endpoints ◽  
Cell Experiment ◽  
Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

scholarly journals Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

2020 ◽  
Author(s):  
Carrie Manore ◽  
Geoffrey Fairchild ◽  
Amanda Ziemann ◽  
Nidhi Parikh ◽  
Katherine Kempfert ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Data Streams ◽  
Cumulative Risk ◽  
State Level ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Global Dynamics ◽  
Flexible System ◽  
Clinical Surveillance ◽  
Spatio Temporal

ABSTRACTPredicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil’s 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models. One size does not fit all, particularly when considering state-level predictions as opposed to the whole country. Our first-of-its-kind high resolution flexible system for predicting dengue incidence with heterogeneous (and still sometimes sparse) data can be extended to multiple applications and regions.

Building a Visual Analytics Tool for Location-Based Services

2016 ◽  
pp. 620-642 ◽  
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Map-based Visual Analytics of Moving Learners

2016 ◽  
Vol 8 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Christian Sailer ◽  
Peter Kiefer ◽  
Joram Schito ◽  
Martin Raubal
Keyword(s):  
Mobile Learning ◽  
Visual Analytics ◽  
Temporal Patterns ◽  
Behavior Patterns ◽  
Usability Engineering ◽  
Learning Content ◽  
Potential Problems ◽  
Spatio Temporal

Location-based mobile learning (LBML) is a type of mobile learning in which the learning content is related to the location of the learner. The evaluation of LBML concepts and technologies is typically performed using methods known from classical usability engineering, such as questionnaires or interviews. In this paper, the authors argue for applying visual analytics to spatial and spatio-temporal visualizations of learners' trajectories for evaluating LBML. Visual analytics supports the detection and interpretation of spatio-temporal patterns and irregularities in both, single learners' as well as multiple learners' trajectories, thus revealing learners' typical behavior patterns and potential problems with the LBML software, hardware, the didactical concept, or the spatial and temporal embedding of the content.

Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework

2012 ◽  
Vol 4 (3) ◽  
pp. 63-84
Author(s):  
Jonathan Cazalas ◽  
Ratan K. Guha
Keyword(s):  
Parallel Processing ◽  
Data Streams ◽  
Graphics Processing Unit ◽  
Performance Model ◽  
Processing Unit ◽  
Data Streaming ◽  
Temporal Data ◽  
Processing Power ◽  
Spatio Temporal ◽  
Graphics Processing

The efficient processing of spatio-temporal data streams is an area of intense research. However, all methods rely on an unsuitable processor (Govindaraju, 2004), namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents a performance model of the execution of spatio-temporal queries over the authors’ GEDS framework (Cazalas & Guha, 2010). GEDS is a scalable, Graphics Processing Unit (GPU)-based framework, employing computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal queries over spatio temporal data streams. Experimental evaluation shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments and demonstrates that, despite the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. To move beyond the analysis of specific algorithms over the GEDS framework, the authors developed an abstract performance model, detailing the relationship of the CPU and the GPU. From this model, they are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based applications.

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