Ghulam Jilani Quadri, Anwesh Tuladhar, S. Malla, P. Rosen
{"title":"Visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation","authors":"Ghulam Jilani Quadri, Anwesh Tuladhar, S. Malla, P. Rosen","doi":"10.1109/VAST.2017.8585678","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585678","url":null,"abstract":"Analysis and exploration of similar continuous data for various air-sampler sensor have been performed by developing a processing tool. The analysis and design for characterizing sensor data have been stated and described. Continuous 24X7 sensor data and metrological data leads the layout choices for the analytical design. Such design choices are helpful to understand the pattern of similar reading for various devices at a time. In this paper, we describe the use of the mentioned design choices for to identify pattern, unusual behavior. We used this tool and design choice to solve VAST 2017 mini challenge 2.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125563290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Splechtna, Silvana Podaras, Michael Beham, D. Gračanin, K. Matkovič
{"title":"MC2 — Spatio-Temporal Provenance Data Aggregation for Visual Analysis","authors":"R. Splechtna, Silvana Podaras, Michael Beham, D. Gračanin, K. Matkovič","doi":"10.1109/VAST.2017.8585615","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585615","url":null,"abstract":"We describe our approach to the analysis of 2017 VAST Challenge Mini-Challenge 2 data. The challenge deals with readings from air sampler stations. To answer the main question, the provenance of the chemicals measured at the sampler stations, we extend the provided data set by aggregated spatio-temporal provenance data. This data is generated from the provided meteorological data and locations map by using it as input for a particle tracer which calculates the provenance of the particles arriving from the emitters (factories) at the collectors (the locations of sampler stations). We use ComVis [3], a coordinated multiple views (CMV) system, to analyze the whole data set (the provided and generated data) by applying a sensor centric data model.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127907795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual Causality Analysis Made Practical","authors":"Jun Wang, K. Mueller","doi":"10.1109/VAST.2017.8585647","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585647","url":null,"abstract":"Deriving the exact casual model that governs the relations between variables in a multidimensional dataset is difficult in practice. It is because causal inference algorithms by themselves typically cannot encode an adequate amount of domain knowledge to break all ties. Visual analytic approaches are considered a feasible alternative to fully automated methods. However, their application in real-world scenarios can be tedious. This paper focuses on these practical aspects of visual causality analysis. The most imperative of these aspects is posed by Simpson’ Paradox. It implies the existence of multiple causal models differing in both structure and parameter depending on how the data is subdivided. We propose a comprehensive interface that engages human experts in identifying these subdivisions and allowing them to establish the corresponding causal models via a rich set of interactive facilities. Other features of our interface include: (1) a new causal network visualization that emphasizes the flow of causal dependencies, (2) a model scoring mechanism with visual hints for interactive model refinement, and (3) flexible approaches for handling heterogeneous data. Various real-world data examples are given.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114741082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael Beham, Silvana Podaras, R. Splechtna, D. Gračanin, K. Matkovič
{"title":"MC1 --- Iterative Analysis of Spatio-temporal Data by Textual Queries and Visualizations","authors":"Michael Beham, Silvana Podaras, R. Splechtna, D. Gračanin, K. Matkovič","doi":"10.1109/VAST.2017.8585513","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585513","url":null,"abstract":"Visualizing monitored traffic over a long period of time is a difficult problem. The trajectories of many traffic participants have to be taken into account to find regular patterns and unusual behavior. We introduce a novel system for visual analysis of spatio-temporal tracking data. This system, developed as response to VAST 2017 Mini-Challenge 1, enables iterative analysis steps by combining textual queries and linking and brushing interactive visualizations in ComVis tool.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124079268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual Analysis to Explore Mystery at Wildlife Preserve","authors":"Bo Sun, Rumeel Jessamy, S. Ha, W. Xu","doi":"10.1109/VAST.2017.8585648","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585648","url":null,"abstract":"We conducted visual analytics to find out possible reasons of the decreasing of Rose-Crested Blue Pipit, a popular local bird in a wildlife Preserve at Midford. Given two large scale and multidimensional datasets on chemical release and meteorological information, we utilized Tableau visualization toolset to reveal the patterns of monitor observation and chemical release. Additionally, we developed a prediction method to connect the wind direction with the chemical release, which eventually suggests the release origins from surrounding manufactories.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125608367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chufan Lai, Qiangqiang Liu, Lu Feng, Chenglei Yue, Xi Chen, Yang Hu, Zhanyi Wang, Pengju Teng, Xiaoru Yuan
{"title":"Interactive and Collaborative Visual Analysis on Traffic Sensor Data","authors":"Chufan Lai, Qiangqiang Liu, Lu Feng, Chenglei Yue, Xi Chen, Yang Hu, Zhanyi Wang, Pengju Teng, Xiaoru Yuan","doi":"10.1109/VAST.2017.8585432","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585432","url":null,"abstract":"In VAST Challenge 2017, we propose an interactive and collaborative visual analytic system for the analysis of traffic sensor data. Our system fully incorporates the power of spatial-temporal visualization, sequence mining techniques and collaborative analysis. It allows users to conduct multi-facet and interactive data analysis in a highly efficient way. We discuss technical details in this report, and demonstrate the effectiveness of our system via convincing cases.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122645394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anwesh Tuladhar, S. Malla, Ghulam Jilani Quadri, P. Rosen
{"title":"Data Aggregation and Visualization Technique for Traffic Sensor Data","authors":"Anwesh Tuladhar, S. Malla, Ghulam Jilani Quadri, P. Rosen","doi":"10.1109/VAST.2017.8585568","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585568","url":null,"abstract":"A wealth of information is captured by traffic sensors but extracting and representing the said information is a challenge. We developed a data processing tool in Apache Spark to aggregate the data points recorded by the sensors and enrich it with geographical information as well. We also developed a tool in Processing to aid the visual analysis of this data set. It plots the paths identified in the transformed data as a subway map, while still preserving the relative locations of each sensor. The transformed data is also suitable for further analysis using existing tools such as Tableau. We use all three of these tools in conjunction to solve the VAST challenge 2017 - mini challenge 1.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131012505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SIZE: Satellite Image Zooming and Exploration","authors":"U. Schlegel, A. Diehl, D. Keim","doi":"10.1109/VAST.2017.8585639","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585639","url":null,"abstract":"The VAST Challenge 2017 presents the case of the ornithologist Mitch, who wants to understand the vanishing of the Rose-crested Blue Pipit in the Boonsong Lekagul Nature preserve. To help the ornithologist to answer the challenge questions, we propose the Satellite Image Zooming and Exploration (SIZE) application that implements a framework to interactively zoom and explore satellite images of the preserve area provided by the VAST Mini Challenge 3. SIZE enables the experts to generate hypothesis through zooming and exploration, which he can further verify using different satellite images and Tableau. Two major hypothesis to help Mitch solving the poor development of the bird species are developed. One being the destruction of the habitat of the birds and the other being a heavy pollution of a lake in the preserve area.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132817160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shu Zhang, Danhuai Guo, Ying-tieng Zhu, Deqiang Wang
{"title":"VAST Challenge 2017: Mini-challenge 1","authors":"Shu Zhang, Danhuai Guo, Ying-tieng Zhu, Deqiang Wang","doi":"10.1109/VAST.2017.8585461","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585461","url":null,"abstract":"In this paper, we addressed the mini-challenge I. To efficiently identify odd behaviors and general patterns, visual analytics was used to parse trajectory data of vehicles and support spatial analyses. Firstly, the adjacent relations among spatial objects were extracted and then simplified as a topological graph. Base on the topological representation, cluster methods and spatio-temporal visualization were utilized to conduct a comprehensive analysis. Through visual analytics on the topological graph, we found general and regular behavior patterns of passages in the preserve and recognize outliers that reflected odd behaviors.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114180028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual Analytic Design for Detecting Airborne Pollution Sources VAST Challenge 2017 Award: Comprehensive Mini-Challenge 2 Answer","authors":"J. Wood","doi":"10.1109/VAST.2017.8585588","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585588","url":null,"abstract":"Using the VAST Challenge 2017 dataset as illustration, the design choices of a visual analytic system for predicting the source of air pollution is described. Probabilistic Source Cones are visual symbols representing the probability of source direction of a pollution event. Using transparency to indicate probability, multiple cones may be overlaid in order to provide a fuzzy triangulation of likely sources. This enabled the correct prediction and elimination of pollution sources at a precision far in excess of the spatial density of the sensors themselves.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123199929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}