The Experts below are selected from a list of 287775 Experts worldwide ranked by ideXlab platform
Luciana Nedel - One of the best experts on this subject based on the ideXlab platform.
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evaluating an immersive space time cube geovisualization for intuitive trajectory Data Exploration
IEEE Transactions on Visualization and Computer Graphics, 2020Co-Authors: Jorge Wagner A Filho, Wolfgang Stuerzlinger, Luciana NedelAbstract:A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory Datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate Exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.
Niklas Elmqvist - One of the best experts on this subject based on the ideXlab platform.
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Datasite proactive visual Data Exploration with computation of insight based recommendations
Information Visualization, 2019Co-Authors: Sriram Karthik Badam, Adil M Yalcin, Niklas ElmqvistAbstract:Effective Data analysis ideally requires the analyst to have high expertise as well as high knowledge of the Data. Even with such familiarity, manually pursuing all potential hypotheses and explori...
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Datasite proactive visual Data Exploration with computation of insight based recommendations
arXiv: Human-Computer Interaction, 2018Co-Authors: Sriram Karthik Badam, Adil M Yalcin, Niklas ElmqvistAbstract:Effective Data analysis ideally requires the analyst to have high expertise as well as high knowledge of the Data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed view, akin to posts in a social media feed. DataSite effectively turns Data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement particularly for complex analyses that existing analytics systems do not support well.
Jorge Wagner A Filho - One of the best experts on this subject based on the ideXlab platform.
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evaluating an immersive space time cube geovisualization for intuitive trajectory Data Exploration
IEEE Transactions on Visualization and Computer Graphics, 2020Co-Authors: Jorge Wagner A Filho, Wolfgang Stuerzlinger, Luciana NedelAbstract:A Space-Time Cube enables analysts to clearly observe spatio-temporal features in movement trajectory Datasets in geovisualization. However, its general usability is impacted by a lack of depth cues, a reported steep learning curve, and the requirement for efficient 3D navigation. In this work, we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a review of previous work and selecting an appropriate Exploration metaphor, we built a prototype environment where the cube is coupled to a virtual representation of the analyst's real desk, and zooming and panning in space and time are intuitively controlled using mid-air gestures. We compared our immersive environment to a desktop-based implementation in a user study with 20 participants across 7 tasks of varying difficulty, which targeted different user interface features. To investigate how performance is affected in the presence of clutter, we explored two scenarios with different numbers of trajectories. While the quantitative performance was similar for the majority of tasks, large differences appear when we analyze the patterns of interaction and consider subjective metrics. The immersive version of the Space-Time Cube received higher usability scores, much higher user preference, and was rated to have a lower mental workload, without causing participants discomfort in 25-minute-long VR sessions.
Anne E Carpenter - One of the best experts on this subject based on the ideXlab platform.
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cellprofiler analyst interactive Data Exploration analysis and classification of large biological image sets
Bioinformatics, 2016Co-Authors: David Dao, Adam Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh, Anne E CarpenterAbstract:Summary: CellProfiler Analyst allows the Exploration and visualization of image-based Data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and Data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. Contact: gro.etutitsnidaorb@enna Supplementary information: Supplementary Data are available at Bioinformatics online.
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cellprofiler analyst interactive Data Exploration analysis and classification of large biological image sets
bioRxiv, 2016Co-Authors: David Dao, Adam Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh, Anne E CarpenterAbstract:Summary: CellProfiler Analyst allows the Exploration and visualization of image-based Data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and Data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (in Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process which supports nightly updates and regular release cycles for the software.
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cellprofiler analyst Data Exploration and analysis software for complex image based screens
BMC Bioinformatics, 2008Co-Authors: Thouis R Jones, In Han Kang, Douglas B Wheeler, Robert A Lindquist, Adam Papallo, David M Sabatini, Polina Golland, Anne E CarpenterAbstract:Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment. Here, we describe CellProfiler Analyst, open-source software for the interactive Exploration and analysis of multidimensional Data, particularly Data from high-throughput, image-based experiments. The system enables interactive Data Exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.
Aditya Parameswaran - One of the best experts on this subject based on the ideXlab platform.
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effortless Data Exploration with zenvisage an expressive and interactive visual analytics system
Very Large Data Bases, 2016Co-Authors: Tarique Siddiqui, Albert Kim, John D Lee, Karrie Karahalios, Aditya ParameswaranAbstract:Data visualization is by far the most commonly used mechanism to explore and extract insights from Datasets, especially by novice Data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations---by composing, filtering, comparing, and sorting them---to find those that depict desired trends or patterns. The process of visual Data Exploration remains a tedious process of trial-and-error. We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large Datasets. We introduce zenvisage's general purpose visual Exploration language, ZQL ("zee-quel") for specifying the desired visual patterns, drawing from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual Exploration algebra---an algebra on collections of visualizations---and demonstrate that ZQL is as expressive as that algebra. zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are "short-cuts" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that Data scientists are able to effectively use zenvisage to eliminate error-prone and tedious Exploration and directly identify desired visualizations.
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Interactive Data Exploration with smart drill-down
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Manas Joglekar, Hector Garcia-molina, Aditya ParameswaranAbstract:We present smart drill-down, an operator for interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each group of tuples is described by a rule. For instance, the rule (a, b, *, 1000) tells us that there are a thousand tuples with value a in the first column and b in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are NP-HARD, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real Datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.
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effortless Data Exploration with zenvisage an expressive and interactive visual analytics system
arXiv: Databases, 2016Co-Authors: Tarique Siddiqui, Albert Kim, John D Lee, Karrie Karahalios, Aditya ParameswaranAbstract:Data visualization is by far the most commonly used mechanism to explore Data, especially by novice Data analysts and Data scientists. And yet, current visual analytics tools are rather limited in their ability to guide Data scientists to interesting or desired visualizations: the process of visual Data Exploration remains cumbersome and time-consuming. We propose zenvisage, a platform for effortlessly visualizing interesting patterns, trends, or insights from large Datasets. We describe zenvisage's general purpose visual query language, ZQL ("zee-quel") for specifying the desired visual trend, pattern, or insight - ZQL draws from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual Exploration algebra, and demonstrate that ZQL is at least as expressive as that algebra. While analysts are free to use ZQL directly, we also expose ZQL via a visual specification interface that we describe in this paper. We then describe our architecture and optimizations, preliminary experiments in supporting and optimizing for ZQL queries in our initial zenvisage prototype, and a user study to evaluate whether Data scientists are able to effectively use zenvisage for real applications.
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interactive Data Exploration with smart drill down
arXiv: Databases, 2014Co-Authors: Manas Joglekar, Hector Garciamolina, Aditya ParameswaranAbstract:We present {\em smart drill-down}, an operator for interactively exploring a relational table to discover and summarize "interesting" groups of tuples. Each group of tuples is described by a {\em rule}. For instance, the rule $(a, b, \star, 1000)$ tells us that there are a thousand tuples with value $a$ in the first column and $b$ in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are {\sc NP-Hard}, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real Datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.
